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The Graduate School
2014
Lexical Processing in Sentence Context:
Semantic and Syntactic Factors
Eileen Fancher
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FLORIDA STATE UNIVERSITY
COLLEGE OF ARTS AND SCIENCES
LEXICAL PROCESSING IN SENTENCE CONTEXT: SEMANTIC AND
SYNTACTIC FACTORS
By
EILEEN FANCHER
A Dissertation submitted to the
Department of Modern Languages and Linguistics
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Degree Awarded:
Summer Semester, 2014
Copyright © 2014
Eileen Fancher
All Rights Reserved
Eileen Fancher defended this dissertation on April 30, 2014.
The members of the supervisory committee were:
Gretchen Sunderman
Professor Directing Dissertation
Michael Kaschak
University Representative
Michael Leeser
Committee Member
Lara Reglero
Committee Member
The Graduate School has verified and approved the above-named committee members, and
certifies that the dissertation has been approved in accordance with university requirements.
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ACKNOWLEDGMENTS
First I would like to acknowledge the Language Learning dissertation grant for helping
fund this project. I would also like to acknowledge the Ada-Belle Winthrop King Foundation
Summer Research grant, as well as the Florida State University dissertation grant, which funded
this research.
I am extremely grateful for all the support I have received throughout my graduate work.
I would like to thank my advisor and friend, Gretchen Sunderman, for her constant
encouragement. Each week after our meeting, I would walk out of her office inspired to tackle
the next challenge. Gretchen’s insights pushed me to think about my own work in new ways,
which in turn helped me develop as a researcher. Thank you, Gretchen, for your dedication and
support as I navigated through my graduate studies and prepared for the next steps in my
professional career.
I also thank Michael Kaschak, Michael Leeser, and Lara Reglero for serving on my
committee and providing helpful comments and feedback throughout my project. In particular, I
want to thank Dr. Reglero for giving me valuable advice as I created the materials for this
project.
I appreciate the support from my research assistants, as well. I thank Taylor Konigseder
and Sam Schlesinger for their help in writing numerous sentences and for their patience in
working through the many challenges of material development. I also thank Jendayi Dillard for
her assistance with piloting the experiments and data collection.
I would like to thank my colleagues in the Department of Modern Languages and
Linguistics. Specifically, I would like to thank Tina for her friendship through each stage of the
dissertation process. From the start, she always provided a listening ear and gave me advice
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when I needed it most. I also thank Patricia, Savannah, Daphne, Amy, and Kristin for
brainstorming ideas with me, encouraging me, and sharing their own perspectives regarding my
research.
Finally, I feel so grateful to have the support of my family. My parents, Jim and Mary,
instilled in me the belief that, with hard work and dedication, I could complete anything I set my
mind to. Thank you for encouraging me to “study hard and learn lots.” My sisters, Jennifer and
Amy, always cheered me on. Knowing I could always count on them whenever I needed to vent
meant so much to me. And lastly, I thank my husband, Aaron. I feel so lucky to have his
unconditional love and devotion. Thank you for always making me laugh, especially when
practicing for upcoming presentations. You mean the world to me.
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TABLE OF CONTENTS
List of Tables ............................................................................................................................... viii
List of Figures ................................................................................................................................ xi
Abstract ......................................................................................................................................... xii
INTRODUCTION ...........................................................................................................................1
Specific Aims .............................................................................................................................1
Background ................................................................................................................................3
Monolingual Lexical Processing in Isolated Context ..........................................................3
Bilingual Lexical Processing in Isolated Context ................................................................5
Monolingual Lexical Processing in Sentence Context ........................................................8
Bilingual Lexical Processing in Sentence Context ............................................................12
Word Recognition and L2 Development...................................................................................21
SENTENCE CONTEXT AND LEXICAL PROCESSING ..........................................................24
Introduction ..............................................................................................................................24
The Interactive Activation Model ............................................................................................24
Claims of the Interactive Activation Model.......................................................................25
Previous Research on the Interactive Activation Model....................................................26
The Bilingual Interactive Activation Plus Model ....................................................................32
Claims of the BIA+ Model ................................................................................................34
Previous Research on the BIA+ Model .............................................................................34
Similarities and Differences Between the Two Models ............................................................39
Areas of Future Research for the BIA+ Model .........................................................................40
Sentence Constraints on Lexical Processing .............................................................................42
A Model of Sentence Processing: Feature Restrictions Hypothesis .........................................46
Motivation for the Current Study ..............................................................................................46
EXPERIMENTAL DESIGN: GENERAL CONSIDERATIONS .................................................48
Overview of Experimental Approach ......................................................................................48
General Method .......................................................................................................................49
Participants .........................................................................................................................49
Proficiency Groups ............................................................................................................53
Materials ............................................................................................................................60
EXPERIMENT 1: COGNATE TARGETS AND SENTENCE CONSTRAINT – LEXICAL
DECISION TASK .........................................................................................................................62
Introduction ...............................................................................................................................62
Method.......................................................................................................................................62
Participants .........................................................................................................................62
Stimuli ................................................................................................................................63
Semantic Constraint ..................................................................................................63
Syntactic Constraint ..................................................................................................65
Norming the Stimuli .................................................................................................67
Design ................................................................................................................................69
Procedure ...........................................................................................................................70
v
Results .......................................................................................................................................71
Data Analysis .....................................................................................................................71
Native Spanish Speakers Results .......................................................................................72
Intermediate L2 Learners Results ......................................................................................74
Advanced L2 Learners Results ..........................................................................................76
Spanish-English Bilinguals Results ...................................................................................77
Overall Results ...................................................................................................................79
Summary ............................................................................................................................82
Materials Control Task Results .........................................................................................83
Discussion .................................................................................................................................88
EXPERIMENT 2: GRAMMATICAL CLASS AND SEMANTIC SIMILARITY IN SENTENCE
CONTEXT – LEXICAL DECISION TASK .................................................................................93
Introduction ...............................................................................................................................93
Method.......................................................................................................................................94
Participants .........................................................................................................................94
Stimuli ................................................................................................................................94
Grammatical Class and Semantic Similarity ............................................................95
Norming the Stimuli .................................................................................................98
Design ..............................................................................................................................101
Procedure .........................................................................................................................102
Results .....................................................................................................................................103
Data Analysis ...................................................................................................................103
Native Spanish Speakers Results .....................................................................................104
Intermediate L2 Learners Results ....................................................................................106
Advanced L2 Learners Results ........................................................................................108
Spanish-English Bilinguals Results .................................................................................109
Overall Results .................................................................................................................111
Summary ..........................................................................................................................113
Discussion ...............................................................................................................................115
CONCLUSIONS..........................................................................................................................119
Summary of Main Findings from Experiment 1 ....................................................................119
Task Demands ..................................................................................................................121
Summary of Main Findings from Experiment 2 ....................................................................123
Implications for Models of Lexical Processing and Models of Sentence Processing ...........126
Implications for L2 Pedagogy................................................................................................130
Conclusion and Directions for Future Research ....................................................................131
APPENDICES .............................................................................................................................134
A.
Translation Recognition Task Materials .............................................................................134
B.
Language History Questionnaire (English) .........................................................................137
C.
Language History Questionnaire (Spanish) .........................................................................142
D.
Materials for Offline Proficiency Measure..........................................................................146
E.
Experiment 1 Norming Questionnaire.................................................................................149
vi
F.
Experiment 1 Lexical Decision Task Materials ..................................................................157
G.
Experiment 2 Norming Questionnaires ...............................................................................164
H.
Experiment 2 Lexical Decision Task Materials ..................................................................178
I.
IRB Approval Letters ..........................................................................................................188
J.
Informed Consent Form ......................................................................................................192
REFERENCES ............................................................................................................................194
BIOGRAPHICAL SKETCH .......................................................................................................201
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LIST OF TABLES
Table 3.1: Mean self-ratings on reading, writing, speaking, listening, and comfort in English.
Each scale rated from 1 (low ability) to 10 (high ability). ......................................................... 51
Table 3.2: Mean self-ratings on reading, writing, speaking, listening and comfort in Spanish.
Each scale rated from 1 (low ability) to 10 (high ability). ......................................................... 51
Table 3.3: Mean reaction times (ms) and percent accuracy for correct translations in Translation
Recognition Task. ...................................................................................................................... 52
Table 3.4: Overall mean score (out of 25) and percent accuracy for offline multiple choice
proficiency measures. ................................................................................................................ 52
Table 3.5: Number of participants in each proficiency level. .................................................... 53
Table 3.6: Average age (in years) of participants by proficiency level. .................................... 54
Table 3.7: Mean self-ratings on reading, writing, speaking, listening, and comfort of expression
in Spanish. Scale was from 1 (low ability) to 10 (high ability). ................................................ 54
Table 3.8: Mean self-ratings on reading, writing, speaking, listening, and comfort of expression
in English. Scale was from 1 (low ability) to 10 (high ability).................................................. 56
Table 3.9: Overall mean ratings for Spanish and English by proficiency group. ...................... 58
Table 3.10: Mean reaction times (ms) and percent accuracy for correct translations by
proficiency group. ...................................................................................................................... 59
Table 3.11: Mean scores and percent accuracy for offline multiple choice task (out of
25). ............................................................................................................................................ 60
Table 4.1: Sentence conditions based on semantic constraint. .................................................. 64
Table 4.2: Sentence conditions with semantic and syntactic constraint. ................................... 66
Table 4.3: Mean word length (number of letters) and mean word frequency per million in
Spanish (Davis & Perea, 2005) for targets and matched controls. ............................................ 68
Table 4.4: Mean similarity rating (on scale of 1-7) between targets and elements from sentence
contexts where 1 indicates “no similarity in meaning” and 7 indicates “very similar in
meaning.” ................................................................................................................................... 69
Table 4.5: Illustration of design of materials for Experiment 1. ................................................ 70
viii
Table 4.6: Mean RTs (ms) and percent accuracy for Experiment 1 for native Spanish speaking
participants. ................................................................................................................................ 72
Table 4.7: Mean RTs (ms) and percent accuracy for cognates and matched controls for native
Spanish speaking participants. ................................................................................................... 73
Table 4.8: Mean RTs (ms) and percent accuracy for Experiment 1for intermediate L2
learners. ...................................................................................................................................... 74
Table 4.9: Mean RTs (ms) and percent accuracy for cognates and matched controls for
intermediate L2 learners…………... ......................................................................................... 74
Table 4.10: Mean RTs (ms) and percent accuracy for Experiment 1 for advanced L2
learners. ...................................................................................................................................... 76
Table 4.11: Mean RTs (ms) and percent accuracy for cognates and matched controls for
advanced L2 learners ................................................................................................................. 76
Table 4.12: Mean RTs (ms) and percent accuracy for Experiment 1 for Spanish-English
bilinguals. ................................................................................................................................... 78
Table 4.13: Mean RTs (ms) and percent accuracy for cognates and matched controls for SpanishEnglish bilinguals....................................................................................................................... 78
Table 5.1: Number of participants in each proficiency level for Experiment 2. ....................... 94
Table 5.2: Sentence with four different target conditions from Experiment 2….. .................... 95
Table 5.3: High and low semantic constraint sentences with targets ........................................ 97
Table 5.4: Mean word length (number of letters) and mean word frequency per million in
Spanish (Davis & Perea, 2005) for targets................................................................................. 99
Table 5.5: Mean semantic similarity rating (on scale of 1-7) between last word of the sentence
and critical targets where 1 indicates “not similar at all in meaning” and 7 indicates “very similar
in meaning.” ............................................................................................................................... 100
Table 5.6: Mean rating (on scale of 1-7) for how well sentence predicts the last word in the
sentence where 1 indicates “sentence does not predict last word” and 7 indicates “sentence
strongly predicts last word.” ...................................................................................................... 101
Table 5.7: Illustration of design of materials for Experiment 2................................................. 102
Table 5.8: Percent accuracy for all participant groups for sentence comprehension
questions .................................................................................................................................... 104
ix
Table 5.9: Mean RTs (ms) and percent accuracy for Experiment 2 for native Spanish
speakers……. ............................................................................................................................. 106
Table 5.10: Mean RTs (ms) and percent accuracy for Experiment 2 for intermediate L2
learners……. .............................................................................................................................. 107
Table 5.11: Mean RTs (ms) and percent accuracy for Experiment 2 for advanced L2
learners……. .............................................................................................................................. 108
Table 5.12: Mean RTs (ms) and percent accuracy for Experiment 2 for Spanish-English
bilinguals……. ........................................................................................................................... 110
x
LIST OF FIGURES
Figure 2.1: The BIA+ Model (adapted from Dijkstra & Van Heuven, 2002) ........................... 33
Figure 6.1: Revisiting the BIA+ Model (adapted from Dijkstra & Van Heuven, 2002) ........... 127
xi
ABSTRACT
This current study investigated bilingual and second language (L2) lexical processing in
sentence context. There is overwhelming evidence from bilingual word recognition studies to
support the notion that both languages are active during lexical processing (e.g., Van Heuven,
Dijkstra, & Grainger, 1998; Dijkstra, Timmermans, & Schriefers, 2000, etc.). While the ‘default’
setting for lexical access may be non-selective in nature, there are instances in which lexical
access is more selective. Past research found that bilinguals process more selectively when
sentences are constrained for semantics. Other studies found that individuals use grammatical
class information (i.e., noun or verb status) to guide lexical access.
While it appears the semantic context of a sentence or word class effects may constrain
selectivity, an often-overlooked dimension that could potentially affect selectivity is syntax. The
current study examined how the cues of semantics and syntax interact and jointly affect lexical
processing. In the first experiment, I investigated whether bilinguals and L2 learners processed
words selectively or non-selectively in sentences constrained for semantics and syntax. The
results showed cross-linguistic effects for intermediate L2 learners only. In the second
experiment, I examined whether bilinguals and L2 learners showed sensitivity to grammatical
class in sentences constrained for semantics. The findings showed that verbs were processed
differently than nouns regardless of sentence context. The results from these experiments give
implications for models of bilingual word processing, such as the Bilingual Interactive
Activation+ (BIA+) model (Dijkstra & Van Heuven, 2002).
xii
CHAPTER ONE
INTRODUCTION
Specific Aims
In a world that is becoming more and more connected, the reality is that most of the
world’s population is bilingual. However, in the field of psycholinguistics, bilinguals were not
always recognized as being the “norm” or “standard.” Up until two decades ago,
psycholinguists mainly focused on the language processing of monolinguals (Kroll & De Groot,
2005). Bilinguals were thought of as a special group, as if bilingualism were its own unique
condition (Grosjean, 1989). Yet, psycholinguists have begun to realize the benefits of studying
bilinguals. The bilingual mind, which constantly juggles two languages, can provide more
insights into the intricacies of how language processing occurs. Language processing is quite
complex, as readers are sensitive to even the smallest features of written scripts when presented
with a word to read. Examining word processing has been key to understanding how lexical
items are stored in the mind and how readers access these lexical representations. The findings
from studies on bilingual word recognition can help answer questions regarding the nature of
the bilingual lexicon: are the two languages in the bilingual lexicon separate or integrated? Do
bilinguals access words selectively by language or non-selectively? Answers to these questions
can help psycholinguists better understand how language processing occurs for bilinguals.
This dissertation seeks to examine bilingual word recognition within the context of a
sentence. How does sentence context hinder or facilitate the activation of words? Can sentence
context help essentially to “shut off” the language not in use by the bilingual? This dissertation
consists of two experiments that test the parallel activation of a second language learner’s (L2)
and bilingual’s two languages while reading within a sentence context. Specifically, it
1
investigates how sentence context may modulate non-selectivity at different levels of L2
proficiency. In other words, do L2 learners and bilinguals activate both languages when reading
sentences? Can L2 sentential cues (e.g., semantic or syntactic) help L2 learners and bilinguals
shut off the native language? Previous studies have shown evidence for bilingual non-selectivity
in out-of-context word recognition (i.e., isolated word processing) and in sentence context.
However, little research has been done to examine how different sentence context constraints
yield selectivity. While most sentence context research has examined sentences constrained for
semantics (e.g., Schwartz & Kroll, 2006; Van Hell & De Groot, 2008), only one preliminary
study has investigated how sentences constrained for syntax may affect non-selectivity
(Gullifer, Dussias, & Kroll, 2010). The goal of this dissertation is to investigate how semantic
and syntactic sentential-level factors affect L2 and bilingual word processing. Two main
research questions are as follows: Can we disentangle the relative contributions of semantic and
syntactic information in L2 lexical processing? Also, do L2 learners and bilinguals of varying
proficiency levels use semantic and syntactic information differently during their lexical
processing?
This chapter explores previous research on monolingual and bilingual word recognition,
both in and out of sentence context. This discussion serves to show the robust evidence for nonselectivity and to further motivate the semantic and syntactic factors that should be investigated
to better understand L2 and bilingual lexical processing.
Chapter 2 focuses on models of both lexical and sentence processing, beginning with
monolingual word processing, then discussing bilingual word processing, and finally discussing
constraints on sentence processing comprehension. These models serve as the theoretical
foundation for the dissertation.
2
Chapter 3 provides an overview of the experiments and general method. Chapter 4
details Experiment 1, investigating semantic and syntactic sentence factors and how they yield
selective or non-selective processing. In Chapter 5, Experiment 2 is presented, which
investigates semantic sentential factors along with grammatical class and semantic similarity at
the word level. Then, in Chapter 6, I present the overall results of the dissertation, including
implications for current models of bilingual lexical processing and pedagogy along with
directions for future research.
Background
Monolingual Lexical Processing in Isolated Context
As early as four decades ago, researchers began investigating visual word recognition in
the monolingual domain. They examined what types of lexical properties, such as a word’s
orthography, phonology, semantics, and grammatical class, affected lexical access (see Balota,
Yap, & Cortese, 2006 for a review). By examining the different types of lexical properties,
researchers could better understand the process of word recognition. In order to investigate
visual word recognition, researchers used words that were ambiguous, in their meaning and/or
grammatical class. In this way, researchers could study whether words were being processed
non-selectively (i.e., all of the multiple meanings of the word were being processed at once) or
selectively (i.e., only one particular meaning of an ambiguous word was being processed). For
example, the word “bank” is semantically ambiguous because its meaning can refer to a
financial institution or the slope next to a river. Since the multiple meanings of the word “bank”
share the same phonology and orthography, this word is an example of a homonym. Another
type of ambiguous word is syntactically ambiguous words. Consider the word “lead” which can
be a noun (e.g., the lead in the pencil) or a verb (e.g., you lead the race). Again, there is overlap
3
of orthography, but not phonology, so this word is a homograph. The change in grammatical
class also makes this word syntactically ambiguous.
Studies on semantically ambiguous words have asked whether these types of words are
processed faster or slower than matched controls. Previous studies (Borowsky & Masson, 1996;
Hino & Lupker, 1996; Rodd, Gaskell, & Marslen, 2002) found facilitation for homonyms such
that semantically ambiguous words were processed faster than matched controls. These studies
argued that the facilitation was due to non-selective activation, or the idea that the activation of
both of the word’s meanings strengthened its activation compared to a matched control.
Gottlob, Goldinger, Stone, and Van Order (1999) also found evidence for facilitation in
processing homonyms (e.g., bank). They also tested the processing of homographs (e.g., lead)
and found inhibition for these words in a lexical decision task (LDT). This finding is significant
because it shows that phonology plays a role in lexical access. With two different phonological
representations competing for activation, word processing was slowed.
In order to further analyze the effects of semantics and syntax in lexical processing,
Wong and Chen (2012) compared the processing of semantically ambiguous words (e.g., bank)
and syntactically ambiguous words (e.g., lead). Their study pulled apart syntax and semantic
factors in order to examine how syntactic-category information and semantic information are
used during lexical processing. Wong and Chen found that native Chinese speakers processing
Chinese character words in isolation processed semantically ambiguous words slower compared
to their matched controls. There was only a syntactic-category disadvantage (i.e., inhibition) in
the syntactic category judgment task which forced the readers to process syntactically. From
these results, they concluded that semantic information is crucial in processing words in isolated
context, but the syntactic information was not, at least in Chinese. Since semantic information is
4
crucial to isolated word processing, Wong and Chen proposed the semantic-mediation model.
This model asserts that semantic processing occurs before syntactic processing. However, they
also acknowledged that there may be parallel activation of both semantics and syntax, but
perhaps semantics has a more powerful influence compared to syntax (as opposed to the other
way around).
The results from monolingual lexical processing out of context suggests that multiple
meanings of a given word are activated during visual word processing. In other words, when
presented with ambiguity at the word-level, multiple representations are processed nonselectively. Readers are also sensitive to a word’s orthography, phonology, semantics, and
grammatical class. Preliminary findings suggest that semantics may play a stronger role in word
processing than syntax, but this finding may be limited to a specific language. These
conclusions lead to further questions: how are words processed out of context when there is
more than one language in the mind? Are words processed non-selectively from both
languages? How does a word’s orthography, phonology, semantics, or grammatical class affect
processing for bilinguals? In the next section, I discuss findings from past studies on bilingual
lexical processing in isolated context.
Bilingual Lexical Processing in Isolated Context
The research on bilingual lexical processing in isolated context, like the research done
with monolingual processing in isolated context, has looked at ambiguous words to see if
readers process words simultaneously from both languages. There have been overwhelming
results supporting non-selective processing for bilinguals (Caramazza & Brones, 1979; Dijkstra,
Van Jaarsveld, & Ten Brinke, 1998; Dijkstra, Grainger, & Van Heuven, 1999; Dijkstra,
Timmermans, & Schriefers, 2000; Van Hell & Dijkstra, 2002; Jared & Kroll, 2001, Lemhofer &
5
Dijkstra, 2004; Lemhofer, Dijkstra, & Michel, 2004; Schwartz, Kroll, & Diaz, 2007). Besides
investigating ambiguous words such as homonyms and homographs (as is done in the
monolingual field), researchers in bilingual lexical processing examine the processing of
cognate words. Cognates are words that overlap across languages in orthography, phonology,
and semantics. For example, an example of a cognate in English and Spanish is the word
“piano,” which is piano in Spanish. Bilinguals typically process cognates more quickly and
accurately than matched controls as found using visual word recognition tasks such as
progressive demasking and LDTs (Dijkstra et al., 1999). In other words, when English-Spanish
bilinguals visually encounter the word “piano,” they are faster and more accurate to respond this
cognate target word than a matched non-cognate. This phenomenon has been termed the
cognate facilitation effect. The cognate facilitation effect explains the facilitation by assuming
non-selective access of both languages, which strengthens the activation of the cognate. The
cognate facilitation effect has also been found using other types of tasks: primed or unprimed
LDTs (Caramazza & Brones, 1979; De Groot & Nas, 1991), ERP studies (Midgley, Holcomb,
& Grainger, 2011), and semantic categorization (Sánchez-Casas, Davis, García-Albea, 1992).
Evidence has even been found that bilinguals access the L2 when reading in the L1 (Duyck,
2005; Van Assche, Duyck, Hartsuiker, & Diependaele, 2009; Van Hell & Dijkstra, 2002). This
phenomenon has even been found for languages that share different writing scripts, such as
Hebrew-English (Gollan, Forster, & Frost, 1997), Greek-English (Dimitropoulou, Duñabeitia,
& Carreiras, 2011; Voga & Grainger, 2007), and Japanese-English (Hoshino & Kroll, 2008).
This effect, therefore, gives strong evidence of non-selectively during bilingual word
processing.
6
In order to better understand how the overlap of orthography, phonology, and semantics
affects word processing, Dijkstra, Miwa, Brummelhuis, Sappelli, and Baayen (2010) tested
Dutch-English bilinguals with an English (L2) LDT using identical cognates (e.g., “fruit” is
identical in phonology, orthography, and semantics in Dutch and English) and non-identical
cognates (e.g., the Dutch translation of the English word “melon” is meloen). Dijkstra and
colleagues found that the cognate facilitation effect was larger for identical cognates compared
to non-identical cognates. The processing of non-identical cognates was still faster than
matched non-cognates, but since the non-identical cognates were not completely identical, two
representations were still activated, which slowed down processing compared to the identical
cognates.
Further evidence for non-selective processing was found by Lemhofer, Dijkstra, and
Michel (2004) who tested the processing of Dutch-English-French trilinguals. The trilinguals
had relatively high proficiency in English (L2) and French (L3), but were tested in Dutch (L1).
The trilinguals performed a LDT with three different word conditions: Dutch-English cognates,
Dutch-French cognates, and non-cognate controls. The researchers found a cognate facilitation
effect for the Dutch-English cognates, and for the Dutch-French cognates. Thus, the strong L1
was facilitated by both the L2 and the L3 in lexical processing.
The results from studies mentioned give ample support for non-selective processing in
the bilingual (and multilingual) mind. However, words are not always processed isolated of
context. Next, I will review how sentence context affects lexical processing. I will first begin by
examining monolingual lexical processing in lexical context and then will continue with
bilingual lexical processing in lexical context.
7
Monolingual Lexical Processing in Sentence Context
Since words are not generally processed out of context, it is important to consider how
sentence context affects lexical processing. I will now discuss two different perspectives that
describe how sentence context affects the processing of individual lexical items: contextindependent models and context-dependent models.
First, context-independent models claim that there is an autonomous lexical processor
that recognizes words based solely on lexical properties. The lexical processor does not depend
on resorting to context to access meaning. Another name for this type of model is exhaustive
access models. These models follow the assumptions of modularity proposed by Fodor (1983).
They assume that the multiple meanings of a word are accessed non-selectively regardless of
context. There have been multiple studies that support context-independent models (Onifer &
Swinney, 1981; Seidenberg, Tanenhaus, Leiman, & Bienkowski, 1982; Swinney, 1979, etc.).
The second type of model is the context-dependent model. Unlike the contextindependent models, these models purport that sentence context can help steer a reader to a
particular interpretation of an ambiguous word. In other words, the context shapes word
processing. Another name for these models are selective access models. These models are
consistent with an interactive view of language processing, since the context interacts with
lexical processing as lexical processing occurs. Support for these models has also been found in
many studies (Simpson & Kreuger, 1991; Tabossi, 1988; Tabossi, Colombo & Job, 1987, etc.).
The context-dependent models assert that sentence context has an effect on word
processing. There are various factors that may affect lexical processing. The first factor is the
effect of predictability. This factor purports that how predictable a word is in a given context
may affect the rate it is processed. Words that are predictable based on the context in which they
8
appear are processed more rapidly than words that do not occur in a predictable environment
(Altarriba, Kroll, Shool, & Rayner, 1996; Ehrlich & Rayner, 1981). Altarriba and colleagues
also found that highly predictable words are more likely to be skipped entirely during reading
than words that are in a neutral context.
Based on this evidence, it would seem like readers are able to anticipate the words that
they will be encountering. However, Stanovich and West (1981, 1983) found that words that
were not predictable based on the context had no interference during language processing. In
other words, readers did not process incongruent words more slowly than neutral words. This
finding gives evidence that readers were not anticipating the word, which would have led to
slowed reaction times as readers suppressed the anticipated word. Thus, the anticipatory
mechanism (the mechanism that predicts what the upcoming word in the sentence will be)
cannot account for the effects found. Further studies done by Morris and colleagues (e.g.,
Duffy, Henderson, & Morris, 1989; Morris, 1994; Morris & Folk, 1998) found that words that
are preceded by a semantically constrained sentence are processed faster than words preceded
by an unrelated context. This finding was true even if the target word was not the original word
predicted from the sentence completion task, which was given to assess predictability ratings for
various targets.
If words are not anticipated based on sentence context, another possibility is that there
is intralexical priming between the sentence context and target word. In other words, the
sentence context could create context effects, which increase activation of the target word due to
the spreading activation from related words in the sentence context. Context effects may
facilitate access to the target word (Duffy et al. 1989; Fodor, 1983; Seidenberg et al. 1982) in
the same way that there are semantic-relatedness effects in word lists (Meyer & Schvaneveldt,
9
1976). There is some evidence that a word that is semantically related to another word may
prime it (Sereno & Rayner, 1992). For example, Carroll & Slowiaczek (1986) found facilitation
for words when preceded by a close semantic associate (e.g., “king” and “queen”), but only
when the words were in the same clause in the sentence. Intralexical priming effects in reading,
however, appear to be short lived so there is debate over how much a sentence context can
affect lexical processing. Other studies have found evidence that lexical relatedness is not
always sufficient to produce a processing advantage (Duffy et al. 1989; Hess, Foss, & Carroll,
1995; Masson, 1986). Thus, intralexical priming effects may not be able to fully describe how
sentence contexts constrain lexical processing.
If it is not simply the words in the sentence which are priming the activation of target
words, what may be affecting word processing? Another factor that may affect lexical
processing in sentence context are interactive sentence context effects. As mentioned above,
Morris found that words are processed faster when preceded by a semantically constrained
sentence. Morris (1994) also examined the time it takes to process words in a sentence when the
sentence varies by structure only (i.e., the content words remain the same) by measuring reading
times using eye-tracking. Compare the two sentences below:
(1) The waiter watched as the accountant balanced the ledger the second time.
(2) The waiter who watched the accountant balanced the ledger the second time.
In this example, the target word was “ledger.” The verb in the sentence, “balanced” was used
because it was semantically related to the target word. Sentences were also created with two
nouns that were semantically related to the verb. In this case, the two nouns were “waiter” and
“accountant.” This created two different scenarios (e.g., “the waiter balanced” and “the
accountant balanced”). Morris found that readers spent less time reading the target (ledger) for
10
sentence (1) than sentence (2). In sentence (1), the accountant is the person who balances, not
the waiter. Thus, when the reader encounters the target ledger, the semantically congruent
interpretation is sentence (1), which results in shorter reading times. These findings suggest that
it is not merely the presence of words in the sentence that primes lexical processing, but that
there is processing at the larger sentence (i.e., syntactic) level which affects word processing.
In order to examine how semantics and syntax affect lexical processing in sentence
context, O’Seaghdha (1997) tested monolinguals in English using phrases that were biased for
nouns or verbs and a LDT on the last word of the phrase. The target words were manipulated
for syntactic appropriateness and semantic congruity within the phrase. The results from the
LDT showed that the semantic effect was larger when the target word appeared in a
syntactically appropriate context. When the target was in a syntactically incongruent context,
the semantic effect was not as large. These results suggest that both semantics and syntax
contribute to the processing of target words.
In another study, Folk and Morris (2003) used an eye-tracking task to investigate the
processing of syntactically ambiguous words (e.g., “duck” can be a noun (animal) and a verb (to
duck)) and semantically ambiguous words (e.g., “bank” is a noun with multiple meanings) in
sentence contexts. Folk and Morris found that participants had longer fixation times on lexically
ambiguous words only when the target word was syntactically unambiguous, but not when it
was syntactically ambiguous. Thus, this study finds a privileged role for syntactic category. The
assigning of syntactic-category precedes meaning resolution in a sentence context. This finding
differs from the results found in Wong and Chen (2012), which looked at monolingual isolated
word processing. Wong and Chen created the semantic-mediation model, which claimed that
semantic processing precedes syntactic processing. Wong and Chen argue that these findings do
11
not disprove the semantic-mediation model; rather, the model explains the findings from Folk
and Morris by arguing that the sentence context strengthens the syntactic representation of the
target, which would otherwise not occur in isolation. Since more research is needed in order to
better understand how a sentence context’s semantics and syntax affect lexical processing, the
current study was created to investigate these issues. Next, I will discuss the findings from
research in bilingual lexical processing in sentence context.
Bilingual Lexical Processing in Sentence Context
Most of the research on bilingual lexical processing in sentence context has examined
whether bilinguals process selectively or non-selectively when processing words in a
semantically constrained sentence context. For example, Van Hell and De Groot (2008)
conducted an experiment where highly proficient Dutch-English bilinguals completed a LDT
with cognate target words after reading sentences with high or low semantic constraint. The
sentence context consisted of English words (L2) and the bilinguals were not instructed to use
their L1 at any point during the study. If participants were to react faster to the LDT for cognate
target words than matched non-cognate words, the authors predicted that both languages would
be active while reading sentences in the L2. In order to test this prediction, two conditions of
semantic constraint were created for the sentence contexts: high and low semantic constraints. A
highly constrained sentence was a sentence that restricted what target word could logically fit in
the blank. Consider example (3):
(3)
The best cabin of the ship belongs to the _______.
The bilinguals read this sentence and were immediately presented with the cognate target word
“captain” afterwards (the Dutch word for captain is kapitein). This sentence context
semantically constrains what the target word can be, and was hypothesized to help the
12
bilinguals predict what word should fill the space. The low semantic constraint sentences did
not predict a specific target word, as shown in (4):
(4)
The handsome man in the white suit is the ________.
Many different lexical items could fill the blank in the sentence, so the target word, which in
this case was also “captain,” is not as greatly restricted. The authors had cognate and noncognate target words and compared reaction times and accuracy on a LDT.
Van Hell and De Groot found that the cognate effect disappeared for the high semantic
constraint sentences. In other words, the Dutch-English bilinguals had statistically similar
reaction times to cognate and non-cognate target words for the LDT after reading sentences
with high semantic constraint. The authors argued that the semantics of the sentence may have
helped pre-activate the target word (as Sereno & Rayner, 1992 argued with semantic priming
effects in the monolingual domain), so that the facilitation that cognates typically have in
isolation was superseded by the sentence context; the top-down processes provided by the
semantic constraint of the sentence context overrode the bottom-up processes of lexical
activation. The authors did not find the same pattern of results for the low semantic constraint
sentences. With these sentences, the cognate effect remained. Thus, the bilinguals completed the
LDT faster and more accurately for cognate target words than non-cognate targets. This result
was surprising for the authors because the bilinguals were only reading sentences in English.
Even though they were only reading in one language, finding a cognate effect suggests that they
were still sensitive to influence from their L1. The authors argue that the bottom-up processes of
word activation were not constrained by the semantics from this condition since many different
possible words could have fit in the sentence. Thus, the sentence context itself did not affect
word recognition, even though the whole sentence was in the same language. These findings
13
show that sentence context can affect word recognition because the degree of semantic
restriction determines whether the cognate effect remained or disappeared.
Libben and Titone (2009) further investigated how sentence context may affect word
recognition by monitoring the eye movements of French-English bilinguals reading sentences in
English (L2). Once again, the target words were cognates and matched controls. The sentence
contexts also had two conditions: low and high semantic constraint. These sentences were
slightly different than (3) and (4) in that the target word appeared in the sentence (there were no
blanks), and each sentence consisted of two clauses. The first clause biased for high or low
semantic constraint for the target word, and the second clause contained the target word.
Example (5) gives an example of one high semantic constraint sentence and one low semantic
constraint sentence:
(5)
a. Because of the bitter custody battle over the kids, the expensive divorce was a
disaster. (high semantic constraint)
b. Because they owned a lot of property around the world, the expensive divorce
was a disaster. (low semantic constraint)
The researchers recorded the reading times for the cognate target word divorce in both
conditions and compared them to the reading times of non-cognates.
Libben and Titone examined early-stage comprehension measures and late-stage
comprehension measures in order to investigate initial lexical access and how this process
changes as later-stage processes are included. The early-stage comprehension measures are first
fixation duration (i.e., how much time is spent on the target word the first time it is
encountered), first pass duration (i.e., the total amount of time spent reading the target word
before moving on), and skipping (i.e., the proportion of trials where there is no fixation on the
14
target word). The late-stage comprehension measures are go-past time (i.e., the reading times
from when the eyes first land on the target until they fixate on a word to the right of the target)
and total reading time (i.e., the total reading time spent on the target word). The results from the
early-stage comprehension measures, which on average was the time range from the initial
fixation on the target until 350ms later, revealed that cognate words were read faster than noncognate words for both high and low semantic constraint sentences. This finding suggests that
lexical access was non-selective at the early stages of reading for both sentence conditions, and
word access was not sensitive to sentence context. The late-stage comprehension measures, on
average from 350ms to 600ms after first fixating on the target word, showed that the cognate
effect was only found for the low semantic constraint condition, not the high semantic
condition. Thus, in the later stages of lexical access, the French-English bilinguals were
sensitive to sentence context condition, and this sensitivity caused the cognate effect to
disappear for the high semantic constraint sentences. The findings from Libben and Titone help
specify when in the time course of lexical access that biasing sentence contexts affect word
recognition.
Van Assche, Dreighe, Duyck, Welvaert, and Hartsuiker (2011) also used eye-tracking to
test Dutch-English bilinguals reading identical and non-identical Dutch-English cognates in
sentence context. As opposed to Libben and Titone who only found a cognate effect in early
measures, Van Assche and colleagues found a cognate effect in both early and late measures. In
order to explain these varying results, Van Assche and colleagues pointed out that their
participants may have been less proficient in the L2 or possibly learned the L2 later in life than
the participants in Libben and Titone. Thus, with a more dominant L1, it may have been more
15
difficult for these particular participants to shut off the L1, even with semantically constrained
sentence contexts.
Titone, Libben, Mercier, Whitford, and Pivneva (2011) further looked at cross-language
activation in the L1 by examining their participants’ age of L2 acquisition and also considering
task demands, such as the inclusion of L2 sentences. Their participants were English-French
bilinguals and they were tested with interlingual homographs, cognates, or control words. They
found that the English-French bilinguals reading in the L1 showed non-selective activation as
long as they had acquired the L2 early in life. When Titone and colleagues changed the task
demands by including French filler items, cross-language activation was increased. Thus, the
researchers concluded that L1 bilingual reading is modulated by L2 knowledge, semantic
constraint, and task demands.
Only one preliminary study has as of yet examined how syntactic sentence constraint
affects word processing. Gullifer, Dussias, and Kroll (2010) tested highly proficient SpanishEnglish bilinguals on word naming when reading sentences with high and low syntactic
constraint. The authors defined syntactic constraint as whether or not the syntax of the sentence
was specific to one language or non-specific because the syntactic structures appeared in both
languages. For example, the high syntactic constraint sentence in Gullifer et al. included a preverbal clitic marker le “to him/to her/to it” and a null subject, as in (6).
(6)
Los estudiantes le contaron el cuento que [pro] leyeron el otro día al profesor de
literature inglesa.
“The students recounted the story that they read the other day to the professor of
English literature.”
16
These two linguistic characteristics are specific to Spanish syntax because English does not
have preverbal object markers and requires an overt subject. Thus, the syntax is giving a strong
cue that the language is Spanish. The low syntactic constraint sentences had word orders that
were non-specific, as shown in (7).
(7)
El taxista que estaba estacionado en la esquina de la panadería llevó al profesor a
su casa.
“The taxi driver who was parked at the corner of the bakery took the professor to
his house.
Since the syntax is non-specific, syntax alone could not be a cue to determine language when
bilinguals read the low syntactic constraint sentences.
The target words in both of these sentence conditions were cognate words, such as
profesor, which overlap in orthography, meaning, and phonology in both English and Spanish.
The cognate words appeared in a red font, and the bilinguals were asked to name the word in
the red font after the presentation of the entire sentence. Gullifer et al. measured reaction times
for naming the target cognates words and matched non-cognates in order to see if there was a
cognate effect in either condition. The results revealed that there was a cognate effect in the low
syntactic condition, but not the high syntactic condition. Thus, the results from the syntactically
constrained sentences parallel the results from the semantically constrained sentences: the
highly constrained sentence can override cognate facilitation, but the less constrained sentence
does not, suggesting that there is parallel activation for the bilinguals’ two languages. The past
research has shown that a sentence context’s semantics and syntax may be able to bias the
bottom-up processing of word processing when reading in the L2. Next, I discuss findings of L2
activation when reading sentence contexts in the L1.
17
Van Assche et al. (2009) examined L2 activation when Dutch-English bilinguals read
sentences in the L1. They used eye-tracking to measure target cognates and matched controls in
reading low constraint L1 sentences. The Dutch-English bilinguals were different from previous
bilinguals tested because they began learning their L2 around the age of 14-15. Van Assche and
colleagues found a significant cognate effect, although they emphasized that the size of the
effect was very small (only 10ms) compared to other found cognate effects. Thus, the L2
activation during L1 reading may not be as strong as L1 activation during L2 reading. This
study gives further evidence of non-selectivity when reading in the L1, even for bilinguals who
begin acquiring the L2 at a later age.
The results from these studies led Kroll, Dussias, Bogulski, and Kroff (2011) to argue
that the bilingual system is designed for parallel activation. Not only has there been ample
evidence in the visual word recognition field, but recently Lagrou, Hartsuiker, and Duyck
(2011) found further evidence for non-selectivity in the auditory modality. In their study,
Dutch-English bilinguals completed an auditory LDT in L2 (Experiment 1) and L1 (Experiment
3). English monolinguals completed Experiment 2, as a control group. Targets were spoken by a
native Dutch speaker or native English speaker (each with the opposite language as the L2).
They looked at interlingual homophones, which overlap in phonology but not semantics, as the
targets. They found that interlingual homophones were recognized slower by all bilinguals
compared to matched control words. This study gives further evidence for language nonselectivity, even with a different modality. Even the language-specific cues based on accent did
not override cross-linguistic activation.
The past studies have examined the effects of semantic and syntactic cues on word
processing. The semantic cues were lexical, as the meaning of the words in the sentences
18
restricted what words could be activated for the target word. However, there is another type of
semantics: grammatical class semantics. Each word belongs to a grammatical class, and the
grammatical class can encode both semantic and syntactic features. Past research on isolated
word recognition (Sunderman & Kroll, 2006; Campbell, 2009) has shown that L2 learners are
sensitive to grammatical class, such that interference effects were found when the prime-target
word pairing shared the same grammatical class. These grammatical class interference effects
disappeared, however, when semantics was engaged. The participants in these studies may not
have been as sensitive to cues of grammatical class since the words were displayed out of
context. By putting the target words in a sentence context, the grammatical class cues could be
strengthened.
Accordingly, a study was conducted by Baten, Hofman, and Loeys (2011) that tested
Dutch-English participants who were L1 dominant in Dutch and highly proficient in English for
grammatical class effects in a sentence context. The participants read sentences in English and
completed a LDT. The last word in the sentence was the target word. It was an interlingual
homograph with matching or mismatching grammatical class in both languages, or it was a
matched control word. The following sentence (8) gives an example with an interlingual target
word:
(8)
She looked up and there seemed to be an angel.
The homograph in (8) is angel, which means “hook” in Dutch. Both the Dutch “hook” and the
English “angel” are nouns, so the grammatical class overlaps between languages. An example
of a sentence for a non-overlapping homograph target word is shown in (9):
(9)
He told me he thinks this news is very big.
19
The homograph big is an adjective in English but is the noun “piglet” in Dutch. Thus, there is
no overlap of grammatical class between the two different languages.
Each word of the sentence was presented one at a time for 700ms. The word preceding
the target word had a red font and appeared on the screen for 1200ms. The red font was an
indication to participants that the target would be appearing next. The researchers did this in
order to prevent any grammatical class ambiguity. For instance, when reading, “She looked up
and there seemed to be an…,” the reader may have expected either an adjective or noun after
the determiner an (e.g., an angelic face versus an angel). By putting the word that preceded the
target (e.g., the determiner an) in a red font, this cued the reader to predict that the target will be
a noun, not an adjective. Participants were instructed to indicate whether the target word was a
word in English as quickly and accurately as possible. Baten et al. measured reaction times and
accuracy on the LDT.
The researchers found that, once again, there were effects of cross-linguistic activation.
Participants were faster and more accurate to respond to the lexical decision when the
interlingual homographs overlapped in grammatical class, compared to when they did not
overlap. By putting interlingual homographs in a sentence context, the target word is being
primed to belong to a specific grammatical class. Consider once more example (8), the sentence
context for the overlapping homograph: “She looked up and there seemed to be an angel.”
When the participant reads the indefinite determiner an in the red font, this word provides a cue
that the next word that appears is going to be a noun. Similarly, consider again the sentence
context with non-overlapping homographs: “He told me he thinks this news is very big.” The
target in this sentence is preceded by the adverb very, which participants know can modify an
adjective. Thus, the participants are being primed to expect an adjective from the sentence
20
context alone. By reading big, which can also be a noun in their L1, this causes interference in
deciding whether the target is a word because it does not fit expectations. Therefore, highly
proficient bilinguals seem to be sensitive to the grammatical class of both languages, even when
only reading in their L2. Future research is needed to see if L2 learners are sensitive to
grammatical class effects at different levels of proficiency and with varying levels of semantic
constraint, and the current study addresses these questions.
Word Recognition and L2 Development
There are still questions regarding how the development of L2 proficiency affects word
recognition in a sentence context. Most of the studies that have examined word recognition in a
sentence context have examined highly proficient bilinguals. However, in the field of word
recognition, there are models that predict that L2 learners process words differently at different
levels of proficiency. The revised hierarchical model (RHM) proposed by Kroll and Stewart
(1994) argues that L2 learners process words differently as their proficiency levels increase. At
lower levels of proficiency, L2 learners recognize words in their L2 by lexical associations with
L1 translations, while more proficient learners recognize L2 words at the conceptual level. In
other words, less proficient L2 learners are more likely to translate a L2 word into the L1, while
more proficient L2 learners are more likely to connect the L2 word directly to its concept, as is
typically done for L1 words. Thus, this model predicts that L2 learners process words
differently in their L2, depending on their proficiency level. The Bilingual Interactive
Activation Plus (BIA+) model (Dijkstra & Van Heuven, 2002) also predicts that wordprocessing will change as proficiency increases. For less proficient L2 learners, the resting
activation levels of L2 words are lower than activation levels for highly proficient bilinguals.
The L2 learners, therefore, are slower to process words in their L2 compared to bilinguals with
21
high proficiency. Both the RHM and the BIA+ models argue that less proficient L2 learners
process words differently than more proficient bilinguals. As of yet, to my knowledge no study
has examined how sentence contexts affect word recognition as development in L2 proficiency
increases.
One study, Schwartz and Kroll (2006), compared how sentence context affects L2
learners and highly proficient bilinguals in a production task. Schwartz and Kroll had
intermediate learners of English (L1 Spanish) and highly proficient Spanish-English bilinguals
read sentences in English (as shown in example 10) with high and low semantic constraint and
then complete a word-naming task.
(10)
a. Before playing, the composer first wiped the keys of the piano at the
beginning of the concert. (high semantic constraint)
b. When we entered the dining hall we saw the piano in the corner of the room.
(low semantic constraint)
Each word of the sentence appeared on the middle of the screen for 250ms at a time, and the
target word (in the examples above, the cognate piano) had a red font. After the final word of
the sentence was presented, the participants were instructed to name aloud the red word as fast
as they could. The researchers recorded the time it took the participants to name the target word
and compared the naming latencies for cognate targets and matched non-cognates. The results
for the intermediate L2 learners and the highly proficient Spanish-English bilinguals showed
that both groups performed similarly. As in the word recognition studies that examined the
effects of semantics on word processing, the cognate effect disappeared in the high semantic
constraint sentences, but remained in the low semantic constraint condition. Thus, for word
naming, it appears that intermediate L2 learners are influenced by highly constraining semantic
22
cues at the sentential level. The present study seeks to examine whether similar results are found
for a word recognition task (via a LDT) and to investigate whether L2 learners are also sensitive
to other sentential cues besides semantics, such as syntax. Moreover, the current study also
addresses how L2 learners and bilinguals with various levels of proficiency in Spanish and
English process lexical items differently.
In the next chapter, I will discuss two models of lexical processing and address the
constraints on sentence comprehension. I will also present a hypothesis that accounts for how
sentence context affects word processing. Finally, I will discuss future areas of lexical
processing in sentence context.
23
CHAPTER TWO
SENTENCE CONTEXT AND LEXICAL PROCESSING
Introduction
When examining visual word recognition for both monolinguals and bilinguals,
researchers ask: How are words accessed in the lexicon? What word-property representations
are activated during word recognition, and how do these representations compete for activation?
For bilinguals, the situation is more complex as there are two languages in the mind. This leads
to the following questions: Do both languages form one integrated lexicon or are they separate?
Is activation selective or non-selective? In this chapter, I first begin by reviewing a model of
monolingual word recognition, the Interactive Activation model (McClelland & Rumelhart,
1981). Next, I present the bilingual version of this monolingual model, the Bilingual Interactive
Activation Plus model (Dijkstra & Van Heuven, 1998). After reviewing the two models of word
recognition, I then present various constraints that affect sentence comprehension according to
the framework of Gibson and Pearlmutter (1998). Finally, I discuss the features restriction
hypothesis (Schwanenflugel & LaCount, 1988; Kellas, Paul, Martin, Simpson, 1991), a model
of sentence processing. I conclude by explaining the motivation for the current study.
The Interactive Activation Model
One model of monolingual word recognition is the Interactive Activation (IA) model
(McClelland & Rumelhart, 1981). The IA model is an interactive direct access model of lexical
processing that has three levels of representation. The first is a level of features; the second is a
level of letters; and the third is a level of whole words. These three different levels contain
nodes that make excitatory (i.e., facilitating) connections and inhibitory connections. The nodes
between different levels of representation can make either type of connections, but nodes within
24
the same level can only make inhibitory connections. When a reader sees the letter “b” in the
word initial position, the feature level activates the letter level, which in turn activates nodes in
the word level that are possible candidates for this description. At the same time, nodes in the
word level that do not have a “b” in the word initial position are inhibited. When the whole
word blue is presented to a reader and the word node for blue is activated, this activation
inhibits other word nodes that may be competing for activation, such as blur, which would have
been activated from the letter level. It is through both activation and inhibition that a reader
recognizes the correct word. The IA model, therefore, can make claims regarding how lexical
items are represented in the mind and how a reader retrieves these representations.
Claims of the Interactive Activation Model
The IA model makes various predictions about how priming will affect readers’
processing of words. When a target word is preceded by a form related non-word prime, the IA
model claims that there will be facilitatory effects. For example, the non-word clib will partially
activate the word node CLIP. If CLIP is the target word being primed, this target word will have
faster reaction times in a lexical decision task (LDT). In a LDT, readers have to indicate by
means of a “yes” or “no” response whether a string of letters is an actual word. The IA model
claims that readers should be faster to make this decision with a form related non-word prime
because of the pre-activation, although partial, of the target word. If the prime were an unrelated
non-word like awly, then the reaction times for a lexical decision task would not be as fast as
the times for a related non-word prime.
The IA model makes claims about inhibitory components of priming, as well. Related
word primes cause more inhibition than related non-word primes. Thus, the related word prime
able would require more inhibition when the target word is AXLE than when the related non-
25
word prime azle precedes the target word. Inhibition occurs because able must be inhibited at
the word level node, while azle is not a word so it does not generate a lexical representation.
However, for excitatory priming, the lexical status of the related prime does not seem to affect
pre-activation: related word and non-word primes yield facilitating priming in LDT tasks. Both
azle and able will facilitate the decision time for the target word AXLE. In other words, related
word primes will produce both facilitation and inhibition for target words, but non-word primes
will only yield facilitation.
The IA model also makes claims about the shared neighborhood effect. Neighbors are
two words that only differ with respect to one letter, in the same position (e.g., it is generally
understood that brown and crown are neighbors). The shared neighbor effect says that a word
prime will inhibit reaction times on a LDT when the prime and target word both share a lexical
neighbor. For example, the prime-target pair wait-BAIT will have a larger inhibitory effect than
wait-WANT or bail-BAIT. From the first pair, the words “wait” and “bait” share the common
neighbor GAIT. Thus, there is another third word that differs by one letter from this word pair,
strengthening the lexical activation of all three words. However, the target word “want” from
the second pair is only a neighbor with “wait” and similarly from the third pair, “bait” only has
the neighbor “bail.” Since the first pair, “wait” and “bait” both share a neighbor (GAIT) and a
stronger activation of the words, this leads to the prediction that it is more difficult to suppress
the activation, creating a larger inhibition effect of priming compared to the effects found for
the second and third pairs that did not share a third lexical neighbor.
Previous Research on the Interactive Activation Model
Many studies have been conducted to test the claims of the IA model. Davis and Lupker
(2006) tested various predictions of the IA model: the prime lexicality effect, shared
26
neighborhood effects, and inhibitory effects for neighborhood size of non-words. In Experiment
1, the researchers tested the prime lexicality effect whereby related non-word primes produce
facilitation priming, but related word primes produce inhibitory priming on the target word.
They reasoned that related non-word primes facilitate priming because they do not generate
lexical representations, which may inhibit processing of the target, but instead pre-activate the
target word. The word primes, on the other hand, pre-activate the target word plus competitors,
and processing is slowed as the competitors are inhibited by the target word. Davis and Lupker
used a masked-priming LDT to test for both facilitatory and inhibitory priming effects. In this
masked-priming LDT, a row of five number signs (#####) was presented for 50 ms, followed
by the presentation of the prime in lowercase letters for 57ms, and then the presentation of the
target in uppercase letters until a “yes” or “no” response was given. The masked-prime LDT
was used in order that the participants were not consciously aware of the prime and so the
researchers could conclude that their responses were due to automatic processes, instead of
conscious ones. The results from Experiment 1 showed that there was a prime lexicality effect:
related non-word primes yielded faster reaction times on the LDT than unrelated non-word
primes, while related word primes slowed reaction times on the LDT compared to unrelated
word primes. Thus, the predictions from the IA model were confirmed: a prime lexicality effect
was found for non-word primes only since non-word primes do not pre-activate the target word.
The related words caused interference due to their pre-activation of the target word.
Experiment 2 tested the shared neighborhood effect and investigated whether there
would be inhibition for prime-target words and non-words that shared a lexical neighbor (e.g.,
trace-BRACE share the neighbor GRACE) versus prime-target word and non-word pairings
that had no other neighbors (e.g., heard-BEARD do not have another shared neighbor). The IA
27
model predicted that the prime-target pairing that did not share any other neighbors would not
show inhibitory priming effects due to neighbors because the prime’s only strong competitor
would be the target word. However, when there are shared neighbors between the prime and
target, there are at least two competitors activated when the target is presented. Inhibition needs
to take place in order to suppress the non-target competitor, which results in slower reaction
times in the LDT. The results from Experiment 2 agreed with the predictions from the IA
model: there was more inhibition for the prime-target pairings that shared a neighbor than for
the prime-target pairings that had no shared neighbor.
Experiment 3 investigated inhibitory priming effects for words and non-words with
varying numbers of neighbors. When the number of neighbors was increased for non-words, the
inhibition increased, slowing reaction times for the LDT from 12ms to 29ms. When the nonwords had more neighbors, they became more word-like and had to inhibit competitors that
were activated. This increased competition for retrieving the target word could explain the
slower reaction times. Davis and Lupker further explained these results by suggesting that the
difficulty of the word-non-word discrimination may affect how participants respond on a LDT.
A non-significant effect was also found for target words when the neighborhood size increased:
the inhibition decreased from 27ms to 14ms. If this trend to decrease inhibition for target words
with more neighbors were a true effect, it would go against the predictions of the model since
higher neighbors typically results in greater inhibition. Further research is needed in order to
verify this result. Overall, Davis and Lupker found evidence to support the predictions of the IA
model from word and non-word primes.
Perry, Lupker, and Davis (2008) continued the investigation into the claims of the IA
model. The researchers examined how ambiguous and nonambiguous partial-word primes affect
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the processing of target words with no neighbors, a low number of neighbors, or a high number
of neighbors. The researchers used two masked priming lexical decision experiments. In the
first experiment, the primes were ambiguous partial-words, such as #rown. The number sign
was used in place of one letter of the word, thus creating a partial-word. The prime #rown can
be multiple words: brown, crown, drown, etc. The partial-word is therefore ambiguous because
it can be one of many words. The target words in Experiment 1 were words with few neighbors
(low-N) or many neighbors (high-N). In Experiment 1A, the non-word targets did not have any
neighbors, while in Experiment 1B, half the non-word targets had few neighbors and the other
half had many neighbors. For the experiment, a row of hash marks (#’s) appeared on the screen
for 590ms. Next, the partial-word prime was presented on the screen for 60ms in lowercase
letters, and then finally the target word immediately followed and remained on the screen until
the readers made a decision.
In Experiment 1, related and unrelated prime-target pairs were created. A related prime
was the same as the target word or non-word, but one of the letters was replaced by the number
sign, #, such as #rown. Since this experiment used ambiguous partial-word primes, the prime
always could be the target word and another word (e.g., brown and drown). Unrelated prime
words did not have the same first letter as the target word and was a related prime word for
another target. The researchers created two lists to ensure that if a prime was preceded by a
related prime in one list, it was preceded by a nonrelated prime in the other list. The model
predicted two findings for Experiment 1. First, high-N words were expected to have less
priming facilitation than low-N words, regardless of the word-non-word discrimination
difficulty, per Forster, Davis, Schoknecht, and Carter’s (1987) neighborhood density effect.
Since high-N target words have more neighbors with the partial-word prime, the high-N target
29
words would be pre-activated from the prime, as would other neighbors. Thus, there would be
more word nodes to suppress, which would result in slower reaction times. A second prediction
from the model is that an increase in difficulty for the word-non-word discrimination should
lead to a decrease of priming effects for both low-N and high-N target words, as found in Davis
and Lupker (2006). As discrimination between words and non-words becomes more
challenging, the priming effects may not be as robust due to the varied number of neighbors for
the non-words.
The results from Experiment 1A showed that priming was significant overall. Low-N
word targets had more priming (30ms faster reaction times for related pairs than unrelated pairs)
than high-N word targets (only 11ms faster reaction times for related pairs). This priming was
not significantly different, but it matches the prediction about the IA model that there are greater
priming effects for low-N words, a phenomenon known as the density constraint (Forster, 1987;
1993). For Experiment 1B, it was predicted that the size of the priming would decrease because
of the increased difficulty in word-non-word discrimination. Numerically, these results were
found: low-N words only had 17ms of priming (compared to 30ms for low-N words in
Experiment 1A), while high-N words only had 7ms of priming (compared to 11ms).
Experiment 2 followed the same procedure as Experiment 1, except the partial-word
primes were unambiguous and the researchers also included hermit target words (words with no
neighbors). The model predicted that low-N and high-N target words should have more priming
effects from unambiguous primes than hermit target words, regardless of the word-non-word
discrimination difficulty. Unambiguous primes, like cr#wn, do not activate lexical
representations of all of the target’s neighbors (e.g., the neighbors DROWN and BROWN
would not be activated for the target CROWN). Because of this type of priming, the target can
30
more quickly inhibit competitors and have faster reaction times. Therefore, the model predicts
that hermit words will not have as much facilitation because they do not benefit from having
any neighbors to quickly suppress. Second, the model predicted that reaction times for hermits
and high-N target words would remain relatively constant as the word-non-word discrimination
increased in difficulty, but that the priming effects for low-N targets would increase as the
word-non-word discrimination became more difficult. Perry et al. argued that this prediction
results from the idea that low-N target words only have a few neighbors to suppress. An
unambiguous prime word can allow the target word to quickly suppress these neighbors,
eliminating competition between the neighbors and resulting in faster reaction times. The
hermits and high-N target words were predicted to not have this priming advantage as wordnon-word discrimination difficulty increased because hermits do not have neighbors to suppress
and are therefore not affected by discrimination difficulty levels, and high-N target words are
overwhelmed with the number of neighbors and have equal difficulty overcoming the number
of neighbors whether the word-non-word discrimination is easy or difficult.
The results from Experiment 2A, which had low-N, high-N, and hermit word targets and
easy non-word targets, found that the priming size was only slightly larger for low-N and highN target words than hermits (29ms and 27ms respectively, versus 24ms), but these priming sizes
were not as different as the model predicted. In Experiment 2B, where there were both easy and
hard non-word targets, the hermit target words actually had more of a priming effect (51ms)
than the low-N (29ms) and high-N (28ms) target words. The results from Experiment 2 also
showed that high-N target words did have similar reaction times for easy and hard word-nonword discrimination, but that hermits and low-N targets did not follow the predictions from the
model. The hermit word targets had an increase in priming from 24ms to 51ms as the word-non-
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word discrimination increased in difficulty, although it was predicted that the priming effects
would remain constant. The low-N priming effects, however, remained constant when they
were predicted to increase. The researchers were not confident in explaining why the model’s
predictions were not realized in the data for unambiguous primes. Perry et al. discussed that
smaller word frequencies overall for the hermits, compared to the low-N and high-N target
words, may have played a role, but they were not sure to what degree. Also, they considered
that a less stringent definition of neighbor may help support their findings. For instance, the
researchers suggest, the partial-word prime sa#ce could activate both SAUCE and SPACE.
Under the traditional definition, SAUCE and SPACE are not neighbors because the letter that
differs between them is not in the same position. This definition may not be realistic, according
to the authors, and they suggest a less strict definition of lexical neighbors in order to explain
how hermits received more priming than low-N and high-N targets.
The Bilingual Interactive Activation Plus Model
The Bilingual Interactive Activation Plus (BIA+) model (Dijkstra & Van Heuven, 2002)
is an updated version of the Bilingual Interactive Activation (BIA) model (Van Heuven,
Dijkstra, & Grainger, 1998). The BIA model was based on the IA model, but created in order to
account for lexical processing for bilinguals whose mental lexicon consists of more than one
language. Thus, like the IA model, the BIA+ model describes how a reader accesses and
retrieves lexical items during processing.
As shown in Figure 2.1, The BIA+ contains an Identification system and a Task schema.
The Identification system contains different levels of linguistic nodes, which interact with one
another as lexical activation occurs. In the first level, sublexical orthography and phonology is
activated. For instance, if the target word were desk, words that share similar orthographic and
32
phonological characteristics to the input string would be activated in parallel, such as words
possibly containing “b,” “a,” “sh,” “g,” etc. The activated sublexical orthographic and
phonological representations would then activate lexical orthographic and phonological
representations, such as dock and bash.
Figure 2.1. The BIA+ Model (adapted from
Dijkstra & Van Heuven, 2002)
Importantly, since the BIA+ is a non-selective and integrated lexicon model, words from both
languages would be activated at this point in processing. However, the activation of
representations depends on the resting level of activation of the lexical items, which is
determined by the frequency of the word candidate. Thus, assuming that on average the L2 is
less frequent than the L1, the activation in the L2 language will be slower than the L1. After the
lexical orthography and phonological nodes are activated, semantics and the language node are
then incorporated. The semantics node activates word representations that are similar
semantically to the target. In this case, semantically similar words for desk may include “chair”
33
and “table.” The language node selects the language of the target. In other words, the language
node assigns a membership tag to the target word.
The input activated from the Identification system feeds into the Task schema. The Task
schema accounts for nonlinguistic processing, such as task demands, instruction, or participant
expectancies. While the input from the Identification system continuously flows into the Task
schema, the reverse is not true: the Task schema cannot influence processing in the
Identification system. In other words, the Task schema can alter how the input from the
Identification system is used, but it cannot alter the candidates being activated in the
Identification system. The Task schema was included in the model to explain how findings
differ from various tasks and demands and thus, is an important part in the overall word
recognition process.
Claims of the BIA+ Model
The BIA+ model claims that the bilingual lexicon is non-selective and integrated. When
a bilingual is reading words, candidates from both languages are activated from one large
mental lexicon store. The BIA+ model also predicts that linguistic context can affect which
lexical candidates become activated. Dijkstra and Van Heuven define linguistic context effects
as effects resulting from information from the lexical, syntactic, or semantic sources or, in other
words, the sentence context. The authors propose that these differing contexts can affect word
recognition by constraining the degree of language selectivity.
Previous Research on the BIA+ Model
Duñabeitia, Perea, and Carreiras (2010) investigated the claims of the BIA+ model by
testing Basque-Spanish simultaneous bilinguals with a masked-priming LDT. The prime-target
word pairings in this experiment consisted of Basque-Spanish or Spanish-Basque translation
34
pairs (the related word condition), such as liburu-LIBRO (the Basque/Spanish equivalent of
book), or an unrelated word condition pairing, such as zarata-LIBRO or ruido-LIBURU
(meaning noise-BOOK in Basque-Spanish and Spanish-Basque, respectively). For the related
condition, the researchers paired half of the target words with a cognate prime, a translation
equivalent with similar spelling and phonology to the target word. The other half of the target
words was paired with a translation equivalent that differed in orthographical and phonological
overlap. The LDT was conducted with a masked-priming methodology. For this task, balanced
Basque-Spanish bilinguals tested in Spain had to decide whether a string of letters was a word
in either Basque or Spanish. For the experiment, a row of hash marks (#’s) appeared on the
screen for 500ms. Next, the prime was presented on the screen for 47ms in lowercase letters,
and then finally the target word immediately followed and remained on the screen until the
bilinguals made a decision. The participants were never told about the presence of the prime
word and due to the hash marks and the quick presentation of the stimulus (47ms), none of the
participants reported seeing the prime word when asked after the experiment.
Duñabeitia et al. found that the highly proficient, balanced Basque-Spanish bilinguals
showed priming effects for both Basque and Spanish prime words. Thus, the bilinguals were
sensitive to the presentation of masked-primes in both languages. With respect to the cognatenoncognate status of the prime words, the bilinguals showed larger priming effects for cognate
primes than noncognate ones. Cognate prime-target word pairings were processed 31ms faster
than non-cognate prime-target word pairings. The results from this study also showed that there
were no asymmetries in the magnitude of the priming effects between Basque-Spanish and
Spanish-Basque translation pairs. For the noncognate pairs, the Basque-Spanish priming effect
was 16ms, while the Spanish-Basque priming effect was 20ms. For cognate pairs, the priming
35
effect was greater overall with Basque-Spanish pairings having priming effects of 44ms and
Spanish-Basque pairings having priming effects of 62ms. The authors predicted that there may
have been less priming effects for Basque-Spanish word pairings because overall, Basque words
were read approximately 100ms slower than Spanish ones. They argued that faster reading
times in Spanish may be attributed to higher levels of orthographic recognition to Spanish
words due to the fact that more books and newspapers are published in Spanish than in Basque
in Spain.
Duñabeitia et al. explain their findings with respect to the predictions of the BIA+
model. The researchers argue that the model can explain the cognate effect, whereby the
cognate prime-target pairings were faster than the noncognate pairings, due to the overlap of
orthography and phonology for the cognate words. Thus, when the Spanish word camión
“truck” is presented to the Basque-Spanish bilingual, the Basque equivalent kamioi is also
activated at the orthographic and phonological levels. Thus, a cognate has an advantage over
other translation equivalents because not only is meaning shared, but orthography and
phonology can also strength activation of the word in the other language. No asymmetries were
found in priming effects because the participants were balanced bilinguals. Thus, the BIA+
model would predict that the resting level of activation would be similar for the two languages
due to the words’ recency of use and the frequency.
Kerkhofs, Dijkstra, Chwilla, and De Bruijn (2006) also investigated the claims of the
BIA+ model with a semantic priming paradigm. The researchers examined how highly
proficient Dutch-English bilinguals performed on an English LDT by recording reaction times
and event-related potentials (ERPs). The prime words for the prime-target pairings included
English-only words that were related or unrelated to the target. The targets were interlingual
36
homographs (words that shared the same orthography and phonology in both languages, but did
not share the same meaning, such as angel which means “sting” in Dutch), English control
words, or non-words. Thus, the entire experiment was conducted in the L2, English. In trials
where the prime was semantically related to the target, the target was always an interlingual
homograph. Kerkhofs et al. created four different categories of interlingual homographs: high
and low frequencies for both Dutch and English. These different frequency categories were
created in order to test how words with different frequencies may interfere with lexical
processing. For the experiment, a fixation point appeared on the screen for 100ms. Next, the
prime was presented on the screen for 200m, followed by 200ms of a blank screen, and then
finally the target word immediately followed and remained on the screen for 200ms.
Participants had 2500ms to respond to the stimuli.
With regards to the reaction time results, Kerkhofs et al. predicted that the bilinguals
would have slower reaction times in the unrelated trials for interlingual homograph targets that
had high reading frequency in Dutch compared to the reaction times for interlingual homograph
targets that had low reading frequency in Dutch. These findings were predicted due to the nonselective nature of the lexicon, as proposed by the BIA+ model. They reasoned that it would be
more difficult to respond to a highly frequent Dutch word than a less frequent one. The
researchers also predicted a semantic priming effect where reaction times would be faster for
semantically related target-prime pairings (e.g., heaven-ANGEL) compared to unrelated ones
(e.g., launch-ANGEL). Finally, the authors predicted that semantic priming from related primetarget pairings would minimize inhibitory effects caused by the high frequency of the Dutch
meaning of a target homograph. For instance, in Dutch brief means “letter,” and this has a highfrequency reading. By preceding the target word brief with the prime SHORT, the prime could
37
help pre-activate the English meaning of the homograph, even though it is a lower frequency
reading of the word. It will also reduce the competition that is created by the higher frequency
Dutch word.
The results from the study showed that reaction times were faster for interlingual
homograph target words when the target was preceded by a semantically related prime word.
Also, high-frequency English homographs targets yielded faster reaction times than lowfrequency English homograph targets. Reaction times were slower when the homograph target
word had a high Dutch frequency than when the target word had a low Dutch frequency, except
for when the frequency for the English meaning of the target word was also low. When both the
frequency of the English and Dutch meanings of the interlingual homograph was low, reaction
times were slowest. Thus, the frequencies of both of the languages affected lexical processing.
The results from the reaction time data confirm the predictions based on the BIA+ model and
show that the bilinguals used non-selective access and that the frequencies of the words in both
languages also played a role in word recognition.
The researchers also collected ERP data in this study. Kerkhofs et al. predicted they
would find N400 priming effects for the related prime-target condition. The N400 is a negative
potential that peaks at approximately 400ms after the presentation of an open class word. N400
effects typically are elicited after semantic violations or unexpected words. The researchers
predicted that there would different ERP results from related target-word pairings (e.g., heavenANGEL) than unrelated pairings (e.g., launch-ANGEL). The N400 effects can also be inversely
related to word frequency: the more frequent a word is, the smaller the N400 should be. Thus, it
was predicted that the N400 effect would be affected by the word frequencies in both English
and Dutch. For instance, the N400 effect should not be as great for highly frequent English
38
target homograph words compared to English target homographs with lower frequency.
Oppositely, the highly frequent Dutch homograph target words should have a greater N400
effect than lower frequent Dutch homograph words. The findings from the ERP data showed
N400 effects for semantic relatedness and the frequencies of the English and Dutch words. The
N400 effect for semantic relatedness was similar to N400 effects found in monolinguals
because amplitudes were smaller for related prime-target pairs than unrelated pairs. Since this
experiment only had L2 words, this finding shows that the bilinguals were sensitive to semantic
priming in their L2 like monolinguals. The researchers also found that N400 amplitudes were
larger for low frequency English target words compared to high frequency English targets.
Thus, the bilinguals showed sensitivities to low frequent targets. For the interlingual
homographs with higher Dutch frequency, the bilinguals showed greater N400 effects than for
the less frequent Dutch homograph targets. Even though the targets were primed with L2
English words, the bilinguals still showed sensitivities to the frequency of their L1. These
findings support a language non-selective access, as the BIA+ model predicts. The findings also
support the idea that word frequency affects activation levels of lexical items and that semantics
affects word activation in the identification system. These findings are significant to better
understand how word recognition occurs for both monolinguals and bilinguals.
Similarities and Differences Between the Two Models
It is not surprising that the IA model and the BIA+ model share many similarities, since
the BIA+ extends the IA model to explain bilingual word recognition. Both models assume that
visual word recognition takes place by parallel activation at different level nodes and that
lexical selection occurs by inhibiting competitors. These two models of word recognition have
39
been able to account for priming effects found from LDTs. The models have been able to show
neighborhood effects and how word frequency may affect processing speed.
However, these two models also differ in how they explain visual word recognition.
Since bilinguals have more than one language in their mental lexicon, an extra language node is
required in the bilingual model of word recognition. The language node in the BIA+ model
serves to tag language membership to the input string. However, since lexical access appears to
be non-selective, the model proposes that this language node is not activated until later in the
word Identification system. The monolingual recognition model does not have to account for a
language node since it does not have to worry about language membership. The BIA+ model is
also more complex than the IA model because it includes nodes for phonology and semantics.
The IA model has levels for features, letters, and whole words but does not incorporate how
meaning or phonology affects word recognition. On top of extra linguistic nodes, the BIA+ also
has the Task schema, which is not part of the IA model. Task demands and participant
expectancies may affect both bilingual and monolingual word recognition, but they are only
accounted for in the BIA+ model. All in all, the two models are very similar in that they both
predict that an input string generates activation of sublexical components of words that lead to
the activation of the target word. The BIA+ is more extensive than the IA model because it
needs to account for more than one language and because it also includes other factors that
influence word processing, like semantics, phonology, and task demands.
Areas of Future Research for the BIA+ Model
Although there have been studies that have supported the predictions of the BIA+
(Dijkstra & Van Heuven, 2002; Duñabeitia, 2010; Kekrhofs et al., 2006), there are still research
areas that need to be investigated. The BIA+ model predicts that linguistic context affects word
40
recognition, but the question still remains: to what extent does a sentence context affect lexical
processing and how can this be encoded by the model? For instance, Schwartz and Kroll (2006)
and Van Hell and De Groot (2008) found that bilinguals processed cognate words faster in a
sentence context that did not highly constrained for meaning (e.g., “When we entered the dining
hall we saw the _____ in the corner of the room” where the target word that filled the blank was
piano). In other words, the semantics of the sentence context did not interfere with the bottomup processing and did not eliminate the cognate effect. However, sentences that were highly
constrained for meaning for the same cognate target words (e.g., “Before playing, the composer
first wiped the keys of the ______ at the beginning of the concert”), the cognate effect
disappeared and cognates were processed just as quickly as non-cognates. Since the target
words in both sentences were cognates, the BIA+ model predicts that the cognates in both
conditions should the same advantages of orthography and phonology overlap. Yet, only the
cognates in the low semantic restraint sentences showed this cognate processing advantage. The
BIA+ model must be able to account for how the semantics at a sentence level can affect word
recognition since these findings show that cognate words are processed differently in different
types of sentence contexts.
Since future research on the BIA+ model should investigate how other linguistic factors
in sentence contexts, such as syntax, affect lexical processing, this current study will investigate
lexical processing within sentence contexts constrained for semantics and syntax. Not much has
been done to investigate the role of syntax on word processing. Gullifer, Dussias, and Kroll
(2010) found preliminary evidence that a sentence’s syntax may affect the processing of
cognates, as semantic factors in the studies mentioned above affected the processing of cognate
target words. In sentence contexts that had Spanish-only syntax, such as sentences that had a
41
direct object clitic marker le before the verb and a null pro (two characteristics that are not
possible in English), there was no cognate effect: cognates were processed just as quickly as
matched non-cognates. However, in sentence contexts that had syntax that was allowable in
both languages, the researchers found a cognate effect where cognates were processed faster
than matched non-cognates. This finding suggests that the syntax of a sentence can also affect
word recognition. Thus, the BIA+ model must be able to explain how these different sentential
contexts can affect word processing.
Another future area of investigation for the BIA+ model (and also for the present study)
is how grammatical class affects lexical processing. Past studies (Sunderman & Kroll, 2006;
Campbell, 2009) have found grammatical class effects in comprehension tasks. It appears as
though grammatical class effects occur for unrelated semantic prime-target pairings. When the
two words are semantically related, the grammatical class effects disappear. Campbell (2009),
therefore, proposed an addition of a grammatical class node in the Identification system of the
BIA+ model. My study will investigate in more detail how grammatical class affects lexical
processing, particularly lexical processing within a sentence context. In sentence contexts, can
grammatical class effects be modulated by sentences with high semantics (as would be
suggested by the previous research on isolated lexical items) or is a sentence context with no
extra semantic constraint enough to also modulate grammatical class effects?
Sentence Constraints on Lexical Processing
When reading, words are not typically read in isolation; words appear in some type of
context, whether it be a simple sentence or in a larger context such as a paragraph. For the
purpose of this dissertation, I will be focusing on sentence context, since ultimately sentences
are the building blocks for larger contexts (e.g., paragraphs, discourse, etc). When considering
42
words processed within a sentence context, this leads to the questions: how does sentence
context affect lexical processing? Can sentence context modulate non-selectivity? In order to
comprehend sentences, readers combine meanings of individual words. This integration is
limited by a finite amount of computational resources. Instead of just reading the word “dog,” in
isolation, the processing becomes much more complex when reading the sentence, “The dog
chased the cat at the park.” Readers must integrate a variety of information sources, such as the
sentence’s syntax, semantics, and other linguistic factors. For example, in processing the
sentence above, “The dog chased the cat at the park,” the reader must be able to identify “the
dog” as the agent in the sentence, rather than “the cat,” which could be another possibility.
Since the reader is processing in English, the sentence’s word order helps the reader identify
“the dog” as the agent. Not all languages, however, have the same linguistic factors. The same
sentence in Spanish could begin with the direct object (e.g., Al gato persiguió el perro en el
parque “The catAcc chased the dogNom in the park.”). In this case, the reader would utilize the
direct object marker –a in front of the word gato “cat” to determine its thematic role. If the
reader used word order, the sentence would be interpreted incorrectly. Thus, sentence
comprehension is complex as it requires the integration of the meaning of multiple words within
a language specific linguistic structure.
In order to explain how sentences are comprehended, Gibson and Pearlmutter (1998)
describe four different types of constraint that affect sentence comprehension: lexical
constraints, contextual constraints, locality-based computational resource constraints, and
phrase-level contingent frequency constraints.
First, lexical constraints refer to knowledge of individual word-properties. One specific
word-property is a word’s grammatical class. Nouns, for instance, typically follow determiners
43
(i.e., articles, demonstratives, quantifiers, etc.) in English. Nouns are also marked
morphologically for number, having both a singular and plural form in English. Verbs, on the
other hand, are marked for tense, person, and number. Since words with different grammatical
classes are constrained in where they appear in the sentence and how they are marked, the
sentence processor must be sensitive to these word-property constraints in order to comprehend
at the sentence level.
The second type of constraint that affects sentence comprehension is context constraints.
This constraint refers to the plausibility of the sentence, especially with possibly ambiguous
constructions. Consider the sentence, “The man observed the woman with the purse.” This is an
ambiguous sentence construction; there are two possible interpretations. The first sentence
interpretation means that the man is using the purse to observe the woman and second
interpretation is that the man is observing the woman who is holding the purse. When the
construction is ambiguous, the more plausible interpretation will be preferred compared to an
implausible interpretation. In this case, therefore, the implausible interpretation is that the man
uses the purse to observe the woman. The plausible interpretation is that the man observes the
woman carrying the purse. Thus, the very context itself can constrain sentence comprehension.
The third type of constraint is locality-based computational resource constraints. As
mentioned earlier, the human mind has a finite amount of resources. The mind’s working
memory can only hold on to information for a short amount of time before it is forgotten. Due
to this constraint, there is a preference for interpreting sentence with local attachment. In
sentences that contain two nouns (N1 and N2) in a complex NP, and have a relative clause (RC)
(see example 1 from Fernández, 2002), N1 and N2 are both possible candidates to attach to the
RC.
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(1)
a. Andrew had dinner yesterday with the [N1 nephew] of the [N2 teacher] [RC
that was in the communist party]
b. Andrés cenó ayer con el [N1 sobrino] del [N2 maestro] [RC que estaba en el
partido comunista]
When answering the question Who was in the communist party?, two possible
interpretations are available: the nephew (N1) or the teacher (N2). The nephew, or N1, appears
in a higher site syntactically than the teacher (N2) according to a hierarchical framework
structure (Chomsky, 1995). Cuetos and Mitchell (1988) found that English-speakers tended to
choose the lower site, N2, to attach to the relative clause, which follows the constraint.
However, there may also be language-specific preferences that override the preference for local
attachment. For example monolingual Spanish-speakers tend to choose the higher site, N1,
contrary to the local attachment preference (Cuetos & Mitchell, 1988). Further research has
found that other languages such as German (Hemforth, Konieczny, & Scheepers, 2000), Dutch
(Brysbaert & Mitchell, 1996), French (Zagar, Pynte, & Rativeau, 1997) and Greek
(Papadopoulou & Clahsen, 2003) also have high attachment preferences. Thus, there is a
general preference for local attachment, but language-specific preferences sometimes override
this preference.
Finally, the fourth type of sentence constraint is phrase-level contingent frequency
constraints. This constraint states that syntactic constructions have their own frequencies that
may even override frequencies of individual words. Typically, syntactic constructions with
higher frequencies will be processed more quickly than lower frequent constructions. When a
sentence has an ambiguous construction, the frequency of the entire complementizer can affect
how the sentence is interpreted.
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A Model of Sentence Processing: Feature Restrictions Hypothesis
Both Van Hell and De Groot (2008) and Libben and Titone (2009) presented evidence
that certain sentence contexts can affect word recognition. Although the BIA+ proposes that
linguistic context can affect word recognition, it does not explicitly state why one type of
sentence context (a high semantic constraint sentence) can affect word recognition, but not
another type (low semantic constraint). The feature restrictions hypothesis (Schwanenflugel &
LaCount, 1988; Kellas, Paul, Martin, & Simpson, 1991) proposes that monolingual readers use
sentence contexts to generate different types of restrictions (semantic, syntactic, and lexical
feature) in order to assist in processing words in a sentence. For instance, consider the following
high constraint sentence in (2), where the target word is apple:
(2)
She took a bite of the fresh green ________.
When reading the sentence, the potential word for the blank is semantically restricted due to the
features [can be bitten], [fresh], and [green]. Only a few word possibilities match these features,
so the features may constrain what lexical items are pre-activated. If only a few lexical items are
pre-activated, bilinguals may have less lexical competitors to inhibit when accessing the target
word (assuming a connectionist model like the BIA+). When there are less target words to
inhibit, word recognition becomes faster. The feature restrictions hypothesis, therefore, is able
to help begin to explain why a high semantic constraint sentence can eliminate the cognate
effect in bilinguals, while a low semantic constraint sentence cannot.
Motivation for the Current Study
Because it is critical to our complete understanding of the interplay of word recognition
in sentence context and since we do not currently know how semantic and syntactic cues affect
bilingual word recognition, I propose the following experiments. The first experiment will
46
examine how semantic and syntactic constraint affect lexical processing as L2 proficiency
levels increase. The second experiment will test the same L2 learners and bilinguals, and
investigate how semantic constraints can affect grammatical class effects. These two
experiments will help show which linguistic cues in sentence context affect L2 learners and
proficient bilinguals when they are processing words. The findings from these experiments have
the potential to better inform the BIA+ model about how linguistic contexts at the sentential
level can affect word recognition at different levels of proficiency.
In the next chapter, I present a general overview of the experimental design for the two
experiments. I discuss the general methodology and specific aims of each experiment, as well. I
also give a general overview of the participants and how they were divided into their four
different groups. I finish by discussing materials and general procedure.
47
CHAPTER THREE
EXPERIMENTAL DESIGN: GENERAL CONSIDERATIONS
Overview of Experimental Approach
In order to investigate how sentence context affects word recognition, I conducted the
following experiments. The first experiment examined how semantic and syntactic constraint
can affect lexical processing with various levels of Spanish proficiency. The second experiment
tested the same L2 learners and bilinguals, and investigated how semantic constraint affects
grammatical class effects. These two experiments were conducted to help show which linguistic
cues in sentence context affect L2 learners and proficient bilinguals when they are processing
words. The findings from these experiments inform the BIA+ model about how linguistic
contexts at the sentential level can affect word recognition at different levels of proficiency.
Experiment 1 examined the way sentence context affects word processing by
modulating the constraint of semantics and syntax. Participants with varying levels of
proficiency completed a LDT primed with a sentence context. In this experiment, the target
words were cognates and matched controls in order to examine if a cognate facilitation effect
was found. The sentences were constrained for high/low semantics and high/low syntax.
Experiment 2 examined whether the same L2 learners and bilinguals showed
sensitivities to cues of grammatical class (a cue that combines semantics and syntactic
information) in sentence context. The sentence context in this experiment was manipulated for
high and low semantic constraint, and the targets were controlled for grammatical class
(matching or different from the grammatical class of the last word in the sentence) and semantic
similarity (high/low semantic similarity to the last word in the sentence context).
48
All participants completed the two experiments (with the exception of 17 participants
that completed Experiment 1, but not Experiment 2 when I piloted the study), a LDT in
isolation, which served as a control measure for Experiment 1, an online measure of
proficiency, and three offline measures of proficiency. The online measure of proficiency was a
Translation Recognition Task (see Appendix A for Translation Recognition Task materials)
where participants saw a word presented on a computer screen in Spanish followed by a word in
English. Participants were asked to indicate whether the two words were the correct translation
of one another. The offline measures of proficiency were a Language History Questionnaire
(see Appendix B for the questionnaire in English and Appendix C for the questionnaire in
Spanish) and two multiple choice activities adapted from the Diplomas of Spanish as a Foreign
Language (DELE) test (see Appendix D to see the two multiple choice activities). The two
multiple choice activities were created to test the structure used in high syntactic constraint
sentences in Experiment 1: direct object pronouns.
General Method
This section provides an overview of the multiple participant groups, the materials for
Experiment 1 and 2, and the general procedures for the experiments. Chapter 4 will give more
detailed information of Experiment 1, and similarly, Chapter 5 will go more in depth for
Experiment 2. This chapter serves to give a general framework of the methods used in both
experiments.
Participants
Experiments 1 and 2 had four different participant groups: a group of intermediate L2
Spanish learners (L1 English), a group of advanced L2 Spanish learners (L1 English), a group
of Spanish-English bilinguals, and a group of native Spanish speakers tested in Spain. The
49
groups of intermediate and advanced L2 Spanish learners and Spanish-English bilinguals were
students recruited from a university in the United States. The intermediate L2 Spanish learners
and advanced L2 Spanish learners were all native speakers of English. The intermediate L2
learners were enrolled in a 3000 level Spanish course, and the advanced L2 learners were
enrolled in Spanish or linguistics courses at the graduate-level. The advanced L2 learners were
also Spanish teaching assistants at the same university. The Spanish-English bilinguals were
native Spanish speakers that either grew up in a Spanish speaking country and later moved to
the United States or grew up as heritage learners, speaking Spanish in home in the United
States. Since all the Spanish-English bilinguals were currently enrolled in a university in the
United States, they were also highly proficient in English. Thus, the main differentiating factor
for the Spanish-English bilinguals was the fact that Spanish is their native language, and they
were currently living in the United States. The Spanish native-speaker control group were all
living in Spain and dominant in Spanish in their everyday life.
As part of the Language History Questionnaire, participants were asked to give selfratings for their two languages: English and Spanish. For each skill, participants rated their
ability on a scale from “1” (meaning lowest ability) to “10” (meaning highest ability). The skills
that participants rated were reading, writing, speaking, listening, and overall comfort in the
language. Table 3.1 shows all the participants’ overall mean self-ratings for English. The overall
mean self-ratings are very high as only one of the groups (the native Spanish speaking control
group) had much lower proficiency in English. The other three participants groups were native
speakers of English (with either intermediate or advanced proficiency in their L2 Spanish) or
Spanish-English bilinguals with high proficiency in their L2 English. The average for English
overall for all participants was 9.4 out of 10.
50
Table 3.1. Mean self-ratings on reading, writing, speaking, listening, and comfort in English.
Each scale rated from 1 (low ability) to 10 (high ability).
Reading
Writing
Speaking
Listening
Comfort
Average
9.4
9.2
9.4
9.5
9.4
9.4
Table 3.2 shows the mean self-ratings in Spanish for all of the participants. The overall
self-ratings are lower than in English (with the average overall rating being 7.6), which again
reflects the wider range of proficiency in Spanish by these participants. The lower self-ratings in
Spanish from the intermediate L2 learners affected the overall ratings.
Table 3.2. Mean self-ratings on reading, writing, speaking, listening, and comfort in Spanish.
Each scale rated from 1 (low ability) to 10 (high ability).
Reading
Writing
Speaking
Listening
Comfort
Average
7.8
7.4
7.3
8.1
7.3
7.6
Along with completing self-ratings in both English and Spanish, all participants beside
the native Spanish speaking control group, completed an online measure of proficiency: an
online Translation Recognition Task. The native Spanish speaking group did not complete this
online proficiency measure due to their presumed lack of proficiency in English. The materials
for the Translation Recognition Task were the filler items used in Sunderman and Kroll (2006).
In the Translation Recognition Task, participants were presented with a pair of Spanish-English
words on a computer screen and asked to answer whether the pair were the correct translation of
each other. Reaction times in milliseconds from the onset of the target word and accuracies
were recorded. It is predicted that participants with higher proficiency in both of their two
51
languages would respond more quickly and more accurately than those with less proficiency1.
Table 3.3 shows the overall mean for reaction times and percent accuracy for correct
translations for the Translation Recognition Task. The average response time was
approximately 730 milliseconds, while the average accuracy was 92.60%. The accuracy is very
high since all of the participants had intermediate, advanced, or native-like proficiency in
Spanish.
Table 3.3. Mean reaction times (ms) and percent accuracy for correct translations in
Translation Recognition Task
Correct Translations
Percent Accuracy
724.61
92.60%
One of the offline proficiency measures that participants completed was two multiple
choice activities. This measure tested participants’ ability to identify incorrect grammatical
structures and to produce correct vocabulary in specific sentence contexts. The offline measure
consisted of 25 multiple choice questions. Each question correct was given 1 point; therefore,
the highest number of points possible in this measure was 25. In Table 3.4, I present the overall
mean score and percent accuracy for the offline measure.
Table 3.4. Overall mean score (out of 25) and percent accuracy for offline multiple choice
proficiency measures.
Multiple Choice Responses
Percent Accuracy
81.92%
20.48
1
It is important to note that since the participants had different native languages, the Spanish-English bilinguals
may have had a more difficult time completing the translation recognition task since they were translating from the
L1 to the L2. Kroll and Stewart (1994) found that translating from the L1 to the L2 took significantly longer than it
did in the reverse direction.
52
Proficiency Groups
The four proficiency groups were determined based off their native and second
languages, self-ratings in the Language History Questionnaire, the class they were enrolled in
when they completed the experiments, the mean reaction times and accuracies in the Translation
Recognition Task, and the scores from the offline multiple choice. In Table 3.5, the number of
participants in each participant group is shown. The largest participant group is the intermediate
learners.
Table 3.5. Number of participants in each proficiency level.
Proficiency Level
n
Intermediate
61
Advanced
15
Spanish-English bilinguals
25
Native Spanish speakers
13
In Table 3.6, the average age (in years) of participants is shown based on their
participant group. The range for the average age was 19.95 years to 29.87 years. The group of
intermediate Spanish L2 learners was the youngest group as most of these L2 learners were
currently enrolled in undergraduate courses. The group of advanced L2 learners had the highest
mean age as they were all graduate students. The average age of the Spanish-English bilinguals
and native Spanish speakers were approximately the same, and this average age was between
the average age of the two L2 learners groups.
53
Table 3.6. Average age (in years) of participants by proficiency level.
Proficiency Level
Age
Intermediate
19.95
Advanced
29.87
Spanish-English bilinguals
24.12
Native Spanish speakers
24.77
Since the four participant groups have different proficiency levels in both English and
Spanish, I now present the self-rating scores for the same skills presented earlier, but divided up
per participant group. First, in Table 3.7, the mean self-ratings from Spanish are presented.
Table 3.7. Mean self-ratings on reading, writing, speaking, listening, and comfort of expression
in Spanish. Scale was from 1 (low ability) to 10 (high ability).
Proficiency
n
Reading
Writing
Speaking
Listening
Comfort
Intermediate
61
6.94
6.57
6.02
6.98
6.02
Advanced
15
8.53
8.33
7.87
8.80
8.57
Span-Eng bilinguals
25
8.8
8.16
9.28
9.40
8.76
Spanish speakers
13
9.38
9.46
9.31
9.85
9.62
As shown in Table 3.7, the intermediate L2 learners have the lowest self-ratings in Spanish.
This finding reflects that these participants have lower Spanish proficiency than the other three
participant groups. In fact, the intermediate L2 learners scored significantly lower than the other
three participant groups in all skills. Specifically, for Spanish reading self-ratings, there was a
significant difference between groups, F (3, 110) = 30.696, p < .001. Post hoc tests using the
54
Bonferroni correction revealed that the intermediate L2 learners scored significantly lower than
the other three participant groups (ps < .001). For the self-ratings on Spanish writing, there also
was a significant difference, F (3, 110) = 18.363, p < .001. Post hoc tests using the Bonferroni
correction showed that the intermediate L2 learners scored significantly lower than the other
three participant groups (ps < .001). There also was a significant difference in Spanish speaking,
F (3, 110) = 41.715, p < .001. Once again, the intermediate L2 learners rated themselves
significantly lower than the other three participant groups (ps < .001). The advanced L2 learners
also had significantly lower Spanish speaking self-ratings than the Spanish-English bilinguals (p
= .018). The difference between the advanced L2 learners and native Spanish speakers was
marginally significant (p = .054). For Spanish listening, the ratings were significantly different
across groups, F (3, 110) = 27.907, p < .001. The intermediate L2 learners rated themselves
significantly lower than the other three participant groups (ps < .001). Lastly, for overall selfratings in comfort in Spanish, there was a significant difference, F (3, 110) = 31.003, p < .001.
Again, the intermediate L2 learners rated themselves significantly lower than the other three
participant groups, (ps < .001). In summary, the intermediate L2 learners had significantly
lower self-ratings for all skills compared to the other three participant groups. In Spanish
speaking self-ratings, the advanced L2 learners rated themselves lower than the Spanish-English
bilinguals and native Spanish speakers, but in the other skills, there was no significant
difference between the advanced L2 learners and the other two native Spanish speaking groups.
These findings support that the advanced L2 learners considered themselves very proficient in
Spanish, even though it is not their native language.
In Table 3.8, I present the self-ratings in English for each participant group. The native
Spanish speakers did have some proficiency in English, but they had the lowest self-ratings
55
among the four groups. The other three participant groups were living in the United States and
had high proficiency in English.
Table 3.8. Mean self-ratings on reading, writing, speaking, listening, and comfort of expression
in English. Scale was from 1 (low ability) to 10 (high ability).
Proficiency
n
Reading
Writing
Speaking
Listening
Comfort
Intermediate
61
9.60
9.47
9.92
9.83
9.89
Advanced
15
9.87
9.87
10.00
10.00
9.93
Span-Eng bilinguals
25
9.40
9.20
9.36
9.44
9.28
Spanish speakers
13
7.69
7.00
6.54
7.38
6.54
The difference in the various English self-ratings between the four participant groups
were significant. Specifically, in English reading, the difference was significant, F (3, 110) =
28.021, p < .001. Post hoc tests using the Bonferroni correction showed that the native Spanish
speakers had significantly lower self-ratings in English reading than the other three participant
groups (ps < .001). A significant difference was also found for English writing, F (3, 110) =
26.915, p < .001. Once again, the post hoc tests using a Bonferroni correction revealed that the
native Spanish speakers had significantly lower English writing self-ratings than the other three
participant groups (ps < .001). The same pattern was found for the English speaking selfratings, where a significant difference was found, F (3, 110) = 65.674, and for English listening
self-ratings, F (3, 110) = 45.435. In both cases, the native Spanish speakers differed
significantly from the other three participant groups (ps < .001). The post hoc tests from the
English speaking self-ratings also revealed that the Spanish-English bilinguals scored
significantly lower than the intermediate L2 learners (p = .026). Finally, a significant difference
56
was also found for English comfort self-ratings, F (3, 110) = 70.294, p < .001. Post hoc tests
using the Bonferroni correction showed that the native Spanish speakers scored significantly
lower than the other three participant groups (ps < .001) and that the Spanish-English bilinguals
scored significantly lower than the intermediate L2 learners (p = .008). The difference between
the advanced L2 learners and Spanish-English bilinguals was marginally significant (p = .065)
with the Spanish-English bilinguals rating themselves lower than the advanced L2 learners in
English comfort. In summary, these results confirm that the native Spanish speakers rate
themselves lower in English than the other three groups. This result is not surprising
considering the native Spanish speakers were living in a Spanish speaking country at the time of
being tested and Spanish dominant. The other three participant groups were all tested in the
United States. In some skills, such as English speaking and overall comfort in English, the
Spanish-English bilinguals scored lower than the intermediate L2 learners and advanced L2
learners. This finding supports that the Spanish-English bilinguals have Spanish as their native
language and are less comfortable in English than the native English speakers.
To conclude with the self-ratings, I provide the overall mean self-ratings in both Spanish
and English for each participant group in Table 3.9. This table shows how the intermediate
Spanish L2 learners and native Spanish speakers were not balanced bilinguals like the advanced
L2 learners and Spanish-English bilingual groups. Both the advanced L2 learners and SpanishEnglish bilinguals rated themselves slightly higher in English than in Spanish. This finding is
not surprising for the advanced Spanish L2 learners, but may be for the Spanish-English
bilingual group. However, since the participants in the Spanish-English bilingual group
currently reside in the United States, this finding may suggest that although these participants
were once dominant in Spanish, they are now balanced bilinguals.
57
Table 3.9. Overall mean ratings for Spanish and English by proficiency group.
Proficiency
n
Spanish
English
Intermediate
61
6.51
9.74
Advanced
15
8.42
9.93
Span-Eng bilinguals
25
8.88
9.37
Spanish speakers
13
9.52
7.03
The difference in overall ratings between the four participant groups was significant for both
English, F (3, 110) = 62.883, p < .001, and Spanish, F (3, 110) = 43.854, p < .001. For English,
native Spanish speakers rated themselves overall significantly lower than the other three
participant groups (ps < .001). The Spanish-English bilinguals also rated themselves
significantly lower in English than the advanced L2 learners (p = .044). The difference between
the intermediate L2 learners and Spanish-English bilinguals was marginally significant (p =
.073), with the Spanish-English bilinguals rating themselves lower in English overall than the
intermediate L2 learners. For overall Spanish ratings, the intermediate L2 learners rated
themselves significantly lower than the other three participant groups (ps < .001). The
difference between the advanced L2 learners and native Spanish speakers overall in Spanish
was marginally significant (p = .095), with the advanced L2 learners ratings themselves lower
overall in Spanish. In summary, these findings support that the native Spanish speakers had less
proficiency in English than the other three groups, and that the intermediate L2 learners had
lower Spanish proficiency than the three other groups.
Next, I present the results from the Translation Recognition Task per participant group.
As mentioned earlier, the native Spanish speaking control group did not complete the
58
Translation Recognition Task as it was presumed that they did not have proficiency in English.
Table 3.10 shows the mean reaction times and accuracies for the correct translation pairs. The
intermediate Spanish L2 learners had the slowest reaction times and the lowest accuracy rates of
the three groups. One-way analyses of variance was conducted on the RT and accuracy data.
The results from the reaction time data was not significant, F (2, 98) = .31, p = .731. Thus,
although the intermediate L2 learners had slower reaction times than the advanced L2 learners
and Spanish-English bilinguals, this finding was not significant. The results from the accuracy
data was significant. The advanced L2 learners and Spanish-English bilinguals were
significantly more accurate than the intermediate L2 learners, F (2, 98) = 14.32, p < .001. Post
hoc comparisons using the Tukey HSD indicated that the mean accuracy for the intermediate L2
learners (M = 90.59, SD = 0.05) was significantly different than the advanced L2 learners (M =
96.13, SD = 0.02) and Spanish-English bilingual group (M = 95.40, SD = .04). However, the
advanced L2 learners and Spanish-English bilinguals were not significantly different. In
summary, the advanced L2 learners and Spanish-English bilinguals were both more accurate
than the intermediate L2 learners.
Table 3.10. Mean reaction times (ms) and percent accuracy for correct translations by
proficiency group.
Proficiency
n
Correct Translations
Intermediate
61
735.29 (91%)
Advanced
15
714.76 (96%)
Span-Eng bilinguals
25
704.46 (95%)
59
I next present the results from the offline multiple choice task per group. All four
participant groups completed this task. Table 3.11 shows the mean scores and percent accuracy
for each participant group. One-way analyses of variance was conducted on the mean scores.
The results from the mean scores show that there was a significant difference in the scores
between the intermediate L2 learners and the three other participant groups, F (3, 110) = 26.17,
p = .000. Post hoc comparisons using the Tukey HSD showed that the mean scores for the
intermediate L2 learners (M = 17.97) was significantly different than the advanced L2 learners
(M = 23.53), the Spanish-English bilinguals (M = 22.64), and the native Spanish speakers (M =
24.62). The scores between the advanced L2 learners, Spanish-English bilinguals, and native
Spanish speakers were not significant. Thus, the intermediate L2 learners scored significantly
lower on the offline multiple choice test than the other three participant groups. The advanced
L2 Spanish learners, therefore, scored similarly to the two native Spanish groups.
Table 3.11. Mean scores and percent accuracy for offline multiple choice task (out of 25).
n
Scores
Intermediate
61
17.97 (71.88%)
Advanced
15
23.53 (94.12%)
Span-Eng bilinguals
25
22.64 (90.56%)
Spanish speakers
13
24.62 (98.48%)
Materials
The materials included in this study were words and sentences in Spanish and English.
The specific details pertaining to each experiment will be explained in subsequent chapters.
60
Now I will describe general considerations that guided the formation of materials in each
experiment. First, each target word and sentence context was only presented once per
experiment. The materials were counterbalanced such that there were multiple versions of each
experiment, but only one presentation of each target and/or sentence context per participant.
Second, target words were always controlled for two factors: word length and word frequency.
Word length was calculated by counting the number of letters in the word. Word frequency was
calculated by using the Buscapalabras program (Davis & Perea, 2005). Since intermediate
learners most likely do not have the same input as native speakers or even more advanced
learners, special consideration was made to include words that were specifically studied in
lower level Spanish courses. Third, to control for semantic variations in sentence context and
target words, there were two independent surveys conducted where subjects at the same level as
the intermediate proficiency participants (but who did not participate in the various
experiments) judged both target words and sentence contexts for their semantic values.
In the next chapter I will discuss Experiment 1, including the specific details pertaining
to its materials and the reaction time and accuracy results.
61
CHAPTER FOUR
EXPERIMENT 1: COGNATE TARGETS AND SENTENCE
CONSTRAINT – LEXICAL DECISION TASK
Introduction
Experiment 1 was a lexical decision task with cognate targets. It was developed to
investigate whether bilinguals and L2 learners process words selectively or non-selectively
when reading sentence contexts manipulated for semantics and syntax. The questions under
investigation were: what sentential constraint (e.g., semantics or syntax) better yields
selectivity? Are L2 learners with different levels of proficiency sensitive to sentential constraint
in the same way? Are L2 learners processing words like Spanish-English bilinguals?
This experiment expanded on the findings from previous research. Van Hell and De
Groot (2008) and Schwartz and Kroll (2006) examined whether sentence context modulated for
semantics led to selective processing for bilinguals. Gullifer, Dussias, and Kroll (2010)
similarly investigated how sentence context affected word processing for bilinguals, but
constrained the sentence context for syntax. The current experiment takes the next step and
combines the factors of semantics and syntax and examines how these cues affect lexical
processing across different proficiency levels.
Method
Participants
The four participant groups in Chapter 3 completed Experiment 1. There were 61
intermediate L2 learners, 15 advanced L2 learners, 25 Spanish-English bilinguals, and 13 native
Spanish speakers. All of the participants, except for the native Spanish speaking control group,
62
were students at a university in the United States. The native Spanish speaking control group
were university students living and tested in Spain.
Stimuli
To test how sentence context constrains lexical processing, lexical decision tasks were
used. In Experiment 1, participants read a sentence presented on a computer screen that had the
target word missing. A blank space appeared in place of where the target word would appear.
See example (1) below:
(1) Yo leo la _____ en la biblioteca.
I read the _____ in the library.
In this example, the target word was the cognate novela (“novel”). The sentence appeared on the
screen for 4 seconds. After participants read the sentence, the sentence automatically
disappeared from the screen and a string of letters appeared. The string of letters was a nonword, target cognate, or matched control. In the example above, the word novela (“novel”)
would have appeared. Participants were asked to determine whether or not the string of letters is
an actual word in Spanish (L2 or L1, depending on participant group) as quickly and as
accurately as possible. Participants pressed the left button on a button box to indicate a “yes”
response and the right button to indicate a “no” response. Reaction times from the onset of the
target word and accuracies were recorded.
Semantic Constraint The sentences in Experiment 1 were created with high or low
semantic constraint (see Appendix F for the materials used in Experiment 2). A sentence with
high semantic constraint greatly restricts what the possible target word could be. A low
semantic constraint sentence, on the other hand, does not limit the possible target word to the
same degree. I will discuss how high and low semantic constraint were determined after giving
63
an overview of the materials. The following table provides examples of both a high and low
semantic constraint sentence:
Table 4.1. Sentence conditions based on semantic constraint.
High semantic constraint
Yo escucho al profesor en la clase.
“I listen to the professor in the class.”
Low semantic constraint
Yo veo al profesor en el supermercado.
“I see the professor in the supermarket.”
In both examples from Table 4.1, profesor “professor” is the cognate target word that would be
represented in the sentence with a blank line. In the high semantic constraint sentence, the verb
escuchar “to listen” restricts the meaning of the target word to something that is able to produce
sound. Likewise, the prepositional phrase en la clase “in the class” restricts the location to a
specific place. Thus, the feature restrictions hypothesis predicts that the target word would have
the features [can produce sound] and [in the classroom]. The low semantic constraint sentence,
however, has a target word with different restricting features. In the low semantic constraint
sentence, the target word profesor “professor” may have the features [can be seen] and [appears
in the supermarket]. These features are less restricting because all concrete nouns can be seen,
and there are many concrete objects that appear in the supermarket. Thus, the feature
restrictions hypothesis would predict that high semantic constraint sentences would generate a
smaller number of lexical items that could fit in the blank space, while low semantic constraint
sentences would generate more lexical items for the space. With fewer lexical items activated
from the high semantic condition, cognates and non-cognates may be processed similarly,
resulting in no cognate effect. This finding would suggest that the semantic cues in the sentence
64
are overriding any facilitation given by the cognate status of the target word. If a cognate
priming effect is found for the low semantic condition, this finding would suggest that a
bilingual’s two languages are both active, even when reading a sentence in a particular
language. The L2 proficiency of the bilingual may also affect processing of the cognate words.
If L2 learners do not show a cognate effect (especially for low semantic constraint sentences),
this may be due to their smaller L2 lexicon, as predicted by the RHM. L2 learners may not be
able to activate as many lexical representations as highly proficient bilinguals, thus resulting in
the lack of a cognate effect. However, if a cognate effect is found, L2 learners would be
showing evidence for cross-linguistic non-selectivity, even at low levels of proficiency.
Syntactic Constraint Experiment 1 also tested how the syntactic structure of sentences
affects lexical processing. In order to test how syntactic structures affect lexical processing, two
more sentence types were created. The sentences had high syntactic constraint and low syntactic
constraint. Following Gullifer et al. (2010), a sentence with high syntactic constraint had a word
order that is only permissible in Spanish, while a low syntactic constraint sentence had a word
order that was allowed in both English and Spanish. Recall that in Gullifer et al.’s study, the
specific sentence had an indirect object clitic and a null subject in the relative clause. These
syntactic structures were used as a cue to process selectively since they were only specific to
Spanish. For this study, the sentences were substantially shorter than the sentences in Gullifer et
al., due to the limited processing abilities of the intermediate L2 learners. Also, the sentences
with high syntactic constraint varied slightly. The sentences with Spanish-specific syntax began
with a direct object and were immediately following by the direct object clitic, such as lo/la
“him/her/it.” This syntactic construction is very marked, even to native Spanish speakers. Thus,
it was chosen because it serves as a salient cue that the syntax is specific to Spanish. The second
65
Spanish-syntax structure that was used was also used in Gullifer et al.: a null subject. Spanish is
a null-subject language, unlike English. A sentence containing a null-subject element or pro
would never be allowed in English. In Gullifer et al.’s study, there was a null subject for the
verb in the relative clause, while in the current study there was a null subject in the main clause.
Again, this difference occurred due to the shorter sentences in the present study.
Consider the example that was given above for the semantic constraint conditions. The
sentence Yo escucho al profesor en la clase “I listen to the professor in the class,” has low
syntactic constraint because the syntax is not specific to one particular language. A sentence
such as Al profesor lo [pro] escucho en la clase or literally, “The professor him [pro] I listen to
in the class,” is highly constrained syntactically. Table 4.2 shows the four different sentence
types that the cognate priming experiment uses.
Table 4.2. Sentence conditions with semantic and syntactic constraint.
High semantic constraint
Low semantic constraint
High syntactic
constraint
Al profesor lo escucho en la clase.
“The professor him I listen to in the
class.”
Al profesor lo veo en el
supermercado.
“The professor him I see in the
supermarket.”
Low syntactic
constraint
Yo escucho al profesor en la clase.
“I listen to the professor in the
class.”
Yo veo al profesor en el
supermercado.
“I see the professor in the
supermarket.”
These Spanish-specific syntactic elements were also chosen because they appear early in
the sentence, thus giving participants an early cue to process in Spanish. Other elements, such as
postverbal subjects, are Spanish specific but were not chosen due to their appearance later in
sentences. If the cognate priming effect is found, this finding would suggest that the syntactic
66
structure does not override the facilitation caused by the cognate status of the target word.
However, if a cognate priming effect is not found for targets read after sentences with a high
syntactic constraint, then it is possible that the syntactic cue in the sentence context is overriding
the facilitation of the processing of the cognate word.
By developing the four sentence conditions, semantics and syntax are put in direct
competition with one another. The results may help show how the semantics and syntax of a
sentence context can affect processing of individual lexical items. The experiment shows how
semantic and syntactic constraints affect less proficient L2 learners, as well. Perhaps L2 learners
are more sensitive to semantic cues than syntactic ones as previous ERP data on sentence
processing has shown that L2 learners are sensitive to semantic anomalies in their L2, but not
syntactic ones (Hahne, 2001; Weber-Fox & Neville, 1996). It is also possible that L2 learners
will not be sensitive to syntactic cues if they process the direct object lo “him” as the subject, as
per VanPatten’s (2004) First Noun Principle. In this case, they may not process the syntax as
specifically Spanish, which could result in a cognate effect even for high syntactic constraint
sentences (assuming a cognate effect is found for low syntactic constraint sentences, as well).
The results from this study will help show if L2 learners are sensitive to semantic and syntactic
cues as they develop in L2 proficiency.
Norming the Stimuli The first process in creating materials was to norm the targets and
filler words. There were 28 target cognates, 28 matched controls, and 56 non-word fillers used
in Experiment 1. Table 4.3 shows the mean word length and mean word frequency per million
in Spanish (Davis & Perea, 2005) for the target cognates and matched controls. No significant
differences were found between conditions for mean word length, t (27) = .113, p = .911, and
mean word frequency, t (27) = 1.072, p = .293. The 56 non-words were created by taking
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Spanish words and changing one letter in the word to create a non-word. The non-words still
followed the phonotactic constraints of Spanish and were verified by a native Spanish speaker
to not be actual Spanish words. The mean word length of the 56 non-words was 7.18. The
results from a one-way, three-level ANOVA yielded no significant difference between the word
length of the cognates, matched controls, and filler words, F (2, 109) = .074, p = .929. The mean
word frequency was not calculated from the non-words, since by the nature of not beings words,
these fillers did not have word frequency.
Table 4.3. Mean word length (number of letters) and mean word frequency per million in
Spanish (Davis & Perea, 2005) for targets and matched controls.
Cognates
Matched Controls
Word Length
6.75
6.61
Frequency
50.36
50.08
Next, the sentence context was normed for high and low semantics. In order to
determine whether a sentence had high or low semantics, a survey was presented to 36
participants in a 3000 level Spanish course (the same level as the intermediate L2 learners) who
did not take Experiment 1. Participants were asked to complete a survey where they rated the
target word with elements from the low or high sentence context. For example, consider the
high and low semantic contexts for the target cognate “novela.”
(2) Yo leo la novela en la biblioteca. (high semantic constraint)
I read the novel in the library.
(3) Yo vendo la novela en la calle.
(low semantic constraint)
I sell the novel on the street.
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In the survey (found in Appendix E), participants were asked to rate how similar “novela”
(novel) was to the verbs in the sentence (e.g., “leer” to read, “vender” to sell) and the
meaningful content in the prepositional phrase (e.g., “biblioteca” library, “calle” street). The
participants rated the similarity between the target and the various words on a 7 point scale. A
score of 7 indicated that the words were very similar in meaning, a score of 4 meant that the
words were somewhat similar in meaning, and a score of 1 meant that the words were not
similar at all in meaning. As shown in Table 4.4, the average mean for the elements from the
high semantic constraint sentences was 4.01 and this rating was higher than the average rating
for the low semantic constraint sentences: 2.18. The difference between the two conditions was
significant, t (111) = 14.62, p < .001.
Table 4.4. Mean similarity rating (on scale of 1 - 7) between targets and elements from sentence
contexts where 1 indicates “no similarity in meaning” and 7 indicates “very similar in
meaning.”
High semantic constraint sentences
Low semantic constraint
sentences
Similarity rating
4.01
2.18
Design
As shown in Table 4.5, there were 4 different critical sentence conditions (high/low
semantics and high/low syntax) and each condition had 14 trials (7 cognates and 7 matched
controls), thus creating 56 critical items. Half of the 56 targets were cognates and the other half
were matched controls. All of these critical items elicited a “yes” response since all of the items
were words in Spanish. There were also 56 filler items that elicited a “no” response. The
sentences for these items mirrored the syntactic structure of the critical items: half were high
69
syntactic and half were low syntactic. Since the filler items were not real words, the sentences
were not created with high or low semantic constraint. In total, participants read 112 sentences
and responded to 56 words and 56 non-words.
Table 4.5. Illustration of design of materials for Experiment 1.
High semantic constraint
Low semantic constraint
Fillers
High syntactic
constraint
7 cognates
7 matched controls
7 cognates
7 matched controls
28 non-words
Low syntactic
constraint
7 cognates
7 matched controls
7 cognates
7 matched controls
28 non-words
Procedure
Participants were tested in a language laboratory on campus at an American university,
except for the native Spanish speakers who were tested in a computer laboratory in a university
center in Spain. All participants completed Experiment 1 on a computer using the software
SuperLab. Participants were given instructions by the researcher in English in the United States
and in Spanish in Spain. Participants also read instructions in English (United States) or Spanish
(Spain) on the computer screen that an asterisk would appear on the screen for 1 second. Next, a
Spanish sentence that had a blank word in the sentence would appear on the screen for 4
seconds. After 4 seconds, the sentence would automatically disappear from the screen and a
string of letters would appear. Participants were told to answer as quickly and accurately
whether those letters formed a word in Spanish. To indicate a “yes” response, participants
pressed the left button on a button box, and to indicated a “no” response, participants pressed
the right button of the same button box. After their response, the string of letters disappeared
and another asterisk appeared before the next sentence. Participants had 10 practice trials in
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order to become familiar with the procedure of the online task. The researcher encouraged
participants to ask any questions during this time period if they had any. Reaction time in
milliseconds from the onset of the target word and percent accuracies were recorded by the
computer software. SuperLab also randomized the order of the trials for each participant.
Results
Data Analysis
Critical items (items with a “yes” response) with a correct response were used in the
reaction time analysis. For the accuracy data, both correct and incorrect responses for the
critical items were included. For the native Spanish speaking group, the advanced L2 learner
group, and the Spanish-English bilingual group, the reaction times that were faster than 200ms
or slower than 3000ms were deleted since they were considered outliers. Since the intermediate
L2 learners were less proficient in Spanish, only reaction times above 5000ms were excluded as
outliers. As with the other groups, reaction times faster than 200ms were eliminated from the
data set for the intermediate L2 learners. Means for each condition were calculated for both
target cognates and matched controls for each participant. Next, standard deviations were found
for each participant’s mean. Reaction times outside 2.5 standard deviations of the participant’s
means were excluded from the data, as well. The data was cleaned up in this way in order to
ensure that exceptionally fast and slow responses that may not reflect true lexical processing
would not be included in the analysis. First, I will present reaction time and accuracy data from
the native Spanish speakers, followed by the intermediate L2 learners, advanced L2 learners,
and Spanish-English bilinguals. I will discuss significant and marginally significant effects. I
finish by summarizing all the findings and discussing their implications to lexical processing in
sentence context.
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Native Spanish Speakers Results
Below I present the response time and accuracy results from the critical items for the 13
native Spanish speaking participants. First, I discuss the results from the reaction time data and
then I discuss the results from the accuracy data. Table 4.6 shows the mean reaction times (ms)
and percent accuracies for the native Spanish speaking group.
Table 4.6. Mean RTs (ms) and percent accuracy for Experiment 1 for native Spanish speaking
participants.
Semantic constraint
Syntactic constraint
+
-
+
-
Cognate:
904 (99%)
Cognate:
931 (97%)
Non-cognate:
909 (99%)
Non-cognate:
909 (97%)
Cognate:
854 (100%)
Cognate:
897 (97%)
Non-cognate:
907 (99%)
Non-cognate:
937 (94%)
A repeated measures ANOVA with three within-group factors was conducted to analyze the
reaction times. The three factors were cognate status (cognate vs. non-cognate), semantic
constraint (high vs. low semantics), and syntactic constraint (high vs. low syntax). With respect
to cognate status, no main effect was found, F (1, 12) = .620, p = .446. As Table 4.7 shows,
cognates (e.g., piano) were processed slightly faster than the non-cognates (e.g., reloj “clock”).
However, this difference was not significant. There was no main effect found for semantic
constraint, F (1, 12) = .844, p = .376. No significant interaction was found between cognate
status and semantic constraint, F (1, 12) = .358, p = .561. There was also no main effect found
for syntactic constraint, F (1, 12) = .396, p = .541. No significant interaction was found between
cognate status and syntactic constraint, F (1, 12) = .744, p = .405. Finally, no significant
72
interaction was found between cognate status, semantic constraint, and syntactic constraint, F
(1, 12) = .034, p = .857.
Table 4.7. Mean RTs (ms) and percent accuracy for cognates and matched controls for native
Spanish speaking participants.
Cognates
Non-cognates
897
915
Overall, no main effects were found for the native Spanish speaking control group. The
sentence context did not affect the processing of cognates and non-cognates. As such, no
interactions were found between any conditions.
A repeated measures ANOVA with the same three within-group factors was conducted
to analyze the accuracy results from the native Spanish speakers. No significant main effects or
interactions were found. There was no main effect found cognate status, F (1, 13) = 1.064, p =
.323, nor for semantic constraint, F (1, 13) = 2.439, p = .144, nor for syntactic constraint, F (1,
13) = .122, p = .733. No significant interaction was found between cognate status and
semantics, F (1, 13) = .103, p = .753. No significant interaction was found between cognate
status and syntax, F (1, 13) = .921, p = .356. There was no significant interaction between
cognate status, semantic constraint, and syntactic constraint, F (1, 13) = .319, p = .582. Overall,
the accuracies were very high, as shown in Table 4.5. The lowest percent accuracy was found in
the condition with non-cognate targets, low semantic constraint, and low syntactic constraint.
Thus, there was nothing about the sentence context that was pushing the participant toward the
target word. For this reason, this condition had the lowest accuracy percentage.
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Intermediate L2 Learners Results
Next, I report the reaction time results from the 61 intermediate L2 learners. As with the
native Spanish speakers group, a repeated measures ANOVA with three within-group factors
(cognate status, semantic constraint, syntactic constraint) was conducted on the response times
for the intermediate L2 learners. Table 4.8 shows the mean reaction times (ms) and percent
accuracies from the critical trials for cognates and non-cognates in the different conditions.
Table 4.8. Mean RTs (ms) and percent accuracy for Experiment 1 for intermediate L2 learners.
Semantic constraint
Syntactic constraint
+
-
+
-
Cognate:
1200 (97%)
Cognate:
1291 (95%)
Non-cognate:
1275 (91%)
Non-cognate:
1431 (88%)
Cognate:
1127 (98%)
Cognate:
1289 (94%)
Non-cognate:
1291 (90%)
Non-cognate:
1367 (87%)
A significant main effect for cognate status was found, F (1, 60) = 23.97, p < .001, such that
cognates were processed more quickly than non-cognates. Table 4.9 shows the average mean
reaction times (ms) for cognates and non-cognates across all conditions. These results from the
intermediate L2 learners provide evidence of the cognate facilitation effect.
Table 4.9. Mean RTs (ms) and percent accuracy for cognates and matched controls for
intermediate L2 learners.
Cognates
Non-cognates
1227
1341
A significant main effect was also found for semantic constraint, F (1, 60) = 40.01, p < .001.
Cognates and non-cognates were both processed more quickly after high semantic constraint
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sentences than low semantic constraint sentences. No significant interaction was found between
cognate status and semantic constraint, F (1, 60) = .026, p = .874. No significant effect was
found for syntactic constraint, F (1, 60) = 2.907, p = .093. No interaction was found between
cognate status and syntactic constraint, F (1, 60) = .033, p = .857. No interaction was found
between cognate status, semantic constraint, and syntactic constraint, F (1, 60) = 2.28, p = .137.
Thus, for the intermediate L2 learners, it appears that high semantic constraint facilitates
processing, high syntactic constraint does not affect processing, and neither sentential cue was
able to eliminate the cognate facilitation effect. In all conditions, even the high semantic/high
syntactic constraint sentences, these intermediate L2 learners are processing cognates more
quickly than non-cognates.
A repeated measures ANOVA with the same three within-group factors was conducted
to analyze the accuracy results from the intermediate L2 learners. The results mirror the findings
from the response time data. A significant main effect was found for cognate status, F (1, 60) =
68.84, p <.001. Participants had higher accuracies for cognates than for non-cognates. A
significant main effect was also found for semantics, F (1, 60) = 6.272, p = .015. Participants
were more accurate with target words following high semantic constraint sentences than low
semantic constraint sentences. No interaction was found between cognate status and semantic
constraint, F (1, 60) = .005, p = .944. No significant effect was found syntactic constraint, F (1,
60) = .556, p = .459. No interaction was found between cognate status and syntactic constraint,
F (1, 60) = 1.003, p = .321. Finally, no interaction was found between cognate status, semantic
constraint, and syntactic constraint, F (1, 60) = .086, p = .770. In summary, the accuracy results
follow the same pattern that the intermediate L2 learners showed in their response time data.
The intermediate L2 learners were slower and less accurate with non-cognates and low semantic
75
constraint sentences. Like the response time data, the accuracy data did not show any significant
differences between high and low syntactic constraints.
Advanced L2 Learners Results
Next, I present the reaction time data results from the 15 advanced L2 learners. A
repeated measures ANOVA with three within-group factors (cognate status, semantic
constraint, syntactic constraint) was conducted on the response times for the advanced L2
learners. Table 4.10 shows the mean reaction times (ms) and percent accuracies for the critical
items for all conditions.
Table 4.10. Mean RTs (ms) and percent accuracy for Experiment 1 for advanced L2 learners.
Semantic constraint
Syntactic constraint
+
-
+
-
Cognate:
1030 (100%)
Cognate:
1123 (100%)
Non-cognate:
1081 (98%)
Non-cognate:
1092 (98%)
Cognate:
993 (100%)
Cognate:
1112 (100%)
Non-cognate:
1040 (100%)
Non-cognate:
1105 (100%)
No significant effect for cognate status was found, F (1, 14) = .822, p = .380. As shown in Table
4.11, the difference in processing was only 15ms between cognates and non-cognates.
Table 4.11. Mean RTs (ms) and percent accuracy for cognates and matched controls for
advanced L2 learners.
Cognates
Non-cognates
1065
1080
A significant main effect was found for semantic constraint, F (1, 14) = 21.380, p < .001, such
that targets processed after high semantic constraint sentences were processed more quickly
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than targets after low semantic constraint sentences. No interaction was found between cognate
status and semantic constraint, F (1, 14) = 1.942, p = .185. No main effect was found for
syntactic constraint, F (1, 14) = 1.267, p = .279. No interaction was found between cognate
status, semantic constraint, and syntactic constraint, F (1, 14) = .124, p = .729. Overall, the
advanced L2 learners do not show evidence of the cognate facilitation effect. The cognate and
non-cognate response times were nearly identical. However, semantic constraint affects
processing for these advanced L2 learners. As with the intermediate L2 learners, high semantic
constraint appears to facilitate processing. Like the intermediate L2 learners, the advanced L2
learners did not show processing differences between high and low syntactic constraints.
Next, I present the accuracy results. As mentioned above, the percent accuracies for
critical items in all conditions are shown in Table 4.9. Overall, the percent accuracies are
extremely high. The advanced L2 learners scored 100% in all but two conditions. Even in the
two conditions where participants did not score 100%, they scored 98%. A repeated measures
ANOVA with three factors (cognate status, semantic constraint, syntactic constraint) yielded no
significant effects. No significant effect was found for cognate status, F (1, 14) = 3.330, p <
.089, for semantic constraint, F (1, 14) = .000, p = 1.000, nor for syntax, F (1, 14) = 3.330, p <
.089. No interaction was found between cognate status and syntactic constraint, F (1, 14) =
3.330, p < .089. No interaction was found between cognate status, semantic constraint, and
syntactic constraint, F (1, 14) = .000, p = 1.000. In summary, the overall accuracy was very high
for all conditions, and no significant effects or interactions were found.
Spanish-English Bilinguals Results
Finally, I will report the reaction time results for the 25 Spanish-English bilinguals. A
repeated measures ANOVA with three within-group factors (cognate status, semantic
77
constraint, syntactic constraint) was conducted on the response times for the Spanish-English
bilinguals. Table 4.12 shows the mean reaction times (ms) and percent accuracies for the critical
items for all conditions.
Table 4.12. Mean RTs (ms) and percent accuracy for Experiment 1 for Spanish-English
bilinguals.
Semantic constraint
Syntactic constraint
+
-
+
-
Cognate:
1067 (99%)
Cognate:
1172 (98%)
Non-cognate:
1109 (99%)
Non-cognate: 1166 (98%)
Cognate:
1053 (99%)
Cognate:
Non-cognate:
1066 (97%)
Non-cognate: 1176 (96%)
1088 (99%)
No significant effect was found for cognate status, F (1, 24) = 2.107, p = .160. As shown in
Table 4.13, the difference in processing was 34ms, but this difference was not significant.
Table 4.13. Mean RTs (ms) and percent accuracy for cognates and matched controls for
Spanish-English bilinguals.
Cognates
Non-cognates
1095
1129
A significant main effect was found for semantic constraint, F (1, 24) = 27.019, p < .001. The
targets processed after high semantic constraint sentences were processed more quickly than the
targets after low semantic constraint sentences. No interaction between cognate status and
semantic constraint was found, F (1, 24) = .105, p = .748. No significant effect for syntactic
constraint was found, F (1, 24) = 3.307, p < .081. No interaction was found between cognate
78
status and syntactic constraint, F (1, 24) = .788, p = .384. No interaction was found between
cognate status, semantic constraint, and syntactic constraint, F (1, 24) = 1.748, p = .199.
A repeated measures ANOVA with the same three within-group factors was conducted
to analyze the accuracy results from the Spanish-English bilinguals. No significant main effects
or interactions were found. There was no significant main effect found for cognate status, F (1,
24) = 2.087, p = .161. There was also no significant main effect for semantic constraint, F (1,
24) = .435, p = .516 nor for syntactic constraint, F (1, 24) = .220, p = .643. No significant
interaction was found between cognate status and semantic constraint, F (1, 24) = .000, p =
1.000, nor between cognate status and syntactic constraint, F (1, 24) = 2.906, p = .101. Finally,
no significant interaction was found between cognate status, semantic constraint, and syntactic
constraint, F (1, 24) = .000, p = 1.000.
Overall Results
After finding the pattern of processing for each individual proficiency group, I next
examined the data to compare the overall results between groups in order to investigate whether
these different patterns changed at different levels of development. A repeated measures
ANOVA with three within-subjects factors, semantic constraint (high, low), syntactic constraint
(high, low), cognate status (cognate, non-cognate) and one between-subjects factor, proficiency
group (intermediate, advanced, Spanish-English bilingual, native Spanish speaker) was
performed on the response times. A main effect for proficiency group was found, F (3, 110) =
7.997, p < .001. Post hoc tests using the Bonferroni correction revealed that the intermediate L2
learners processed significantly more slowly than the native Spanish speakers (p < .001), and it
also showed that the intermediate L2 learners processed marginally slower than the SpanishEnglish bilinguals (p = .068). For the purpose of this archival dissertation, I define marginal to
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mean p falls between .055 - .07. By doing so, this allows me to further explore potential patterns
to investigate factors that may affect processing. A main effect for semantic constraint was
found, F (1, 110) = 30.792, p < .001. Targets were processed faster after high semantic
constraint semantics than after low semantic constraint sentences. A significant interaction was
found between semantic constraint and proficiency group, F (3, 110) = 3.182, p = .027. While
the targets following high semantic constraint sentences were processed faster for each
proficiency group, the native Spanish speakers only had a difference of 25ms between high and
low semantic constraint conditions, while the other three groups had a difference of at least
70ms between conditions. For syntactic constraint, no significant effect was found, F (1, 110) =
2.992, p = .086. No significant interaction was found between syntactic constraint and
proficiency group, F (3, 110) = .118, p = .949. With regard to cognate status, a main effect was
found, F (1, 110) = 6.183, p = .014. Cognate targets were processed more quickly than the
matched non-cognates. A significant interaction was found between cognate status and
proficiency group, F (3, 110) = 3.133, p = .029. The difference in processing between cognate
and non-cognate targets was much greater for intermediate L2 learners than the other three
proficiency groups. No other significant interactions were found in the response time data.
Since the native Spanish speakers were used as a control group for the data, I also
analyzed the response times between the three other participant groups. A repeated measures
ANOVA with the same three within-subjects factors and one between-subjects factor,
proficiency group (intermediate L2 learners, advanced L2 learners, Spanish-English bilinguals)
was performed on the response times. A main effect for proficiency group was still found, F (2,
98) = 4.522, p = .013. Post hoc tests using the Bonferroni correction revealed that the
intermediate L2 learners had significantly slower reaction times than the Spanish-English
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bilinguals (p = .037). All the other findings mirrored what was found when all four participant
groups were compared. The only difference was that when the native Spanish speakers were
removed from the data, no significant interaction was found between semantic constraint and
proficiency group, F (2, 98) = 2.142, p = .123. This finding supports that high semantic
constraint facilitated processing overall for the intermediate L2 learners, advanced L2 learners,
and Spanish-English bilinguals.
A repeated measures ANOVA with the same three within-subjects factors and one
between-subjects factor was also performed on the accuracy results. A main effect for
proficiency group was found, F (3, 110) = 16.093, p < .001. Post hoc tests using the Bonferroni
correction showed that the intermediate L2 learners were less accurate than the other three
proficiency groups (ps < .002). A marginally significant effect was found for semantic
constraint, F (1, 110) = 3.752, p = .055. Targets following high semantic constraint sentences
were more accurate than targets following low semantic constraint sentences. No interaction
was found between semantic constraint and proficiency group, F (3, 110) = 1.065, p = .367. No
main effect was found for syntactic constraint, F (1, 110) = .072, p = .789, and no interaction
was found between syntactic constraint and proficiency group, F (3, 110) = .309, p = .819. A
main effect was found for cognate status, F (1, 110) = 16.201, p < .001. Overall, all participants
were more accurate for cognates than non-cognates. A significant interaction between cognate
status and proficiency group was also found, F (3, 110) = 11.155, p < .001. The intermediate L2
learners had lower accuracy processing targets after non-cognates than the other three groups.
No other significant interactions were found.
Once again, the accuracy data was also examined after removing the native Spanish
speaking control group data. A main effect for proficiency group was still found, F (2, 98) =
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20.820, p < .001. Post hoc tests using the Bonferroni corrected showed that the intermediate L2
learners had significantly lower accuracy than the advanced L2 learners and Spanish-English
bilinguals (ps < .001). Unlike the analyses that included the results from the native Spanish
speakers, no significant effect was found for semantic constraint, F (1, 98) = 1.774, p = .186.
All other findings matched the findings from when all four participant groups were analyzed,
including a significant effect for cognate status, F (1, 98) = 20.335, p < .001, and a significant
interaction between cognate status and proficiency group, F (2, 98) = 12.808, p < .001.
Summary
In summary, the previous results examined the lexical processing in sentence context for
four different participant groups with various proficiency levels in English and Spanish. The
processing patterns of the native Spanish speakers, advanced L2 learners, and Spanish-English
bilinguals were similar, while the intermediate L2 learners had distinctly different processing
patterns. The intermediate L2 learners group was the only group to significantly process
cognates more quickly than non-cognates, thus showing evidence of having the cognate
facilitation effect. However, this cognate facilitation effect was present in all the different
sentential conditions, even those with high semantic and high syntactic constraint. The other
three groups did not show evidence of the cognate facilitation effect in any condition. Thus,
these participants had no differences in processing cognates and non-cognates. The accuracy
data from the intermediate L2 learners showed that they more accurately processed cognates
than non-cognates. The other three proficiency groups did not process cognates or non-cognates
more accurately. No significant interactions for response time or accuracy results were found.
With regards to semantic constraint, all groups except the native Spanish speaking group
showed facilitation for targets after high semantic constraint sentences in their response time
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results. The native Spanish speaking group showed no significant effect for semantic constraint
in their response times. For the accuracy data results, however, only the intermediate L2
learners yielded a significant main effect for semantic constraint. The intermediate L2 learners
were more accurate with targets following high semantic constraint sentences than low semantic
constraint sentences. The advanced L2 learners and Spanish-English bilinguals had no main
effect for semantic constraint for their accuracy results. No significant interactions between
semantic constraint and the variables of syntactic constraint and cognate status were found for
response time or accuracy results.
For the syntactic constraint response time results, the native Spanish speakers,
intermediate L2 learners, advanced L2 learners, and Spanish-English bilinguals all had no
significant main effects. It appears that syntax neither facilitated nor interfered with the
response time latencies. Like the response time results, the accuracy data showed no main
effects for all participant groups. Therefore, unlike semantics, syntax does not facilitate nor
interfere with the processing of the cognate and non-cognate targets.
Materials Control Task Results
As a follow-up to Experiment 1, participants also completed a LDT in isolation using
the same target cognates and matched-controls. Findings from bilingual word recognition
suggest that L2 learners and bilinguals show evidence of cognate facilitation when completing
word recognition tasks. Since the assumption was that the participants would process cognates
more quickly than matched non-cognates, this follow-up activity was included to confirm the
assumption. Since only the intermediate L2 learners had a main effect for cognate status, it was
even more crucial to be able to provide evidence that the other participants’ results was due to
their processing and not due to the materials used in this task.
83
In order to verify that a cognate facilitation would be found in isolation, each participant
group completed a LDT for the same cognates, non-cognates, and fillers from Experiment 1.
The difference in procedure from the LDT in isolation and Experiment 1 is that the LDT in
isolation did not have sentence contexts. Instead, strings of letters would appear on the screen
one by one, and participants were asked to indicate whether the strings of letters were Spanish
words as fast and as accurately as possible. Participants used the same buttons to indicate their
“yes” and “no” responses as they did in Experiment 1. Next, I present the results from this LDT
in isolation for each participant group.
The intermediate L2 learners showed evidence of a cognate facilitation effect with the
response times from the targets in isolation. The results from a paired samples T-test yielded a
significant effect for cognate status, t (60) = 10.194, p < .001. The mean reaction time for
cognates was 709ms and for non-cognates was 796ms. The mean accuracy for cognates was
99% and the mean accuracy for non-cognates was 92%. The results from a paired samples Ttest showed that there was a significant effect for cognate status in the accuracy results, t (60) =
8.465, p < .001. The cognates were processed more accurately than the non-cognates.
The advanced L2 learners also showed evidence of a cognate facilitation effect in
isolation. The results from a paired samples T-test showed a significant difference between
cognates and non-cognates, t (14) = 2.867, p = .012. The mean reaction time for cognates was
705ms, while the mean reaction time for non-cognates was 729ms. Thus, advanced L2 learners
processed cognates more quickly than non-cognates. There was no significant difference in the
accuracy data between cognates and non-cognates, t (14) = .000, p = 1.000. The percent correct
for cognates was 99.5%, and the percent correct for non-cognates was also 99.5%.
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The results from the paired samples T-test for the Spanish-English bilinguals showed a
significant effect for cognate status, t (24) = -4.211, p < .001. The cognates (732ms) were
processed faster than the non-cognates (797ms). The accuracy data also yielded a significant
main effect for cognate status, t (24) = 2.191, p = .038, such that cognates had a higher accuracy
(99%) than non-cognates (98%).
The native Spanish speakers did not have a cognate facilitation effect for the response
times in the LDT in isolation. There was only an 8ms difference between the response times for
cognates (614ms) and non-cognates (622ms). The results from the paired samples T-test
showed no significant effect for cognate status, t (12) = -1.054, p = .312. The accuracy data also
did not show a cognate facilitation effect. The percent accurate for the cognates was 99%, while
the percent correct for the non-cognates was also 99%. The results from a paired samples T-test
showed that there was no significant effect for cognate status in the accuracy data, t (12) = .562, p = .584.
The overall results of the response times were tested using a repeated measures ANOVA
with one within-subjects factor, cognate status (high, low) and one between-subjects factor,
proficiency group (intermediate, advanced, Spanish-English bilingual, native Spanish speaker).
A main effect for group was found, F (3, 110) = 3.539, p = .017. Post hoc tests using the
Bonferroni correction showed that the intermediate L2 learners processed significantly slower
than the native Spanish speakers (p = .017). Furthermore, the post hoc tests using the Bonferroni
correction also revealed that the Spanish-English bilinguals processed significantly slower than
the native Spanish speakers (p = .021). A main effect for cognate status was found, F (1, 110) =
45.976, p < .001. Overall, all participants were faster to process cognates than non-cognates. A
significant interaction was found between cognate status and proficiency group, F (3, 110) =
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7.627, p < .001. The native Spanish speakers processed cognates only 8ms faster than noncognates, while the other three proficiency groups had larger differences between cognates and
non-cognates.
Next, I examined the overall response time results by excluding the data from the native
Spanish speakers. A repeated measures ANOVA with one within-subjects factor, cognate status
(high, low) and one between-subjects factor, proficiency group (intermediate, advanced,
Spanish-English bilingual) was performed on the response time data. When excluding the native
Spanish speaker data, there was no significant main effect for group, F (2, 98) = .340, p = .713.
All three groups performed similarly on the isolated LDT. A main effect was found for cognate
status, F (1, 98) = 62.399, p < .001, such that all participants responded more quickly to
cognates than non-cognates. There was a significant interaction found between cognate status
and proficiency group, F (2, 98) 4.743, p = .011. The intermediate L2 learners had a larger
difference in response times between cognates and non-cognates, compared to the other two
groups.
The overall results of the accuracy data were also tested using a repeated measures
ANOVA with one within-subjects factor, cognate status (high, low) and one between-subjects
factor, proficiency group (intermediate, advanced, Spanish-English bilingual, native Spanish
speaker). A main effect for group was found, F (3, 110) = 14.080, p < .001. Post hoc tests using
the Bonferroni correction revealed that the intermediate L2 learners were significantly less
accurate than the other three proficiency groups (ps < .001). A significant main effect was found
for cognate status, F (1, 110) = 14.261, p < .001. Overall, all participants processed cognates
more accurately than non-cognates. A significant interaction was also found between cognate
status and proficiency group, F (3, 110) = 14.613, p < .001. The intermediate L2 learners had an
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8% difference in accuracy between cognates (99%) and non-cognates (92%), while the
difference between conditions in the other three proficiency groups was less than 0.014%.
Finally, I analyzed the accuracy results from the isolated LDT without the results from
the native Spanish speakers. All the significant effects and interactions that were found when
examining all four participant groups were maintained in the analysis of the intermediate L2
learners, advanced L2 learners, and Spanish-English bilinguals. There was a significant effect
for group, F (2, 98) = 15.814, p < .001. Post hoc tests using the Bonferroni correction revealed
that the intermediate L2 learners were significantly less accurate than the advanced L2 learners
and Spanish-English bilinguals (ps < .001). A significant effect was also found for cognate
status, F (1, 98) = 22.252, p < .001. Overall, all participants were more accurate with cognates
than non-cognates. A significant interaction was also found between cognate status and
proficiency group, F (2, 98) = 14.944, p < .001. The intermediate L2 learners were much less
accurate for non-cognates than cognates, compared to the advanced L2 learners and SpanishEnglish bilinguals.
In summary, the results from the LDT in isolation give evidence of a cognate facilitation
effect for L2 learners and bilinguals. The only group that did not show a main effect for cognate
status in both reaction time and accuracy data was the native Spanish speaking control group.
Since these participants were highly Spanish dominant and living in a Spanish-speaking country
during the time of testing, it was hypothesized that they would not show evidence of processing
cognates more quickly and more accurately than non-cognates. The results from the
intermediate L2 learners is also not surprising given that these participants showed a cognate
facilitation effect in sentence context. The results from the advanced L2 learners, who showed a
cognate facilitation effect in response times, and the Spanish-English bilinguals, who showed a
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cognate facilitation effect in response times and accuracy data, are more surprising since they
did not show a cognate facilitation effect in Experiment 1 with the sentence context. In
isolation, these participants showed evidence of non-selective processing, which would be
expected. However, with the addition of the sentence context, the cognate facilitation effect
disappears, thus suggesting the possibility that these participants are able to process more
selectively with the addition of the sentence context. I discuss these findings from Experiment 1
and the isolated LDT in more detail in the next section.
Discussion
Experiment 1 tested how participants with various proficiencies in English and Spanish
processed lexical items in sentence context. The task was a LDT following a presentation of a
sentence in Spanish. In the LDT, participants had to decide if a string of letters formed a word
in Spanish. The targets were cognates and matched controls, and the sentence contexts were
constrained for high and low semantics and/or syntax. The experiment was designed to see if
semantic or syntactic constraint affected the processing of cognates and non-cognates. First, I
investigated whether there were main effects for cognate status, semantic constraint, and
syntactic constraint in both response times and accuracies for each participant group. Next, I
examined the response time and accuracy results for each participant group from an isolated
LDT, which omitted the sentence context to verify if the materials would yield a cognate
facilitation task in isolation.
In Experiment 1, there emerged a distinct pattern of processing and accuracy with regard
to the variable cognate status between the intermediate L2 learners and the rest of the three
participant groups: the advanced L2 learners, the Spanish-English bilinguals, and the native
Spanish speakers. The intermediate L2 learners group was the only group to show a cognate
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facilitation effect where they processed cognates significantly more quickly and more accurately
than non-cognates. The other three participant groups did not show any significant effects in
response time or accuracy for cognate status. Furthermore, the results showed an overall effect
for group, such that the intermediate L2 learners were indeed less accurate than the other three
proficiency groups. Due to the intermediate L2 learners’ lower proficiency level in Spanish,
they had a more difficult time “shutting off” their more dominant L1. The other three participant
groups had advanced proficiency in Spanish or were native Spanish speakers. With this higher
level of proficiency in Spanish, these participants did not have as much competition to suppress
English. These findings give evidence that the advanced L2 learners, Spanish-English
bilinguals, and native Spanish speakers were processing selectively, while the intermediate L2
learners were processing non-selectively.
With regard to the variable semantic constraint, all groups except the native Spanish
speakers found facilitation effects in response times for high semantic constraints sentences. By
highly constraining the semantics of the sentence, participants responded more quickly than if
the sentence had low semantic constraint. These findings support the features restriction
hypothesis (Schwanenflugel & LaCount, 1988; Kellas, Paul, Martin, Simpson, 1991), which
states that sentences with more meaningful features will constrain the activation of potential
words in a sentence. Another type of model that supports the semantic facilitation effect found
by the participants is context-dependent models. Context-dependent models argue that high
semantic constraint sentences have faster processing times due to the high predictability of the
target words. Recall that Altarriba, Kroll, Sholl, and Rayner (1996) and Ehrlich and Rayner
(1981) found that predictable words were processed more quickly than non-predictable words.
Since the high semantic constraint sentences restricted the features of the possible target words,
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it made these words more predictable. In the sentence Yo leo la novela en la biblioteca “I read
the novel in the library,” the target word novela “novel” is being restricted to something that [is
read] and [in the library]. These semantic restrictions may make the target word more
predictable and thus processed more quickly. Just as there are semantic-relatedness effects in
word lists (Meyer & Schvaneveldt, 1976), so too can a sentence highly constrained for
semantics yield context effects. There is also some evidence that a word that is semantically
related to another word may prime it (Sereno & Rayner, 1992). According to this explanation,
the word biblioteca “library” or the word leo “I read” may be able to prime the target word
novela “novel.” Predictability and sentence priming are two possible explanations that contextdependent models use to explain the results of the semantic facilitation effects.
The variable of syntactic constraint showed no significant effects for all of the
participants groups in both response times and accuracies. The results from syntactic constraint
suggest that L2 learners and bilinguals were not affected in processing or accuracy by high or
low syntactic sentences. Thus, syntactic constraint does not seem to be used as a cue to process
selectively as there were no facilitation effects for high syntactic constraint. These results
suggest that the features restriction hypothesis may not be applicable for syntax. When L2
learners, Spanish-English bilinguals, or native Spanish speakers read a sentence such as, La
novela la leo en la biblioteca “The novel it I read in the library,” the syntax is not being used as
a cue to help facilitate processing. Semantics and syntax, therefore, may be utilized differently
in processing.
Importantly, no significant interactions were found in any participant group for semantic
or syntactic constraint and cognate status. In Schwartz and Kroll (2006), Van Hell and De Groot
(2008), and Gullifer, Dussias, and Kroll (2010), the results showed a significant interaction
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between sentence constraint (semantic constraint for the first two studies and syntactic
constraint for the third) and cognate status. These findings supported the idea that the high
sentence constraint condition was able to modulate non-selective processing. However, the
results from Experiment 1 showed that neither semantic constraint nor syntactic constraint
interacted with cognate status. The cognate status was present only for the intermediate L2
learners, but for all conditions, even the most highly constrained condition: high semantic and
high syntactic constraint.
In order to ensure that the lack of a cognate facilitation effect for the advanced L2
learners and Spanish-English bilinguals was not due to the target cognates and non-cognates
used in Experiment 1, participants also completed a LDT without sentence context for the same
targets. The results showed that all groups except for the native Spanish speakers showed
evidence of a cognate facilitation whereby they processed cognates more quickly than noncognates. These results suggest that the materials were able to show non-selective processing
for the participants. Due to the overlap of orthography, phonology, and semantics, the BIA+
model’s (Dijkstra & Van Heuven, 2002) Identification System predicts that cognates are
processed more quickly than matched non-cognates. However, the BIA+ model also contains a
Task Schema. The Task Schema purports that language processing is not only affected by
linguistic elements, but also by the task itself. The task’s demands and expectations may affect
the results. The difference between Experiment 1 and the isolated LDT is twofold: 1) The
sentence context in Experiment 1 primed the target word so there was more linguistic
information to be processed, which could have affected word recognition. 2) The task itself was
different in that the participant had to wait 4 seconds to read the entire sentence before the string
of letters appeared on the screen. All the participants had 4 seconds to read the sentence context
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in order to standardize the task. This procedure was piloted with other intermediate L2 learners
who told the researcher that they had sufficient time to read the whole sentence. However, the
more advanced L2 learners and the native Spanish speakers may have had too much time to
read the sentence, thus making it difficult to tap into the small window of non-selective
processing (as found in the early-stage comprehension measures from Libben & Titone, 2009).
With the LDT in isolation, the task was able to tap into non-selective processing for both the
advanced L2 learners and Spanish-English bilinguals. Future studies should work to try and
disentangle task effects from context effects in order to better understand the factors that
modulate selective lexical processing.
In the next chapter, I will discuss Experiment 2, a chapter that focuses on another aspect
of lexical processing: grammatical class effects. These grammatical class effects are further
investigated by analyzing how sentence context affects processing across proficiency levels. I
start the chapter by describing the participants, materials, and procedures, and then continue by
discussing the response time and accuracy results. I will then discuss and summarize the results.
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CHAPTER FIVE
EXPERIMENT 2: GRAMMATICAL CLASS AND SEMANTIC
SIMILARITY IN SENTENCE CONTEXT – LEXICAL
DECISION TASK
Introduction
Experiment 2 examined how grammatical class effects differ across proficiency level
when primed by a sentence context. A LDT was used with a sentence context prime. The
sentence context was constrained for semantics (high vs. low). The target words were
constrained for both grammatical class (noun vs. verb) and semantic similarity (high vs. low)
with the last word in the sentence context prime. The questions investigated were: Does a
difference in grammatical class affect lexical processing? Does the semantic similarity between
the last word in the sentence context and the target word show a priming relationship? Does
semantic constraint at the sentence level affect processing of the targets?
This experiment continued the investigation from previous research done by Sunderman
and Kroll (2006) and Campbell (2009). These studies examined grammatical class effects for
lexical processing without sentence contexts. Sunderman and Kroll used a translation
recognition task and found that L2 learners were using grammatical class as a cue for lexical
processing because participants experienced less lexical interference when the prime-target pair
did not share the same grammatical class. Campbell (2009) used a LDT in isolation and found
that prime-target pairs with the same grammatical class had slower reaction times than primetarget pairs with mis-matched grammatical class. L2 learners were also faster to respond to
prime-word pairs that were semantically related than those that were unrelated. The current
experiment used a LDT with a sentence context prime (with high and low semantic constraint)
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instead of an isolated word prime to investigate how sentence context affects lexical processing
cues for L2 learners and bilinguals with various proficiency levels.
Method
Participants
The participants were the same as participants from Experiment 1. The only difference is
that there were 17 participants who did not take Experiment 2, but did complete Experiment 1
as part of a pilot study. Two native Spanish speakers and one intermediate L2 learner were also
eliminated as they did not accurately respond to all items within one of the sentence conditions,
so it was not possible to run their response time data. Table 5.1 shows the number of
participants in each participant group for Experiment 2. The total number of participants
included in data analysis was 94.
Table 5.1. Number of participants in each proficiency level for Experiment 2.
Proficiency Level
n
Intermediate
45
Advanced
15
Spanish-English bilinguals
23
Native Spanish speakers
11
Stimuli
To test how sentence context constrains lexical processing, three variables were
investigated: grammatical class (noun vs. verb), semantic similarity (between the target and the
last word in the sentence), and semantic sentence constraint (high vs. low).
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Grammatical Class and Semantic Similarity Materials were created such that the
relationship between the last word in the sentence and target words were manipulated for
grammatical class and semantic similarity (see Appendix H for the materials used in
Experiment 2). All of the target words, which appeared on the screen after participants read the
entire sentence, were either concrete nouns or verbs. Four different target words, adapted from
Campbell (2009) and matched in frequency and length, were created for each sentence, although
participants only saw one of the four target words. Consider the following sentence and target
words in Table 5.2:
Table 5.2. Sentence with four different target conditions from Experiment 2.
Sentence
Los viernes tomo vino de un vaso.
Target words
copa ‘cup’
“On Fridays I drink wine from a glass.”
[+grammatical class,
+semantic similarity]
beber ‘to drink’
[-grammatical class,
+semantic similarity]
flor ‘flower’
[+grammatical class,
-semantic similarity]
decidir ‘to decide’
[-grammatical class,
-semantic similarity]
In this example from Table 5.2, the last word in the sentence is vaso “glass.” The first target
word, copa “cup” is a noun. Thus, both vaso “glass” and copa “cup” belong to the same
grammatical class. These two words also share semantic similarity because they are items that
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are used for drinking. Therefore, copa “cup” is marked as [+grammatical class, +semantic
similarity] with respect to the last word in the sentence, vaso “glass.” The next target word that
could appear after the sentence is beber “to drink.” This target word is a verb, unlike the noun
vaso “glass,” so it is [-grammatical class]. However, the target word shares semantic similarity,
so it is marked [+semantic similarity]. The third target word, flor “flower” is [+grammatical
class, -semantic similarity] because it is also a noun but its meaning does not relate to vaso
“glass.” The last target word is decidir “to decide,” which shares no grammatical class or
semantic similarity to the last word in the sentence, and thus is marked [-grammatical class,
-semantic similarity].
If participants are using grammatical class as a cue (as found in Sunderman & Kroll,
2006; Campbell, 2009), participants are expected to respond faster and more accurately to target
words that do not share the same grammatical class (i.e. [-grammatical class]) with the last word
in the sentence. For example, participants should be faster and more accurate to respond to the
target word decidir “to decide” than the target word flor “flower.” Also, participants are
expected to respond faster and more accurately to target words that share semantic similarity
with the last word in the sentence (i.e. [+semantic similarity]). Thus, participants should be
faster to decide that copa “cup” is a word than flor “flower.” Previous research (Kroll and
Sunderman, 2006; Campbell, 2009) also suggests sensitivity to grammatical class, even with
low proficient L2 learners, albeit in different tasks.
As in Experiment 1, sentences were created with high or low semantic constraint. I will
discuss how sentence constraint was determined in a later section. A sentence with high
semantic constraint provides a narrower context, making the last word of the sentence more
predictable. A low semantic constraint sentence, however, does not restrain what the last word
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in the sentence may be. Table 5.3 provides examples of both a high and low semantic constraint
sentence with their corresponding target words:
Table 5.3. High and low semantic constraint sentences with targets.
Sentence
High semantic constraint Los viernes tomo vino de un vaso.
“On Fridays I drink wine from a
glass.”
Low semantic constraint
En la tienda compré un vaso.
“In the store I bought a glass.”
Target words
copa “cup”
beber “to drink”
flor “flower”
decidir “to decide”
copa “cup”
beber “to drink”
flor “flower”
decidir “to decide”
In the high semantic constraint sentence, vaso “glass” is predicted because, following the
feature restrictions hypothesis, the last word in the sentence has the feature [drink wine out of].
The low semantic constraint sentence does not predict vaso “glass” because it has the feature
[bought in a store]. Thus, the semantics of the sentence will be manipulated to see how this
affects participants’ processing of the various target words. In high semantic constraint
sentences, it is possible that the semantics of the sentence will be so strong that participants only
activate a few possible lexical representations. Thus, participants will be faster and more
accurate to decide that copa “cup” is a word in comparison to when copa “cup” follows a low
semantic constraint sentence. Low proficiency participants may also show faster reaction times
and higher accuracy on high semantic constraint sentences with target words that are [+semantic
similarity] if they are relying on semantic information provided by the whole sentence context.
If low proficiency participants do not show as large of an effect for high and low constraint
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sentences with target words that are [+semantic similarity], this shows that they are not sensitive
to the semantic information in the sentence context.
By adding a sentence context, the grammatical class of the last word in the sentence
should be more salient. In both the high and low semantic constraint contexts, the last word in
the sentence, vaso “glass” is preceded by the determiner un “a.” From repeated exposure to the
language, participants know that nouns take determiners, like un “a.” Thus, in both the high and
low semantic constraint sentences, the grammatical class of the last word in the sentence may be
more predictable than it would be if it were presented in isolation. As predicted by the revised
hierarchical model, less proficient learners may still be decoding each L2 word by translating it
to their L1, while high proficient learners are able to connect the words in the sentence to their
conceptual meanings. High proficient L2 learners, therefore, may be more sensitive to
grammatical class interference than low proficient L2 learners. For example, when a target verb
like decidir “to decide” follows a high or low constraint sentence, high proficient participants
may be faster and more accurate to determine that it is a word in comparison to the noun flor
“flower.”
Norming the Stimuli In Experiment 2, there were 56 targets: 14 nouns with high
semantic similarity to the last word in the sentence, 14 nouns with low semantic similarity, 14
verbs with high semantic similarity, and 14 verbs with low semantic similarity. There were also
56 non-word fillers used in Experiment 12. Table 5.4 shows the mean word length and mean
word frequency per million in Spanish (Davis & Perea, 2005) for each of the target conditions.
The results from a one-way, four-level ANOVA showed no significant differences were found
between all conditions for mean word length, F (3, 220) = .876, p = .454, and mean word
frequency, F (3, 220) = .414, p = .743. The 56 non-words were created by taking Spanish words
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and changing one letter in the word to create a non-word. The non-words still followed the
phonotactic constraints of Spanish and were verified by a native Spanish speaker to not be
actual Spanish words. The mean word length of the 56 non-words was 6.39. The results from a
one-way, five-level ANOVA yielded no significant difference between the word length of the
critical targets and fillers, F (4, 275) = .745, p = .562. The mean word frequency was not
calculated for the non-words, since by the nature of not beings words, these fillers did not have
word frequency.
Table 5.4. Mean word length (number of letters) and mean word frequency per million in
Spanish (Davis & Perea, 2005) for targets.
+GC +SS
-GC +SS
+GC -SS
-GC -SS
Word Length
6.64
6.48
6.29
6.73
Frequency
31.81
29.22
35.45
27.73
After all the critical and filler items were matched on word length and the critical items
were matched on word frequency, I next normed the semantic similarity between the critical
targets and the last word of the sentence context. The sentence context was constructed so that
the last word in each sentence was a noun, and this noun ended both the high semantic
constraint sentences and the low semantic constraint sentences. In order to determine semantic
similarity of the targets to the last word in the sentence, a survey was completed by 29
participants (see Appendix G for the materials of the survey). These participants were from the
same group that completed the survey from Experiment 1. The participants were given the last
word in the sentence and asked to rate how semantically similar it was to the target on a 7 point
scale. A rating of 7 indicated that the target word was very similar in meaning to the last word
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in the sentence. A rating of 4 indicated that the target word and last word in the sentence were
somewhat similar in meaning, and a rating of 1 indicated that the target word and last word in
the sentence were not similar at all in meaning. Table 5.5 shows the mean semantic similarity
rating for all conditions. The targets were created so that there would be no significant
differences in semantic similarity between the nouns and verbs. However, materials were
created so there would be a significant difference between high and low semantic similarities. A
paired samples T-test between overall similar pairs and the distractor pairs revealed a significant
effect, t (111) = 21.083, p < .001, such that the similar pairs had a higher semantic similarity
rating than the distractor pairs. The difference between similar pairs with matching grammatical
class (+GC) and mis-matching grammatical class (-GC), however, was significant, t (55) = 4.49,
p < .001. Nouns had a significantly higher semantic similarity rating to the last word in the
sentence than the verbs.
Table 5.5. Mean semantic similarity rating (on scale of 1 - 7) between last word of the sentence
and critical targets where 1 indicates “not similar at all in meaning” and 7 indicates “very
similar in meaning.”
Semantic Similarity
Overall similar last word-target pairs
3.84
+ Grammatical class
4.21
- Grammatical class
3.46
Distractor pairs
1.50
Next, the sentence context was normed for high and low semantics. In order to
determine whether a sentence had high or low semantics, a survey was presented to 32
participants in a 3000 level Spanish course. These were from the same set of participants who
normed the materials for Experiment 1 and the target semantic similarity in Experiment 2. In the
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survey, participants were asked to rate how well the sentence predicts the last word in the
sentence. For example, consider the sentence in (1):
(1) Cuando tengo sed tomo agua de un vaso.
When I am thirsty I drink water from a glass.
The participants had to indicate how well sentence (1) predicts the word vaso “glass.” As in
Experiment 1 and the target semantic similarity survey for Experiment 2, the participants rated
the sentences on a 7 point scale where a score of 7 meant that the sentence strongly predicted
the last word, a score of 4 meant that the sentence somewhat predicted the last word of the
sentence, and a score of 1 indicates that the sentence did not predict the last word in the
sentence. As shown in Table 5.6, the mean rating for the sentence predicting the last word in the
sentence for high semantic constraint sentences was 4.73 and the mean rating for the sentence
predicting the last word in the sentence for low semantic constraint sentences was 2.67. The two
conditions were significantly different, t (55) = 14.655, p < .001, as the high semantic constraint
ratings were higher than the low semantic constraint sentences.
Table 5.6. Mean rating (on scale of 1 - 7) for how well sentence predicts the last word in the
sentence where 1 indicates “sentence does not predict last word” and 7 indicates “sentence
strongly predicts last word.”
High semantic constraint
Low semantic constraint
Predictability rating
sentences
sentences
4.73
2.67
Design
As shown in Table 5.7, there were 2 different critical sentence conditions (high and low
semantics) and 4 different types of critical targets. Half of the targets were nouns (+grammatical
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class) and half the targets were verbs (-grammatical class). Of these targets, half had high
semantic similarity to the last word in the sentence context (related) and half had low semantic
similarity to the last word in the sentence context (unrelated). In total, there were 56 critical
items as shown in Table 5.7. All of these critical items elicited a “yes” response since all of the
items were words in Spanish. There were also 56 filler items that elicited a “no” response. Since
the filler items were not real words, the sentences were not created with high or low semantic
constraint. In total, participants read 112 sentences and responded to 56 words and 56 nonwords. There also were a total of 10 comprehension questions that appeared randomly
throughout the experiment. The comprehension questions elicited a “yes” or “no” response and
were presented in English. The questions asked about the meaning of the sentence context.
Table 5.7. Illustration of design of materials for Experiment 2.
+GC
-GC
High semantic constraint
Low semantic constraint
Fillers
7 Related
7 Unrelated
7 Related
7 Unrelated
28 non-words
7 Related
7 Unrelated
7 Related
7 Unrelated
28 non-words
Procedure
The procedure for Experiment 2 was very similar to the procedure of Experiment 1 with
one difference. In Experiment 2, there was no blank space in the sentence. First, an asterisk
appeared as a fixation point in the middle of the screen for 1 second. Then, the participants read
the entire sentence for 4 seconds on a computer screen, and the sentence disappeared. Next, a
string of letters appeared on the screen and participants indicated whether the string of letters
formed a word in Spanish. Participants used the same buttons to indicated a “yes” or “no”
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response as they did in Experiment 1. After 10 trials, a sentence comprehension question in
English appeared, and participants were instructed to answer the question with the same “yes”
or “no” buttons. Again, participants had 10 practice trials before the experiment began and
during this time, all participants were encouraged to ask the researcher questions. Sentence
comprehension questions followed 3 of the 10 practice trials. Reaction times to the nearest
millisecond and accuracy were recorded by the computer software SuperLab. Reaction times
were recorded from the onset of the target word.
Results
Data Analysis
As in Experiment 1, critical items (items with a “yes” response) with a correct response
were used in the reaction time analysis. For the accuracy data, both correct and incorrect
responses for the critical items were included. For the native Spanish speaking group, the
advanced L2 learner group, and the Spanish-English bilingual group, the reaction times that
were faster than 200ms or slower than 3000ms were deleted since they were considered outliers.
Since the intermediate L2 learners had less proficiency in Spanish, only reaction times above
5000ms were excluded as outliers. As with the other groups, reaction times faster than 200ms
were eliminated from the data set for the intermediate L2 learners. Means for each condition
were calculated for each participant. Next, standard deviations were found for each participant’s
mean. Reaction times outside 2.5 standard deviations of the participant’s means were excluded
from the data, as well. The data was cleaned up in this way in order to ensure that exceptionally
fast and slow responses that may not reflect true lexical processing would not be included in the
analysis.
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Before discussing the findings from Experiment 2, I present the results from the
comprehension questions. The ANOVA yielded no significant effect for the accuracy results, F
(3, 90) = .901, p < .444. Although the native Spanish speakers had the highest accuracies on the
comprehension questions, the difference between groups was not significant. All groups
answered above chance, showing that they were paying attention to the meaning of the
sentences during the experiment. Table 5.8 shows the mean accuracies for the comprehension
questions for each group.
Table 5.8. Percent accuracy for all participant groups for sentence comprehension questions.
Proficiency Level
Percent Correct
Intermediate
66%
Advanced
66%
Spanish-English bilinguals
64%
Native Spanish speakers
74%
Next, I will present reaction time and accuracy data from the native Spanish speakers,
followed by the intermediate L2 learners, advanced L2 learners, and Spanish-English bilinguals.
I finish by summarizing all the findings and discussing their implications to lexical processing
in sentence context.
Native Spanish Speakers Results
First I report the response time data from the native Spanish speakers. A repeated
measures ANOVA with three factors (high and low grammatical class, high and low semantic
similarity, high and low semantic constraint) was conducted. For the grammatical class variable,
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the high value meant that the target was a noun, and the low value meant that the target was a
verb. For the semantic similarity variable, recall that this variable was for the semantic value
between the target word and the last word in the sentence context. In the following tables,
semantic similarity is referred to as “related” or “unrelated.” Finally, the semantic constraint
variable had high or low conditions to measure the semantic value between the sentence as a
whole and the last word in the sentence. Table 5.9 shows the mean reaction times and
accuracies for the native Spanish speakers for Experiment 2. The condition with the fastest
response times is the +GC related semantics and low semantic constraint condition (806ms).
The results from the repeated measures ANOVA showed that there was no main effect for
grammatical class, F (1, 10) = 3.354, p = .097, although the response times for verbs (-GC)
were faster than the nouns (+GC). There was no main effect found for semantic similarity, F (1,
10) = 2.257, p = .164, although the native Spanish speakers read semantically similar (related)
targets 23ms faster than non-semantically (unrelated) similar targets. A marginally significant
interaction was found between grammatical class and semantic similarity, F (1, 10) = 4.630, p =
.057. As in Experiment 1, I define marginal to mean p falls between .055 - .07. When the target
was a noun (+GC), the non-semantically similar targets had faster response times than the
semantically similar targets. Yet when the target was a verb (-GC), the semantically similar
targets had faster response times than the non-semantically similar targets. It appears that the
native Spanish speakers show some sensitivities to both grammatical class and the semantics of
the last word in the sentence context. The sentence context itself, however, did not affect
response times; no main significant effect was found for sentence constraint, F (1, 10) = .392, p
= .802. There also was no significant interaction between semantic similarity and sentence
context, F (10) = .196, p = .667, nor between grammatical class and sentence context, F (1, 10)
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= .092, p = .768. No significant interaction was found between grammatical class, semantic
similarity, and sentence context, F (1, 10) = .487, p = .501.
Table 5.9. Mean RTs (ms) and percent accuracy for Experiment 2 for native Spanish speakers.
High semantic constraint
Low semantic constraint
Related:
880 (100%)
Related:
893 (95%)
Unrelated:
878 (99%)
Unrelated:
854 (100%)
Related:
827 (97%)
Related:
806 (100%)
Unrelated:
891 (97%)
Unrelated:
875 (99%)
Grammatical class
+
-
Next, I present the results from the accuracy data for the native Spanish speakers.
Another repeated measures ANOVA with three factors was conducted. The results showed no
main significant effects for grammatical class, F (1, 10) = .006, p = .938, for semantic similarity
F (1, 10) = .396, p = .543, nor for semantic constraint, F (1, 10) = .003, p = .954. There was no
significant interaction between grammatical class and sentence constraint, F (1, 10) = 3.742, p =
.082, and no significant interaction between grammatical class, semantic similarity, and
sentence constraint, F (1, 10) = 3.724, p = .082. Thus, the native speakers showed no effects in
the accuracy data.
Intermediate L2 Learners Results
I now present the response time results for the 45 intermediate L2 learners. Table 5.10
shows the mean response times in milliseconds and accuracy results for the critical conditions
of Experiment 2. The condition with the fastest response time (1226ms) was the related –GC
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and high semantic constraint condition. In other words, intermediate learners were fastest to
respond to target verbs when the target was semantically related to the last word in the sentence
and when the sentence context had high semantic constraint. The condition with the slowest
response time (1321ms) was the unrelated +GC with high semantic constraint. When the target
was a noun and not semantically similar to the last word in the sentence and the sentence
semantic constraint was high, the intermediate L2 learners were the slowest to respond.
A repeated measures ANOVA with three-factors (grammatical class, semantic
similarity, and sentence constraint) was conducted, but no significant main effects or
interactions were found (all conditions with ps > .1). These findings suggest that there was no
difference in processing between conditions for this particular group of participants.
Table 5.10. Mean RTs (ms) and percent accuracy for Experiment 2 for intermediate L2
learners.
High semantic constraint
Low semantic constraint
Grammatical class
+
-
Related:
1260 (80%)
Related:
1320 (77%)
Unrelated:
1321 (80%)
Unrelated:
1302 (78%)
Related:
1226 (89%)
Related:
1248 (89%)
Unrelated:
1273 (90%)
Unrelated:
1311 (91%)
The accuracy results for each condition are shown in Table. 5.10. A repeated measures
ANOVA with three-factors (grammatical class, semantic similarity, and sentence constraint)
was conducted for the intermediate L2 learners. A significant main effect for grammatical class
was found, F (1, 44) = 41.490, p < .001. Intermediate L2 learners were more accurate when the
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target was a verb than when it was a noun. No other main effects were found: not for semantic
similarity, F (44) = .913, p = .450, nor for semantic constraint, F (1, 44) = .296, p = .589. No
significant interactions were found between grammatical class and semantic similarity, F (1, 44)
= .115, p = .737, between grammatical class and sentence constraint, F (1, 44) = 2.173, p =
.148, nor between semantic similarity and sentence constraint, F (1, 44) = .100, p = .753. No
significant interaction was found between grammatical class, semantic similarity, and sentence
context, F (1, 44) = .064, p = .801.
Advanced L2 Learners Results
Next, I discuss the response time and accuracy data for the 15 advanced L2 learners.
Table 5.11 shows the response time and accuracy data for the advanced L2 learners.
Table 5.11. Mean RTs (ms) and percent accuracy for Experiment 2 for advanced L2 learners.
High semantic constraint
Low semantic constraint
Grammatical class
+
-
Related:
1087 (97%)
Related:
1122 (96%)
Unrelated:
1098 (99%)
Unrelated:
1060 (100%)
Related:
1053 (99%)
Related:
1084 (98%)
Unrelated:
1079 (99%)
Unrelated:
1086 (97%)
A repeated measures ANOVA with three-factors (grammatical class, semantic
similarity, and sentence constraint) was conducted, but no significant main effects or
interactions were found (all conditions with ps > .1). These findings suggest that there was no
difference in processing between conditions for this particular group of participants. Thus, both
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native English speaking participant groups did not show evidence of grammatical class effects
after reading a sentence prime.
Another repeated measures ANOVA with the same three-factors (grammatical class,
semantic similarity, and sentence constraint) was tested on the accuracy results for the advanced
L2 learners. This test showed that, like was found with the response time results, there were no
significant main effects for grammatical class, F (1, 14) = .072, p =.792, semantic similarity, F
(14) = 1.522, p = .238, nor for sentence constraint, F (1, 14) = 1.000, p = .334. A significant
interaction, however, was found between grammatical class and semantic similarity, F (1, 14) =
4.699, p = .048. When the target was a noun, the accuracy for unrelated targets was 99.5%, but
when the targets were related, the accuracy was 96.7%. When the target was a verb, the
accuracy results were similar for the two conditions of semantic similarity: high semantic
similarity (related targets) had 98.6% accuracy and low semantic similarity (unrelated targets)
had 98.1% accuracy. No significant interaction was found between grammatical class and
sentence constraint, F (1, 14) = .677, p = .424, nor between semantic similarity and sentence
context, F (1, 14) = .104, p = .751. No significant interaction was found between grammatical
class, semantic similarity, and sentence context, F (1, 14) = .677, p = .424.
Spanish-English Bilinguals Results
Next, I discuss the response time and accuracy data for the 23 Spanish-English
bilinguals, which are presented in Table 5.12. A repeated measures ANOVA with three-factors
(grammatical class, semantic similarity, and sentence constraint) was conducted. A significant
main effect for semantic similarity was found, F (1, 22) = 4.241, p = .051. The targets with high
semantic similarity (related) to the last word in the sentence context had faster reaction times
than the targets with mismatched semantic similarity (unrelated). No significant main effect was
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found for grammatical class, F (1, 22) = .139, p = .713, nor for semantic constraint, F (1, 22) =
2.152, p = .157. A significant interaction, however, between grammatical class and semantic
similarity was found, F (1, 22) = 4.677, p = .042. When the target was a noun (+GC), targets
with high semantic similarity were processed 18ms more quickly; when the target was a verb (GC), targets with high semantic similarity were processed 75ms more quickly. Thus,
participants were faster to respond to related targets when the target as a verb (1053ms) than a
noun (1091ms). No significant interactions were found between grammatical class and sentence
context, F (1, 22) = 1.974, p = .174, nor between semantic similarity and sentence context, F (1,
22) = .554, p = .465. A marginally significant interaction was found between grammatical class,
semantic similarity, and sentence context, F (1, 22) = 3.672, p = .068. In all conditions, the
targets with high semantic similarity were processed more quickly than the targets with low
semantic similarity except for the related +GC and high sentence context. This condition was
processed 46ms slower than the unrelated +GC high sentence constraint condition.
Table 5.12. Mean RTs (ms) and percent accuracy for Experiment 2 for Spanish-English
bilinguals.
High semantic constraint
Low semantic constraint
Grammatical class
+
-
Related:
1071 (96%)
Related:
1112 (94%)
Unrelated:
1132 (93%)
Unrelated:
1086 (96%)
Related:
1035 (99%)
Related:
1072 (96%)
Unrelated:
1095 (96%)
Unrelated:
1162 (94%)
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Finally, I present the accuracy results from the 23 Spanish-English bilinguals. A
repeated measures ANOVA with three-factors (grammatical class, semantic similarity, and
sentence constraint) was conducted for the Spanish-English bilinguals. A marginally significant
effect for semantic similarity was found, F (1, 22) = 3.892, p = .061, such that targets with high
semantic similarity had higher accuracies (96.3%) than targets with low semantic similarity
(94.6%). No other main significant effects were found for grammatical class, F (1, 22) = 2.461,
p = .131, nor for semantic constraint, F (1, 22) = .681, p = .418. No significant interactions were
found (all ps > .05).
Overall Results
After presenting the results of the response time and accuracy data from each
proficiency group, it is clear that the proficiency groups have different processing patterns. In
order to compare the four proficiency groups in relationship to one another, I next present
results from a repeated measures ANOVA with both within-subjects and between-subjects
analysis. Although group sizes were small, this type of analysis provides another way to
examine processing at different levels of proficiency.
A repeated measures ANOVA with three within-subjects factors, grammatical class (1
“noun”, 0 “verb”), semantic similarity (high, low), sentence constraint (high, low) and one
between-subjects factor, proficiency group (intermediate, advanced, Spanish-English bilingual,
native Spanish speaker) was performed on the response times. A main effect for proficiency
group was found, F (3, 90) = 9.473, p < .001. Post hoc tests using the Bonferroni correction
revealed that the intermediate L2 learners had significantly slower response times than the
native Spanish speakers (p < .001), the Spanish-English bilinguals (p = .030), and marginally
significant slower response times than the advanced L2 learners (p = .060). No main effect was
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found for grammatical class, F (1, 90) = 2.160, p = .145, for semantic similarity, F (1, 90) =
2.752, p = .101, nor for sentence constraint, F (1, 90) = .528, p = .448. No significant interaction
was found between grammatical class and semantic similarity, F (1, 90) = 3.125, p = .081. No
other significant interactions were found.
Since the native Spanish speakers were a control group, I also analyzed the response
times between the other three proficiency groups: intermediate L2 learners, advanced L2
learners, and Spanish-English bilinguals. The results from the repeated measures ANOVA with
three within-subjects factors, grammatical class (1 “noun”, 0 “verb”), semantic similarity (high,
low), sentence constraint (high, low) and one between-subjects factor, proficiency group
(intermediate, advanced, Spanish-English bilingual) again found a significant effect for group, F
(2, 80) = 5.712, p = .005. Post hoc tests using the Bonferroni correction showed again that the
intermediate L2 learners differed significantly from the other proficiency groups: advanced L2
learners (p = .034) and Spanish-English bilinguals (p = .018). Overall, the mean response time
for the intermediate L2 learners was 1283ms, while the mean response time for the advanced L2
learners was 1083ms and for the Spanish-English bilinguals was 1095ms. No other significant
main effects or interactions were found.
Next, I present the results from the accuracy data for Experiment 2 for all four
proficiency groups. A repeated measures ANOVA with three within-subjects factors,
grammatical class (1 “noun”, 0 “verb”), semantic similarity (high, low), sentence constraint
(high, low) and one between-subjects factor, proficiency group (intermediate, advanced,
Spanish-English bilingual, native Spanish speaker) was performed on the accuracy data. A main
effect for proficiency group was found, F (3, 90) = 25.050, p < .001. Post hoc tests using the
Bonferroni correction revealed that the intermediate L2 learners were significantly less accurate
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than the other three proficiency groups (ps < .001). A main effect for grammatical class was
found, F (1, 90) = 9.984, p = .002. Overall, all participants were more accurate processing verbs
than nouns. A significant interaction was also found between grammatical class and group, F (3,
90) = 11.722, p < .001. The intermediate L2 learners were much less accurate processing nouns
(79%) than verbs (90%), while the other three proficiency groups did not have such a large
difference between grammatical class conditions. These same findings were also found when
the accuracy data from the native Spanish speakers were removed. A significant main effect was
found for group, F (2, 80) = 27.692, p < .001. Post hoc tests using the Bonferroni corrected
showed that once again the intermediate L2 learners overall were significantly less accurate than
the advanced L2 learners (p < .001) and Spanish-English bilinguals (p < .001). A main effect for
grammatical class was also found, F (1, 80) = 15.223, p < .001, such that all participants were
more accurate processing verbs than nouns. A significant interaction was also found between
grammatical class and group, F (3, 80) = 13.210, p < .001. The intermediate L2 learners were
more accurate processing verbs overall (90%) than nouns (79%), while the advanced L2
learners processed both nouns and verbs with 98% accuracy and the Spanish-English bilinguals
were also very accurate in both conditions: verbs (96%) and nouns (95%).
Summary
In summary, the previous results examined grammatical class and semantic similarity
for targets that followed a sentence context for four different participant groups with various
proficiency levels in English and Spanish. The processing patterns differed between those with
Spanish as a L1 (native Spanish speakers and Spanish-English bilinguals) and the Spanish L2
learners (both intermediate and advanced). The intermediate and advanced L2 learners did not
show any significant differences in their response times due to grammatical class, semantic
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similarity of the target word with respect to the last word in the sentence context, and the
sentence constraint. The accuracy data, however, did show some significant main effects and
interactions. The intermediate L2 learners only had a significant main effect for grammatical
class. Their accuracy scores were higher overall for verbs than it was for nouns. It appears,
therefore, that just as cognates yielded facilitation in Experiment 1, verbs are showing evidence
of facilitating processing in this experiment. The advanced L2 learners had a significant main
interaction between grammatical class and semantic similarity. When the targets were nouns,
the targets with related semantics had lower accuracies than the targets with unrelated
semantics. Yet, when the targets were verbs, both high and low semantic similarity conditions
had equal accuracy percentages (98%). Therefore, verbs were responded to more accurately
overall regardless of semantic similarity, while nouns seemed to be affected by the presence of
related semantics. Although the response time data did not show any significant main effects or
interactions, the accuracy results suggest that both groups of Spanish learners showed some
sensitivities to whether the target was a noun or verb.
The results from the two groups of L1 Spanish speakers showed that both groups had
evidence of sensitivities to grammatical class and semantic similarity in their response time
data. For the native Spanish speakers, the effect for grammatical class and semantic similarity
approached significance (p < .057) in the response time data. When the targets were nouns, the
unrelated semantic condition had faster response times than the related semantic condition. Yet,
when the targets were verbs, the opposite was found: high semantic similarity targets had faster
response times than low semantic similarity targets. The Spanish-English bilinguals had a
significant main effect for semantic similarity in their response time data. Targets with overall
high semantic similarity to the last word in the sentence context had faster response times than
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targets with low semantic similarity. The Spanish-English bilinguals also had a significant
interaction between grammatical class and semantic similarity. When the targets were nouns
and verbs, targets with high semantic similarity were processed more quickly than targets with
low semantic similarity. The interaction occurred because the difference between high and low
semantic similarity conditions was much higher when the targets were verbs than when they
were nouns.
For the accuracy results, the native Spanish speakers had no significant effects or
interactions. The Spanish-English bilinguals had a marginally significant effect for semantic
similarity. Not only were these participants faster to process high semantic similarity targets,
but they also were significantly more accurate. The response time results from the L1 Spanish
speakers show that both groups showed some sensitivities to grammatical class and semantic
similarity. The Spanish-English bilinguals also showed evidence of sensitivities in their
accuracy results, but the native Spanish speakers did not.
Discussion
Experiment 2 tested how four groups of participants with various proficiencies in
English and Spanish processed target words with differing semantic similarity and grammatical
class in sentences constrained for semantics. The task was a LDT following a presentation of a
sentence in Spanish where the last word in the sentence always was a noun. In the LDT,
participants had to decide if a string of letters formed a word in Spanish. The targets were nouns
(+GC) or verbs (-GC) and varied in semantic similarity (high or low) with the last word in the
sentence context. The sentence contexts were constrained for high and low semantics. The
experiment was designed to see how a sentence context may affect previously found
grammatical class and semantic similarity effects. I examined whether there were main effects
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for grammatical class, semantic similarity (between the target and the last word in the sentence
context), and sentence constraint in both response times and accuracies for each participant
group in order to investigate how sentence context affected lexical processing.
In Experiment 2, different patterns emerged between L1 speakers of Spanish and L2
learners of Spanish. The L1 speakers of Spanish, consisting of the native Spanish speakers
tested in Spain and the Spanish-English bilinguals (who all grew up speaking Spanish but lived
in the United States), showed effects for grammatical class and semantic similarity. The native
Spanish speakers had an interaction between grammatical class and semantic similarity, as did
the Spanish-English bilinguals. The nature of the interaction, however, differed between groups.
The native Spanish speakers responded faster in the low semantic similarity condition for
nouns, but not verbs, while the Spanish-English bilinguals were faster to respond to the high
semantic similarity condition, regardless of whether the target was a noun or verb. The mean
difference between conditions was much larger when the targets were verbs, however. The
Spanish-English bilinguals also showed a significant effect for semantic similarity. These
participants overall responded more quickly when the targets had high semantic similarity. This
finding was predicted by Sunderman and Kroll (2006), since this study found evidence that
semantic similarity yielded facilitation in processing using a translation recognition task.
The non-native Spanish participant groups did not show any significant main effects in
response times for grammatical class, semantic similarity, and sentence context. Campbell
(2009) found grammatical class effects for intermediate L2 learners using a LDT with word
primes instead of sentence primes. In this task, the grammatical class of the prime was very
salient, as there was only one word to process and the grammatical class was an inherent
property of the prime. It is possible that the grammatical class effects were eliminated for the L2
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learners in this study due to the presence of the sentence context. Although it was predicted that
the sentence context would help strengthen the activation of nouns (since the last word of the
sentence was always a noun), the participants read sentences with both verbs and nouns, which
may have increased the activation of both grammatical classes. Thus, they were not faster to
respond to verbs over nouns. The lack of sentence constraint effects may be due to the fact that
both the high and low semantic constraint conditions ended with the same word. Sereno and
Rayner (1992) found some evidence that a word that is semantically related to another word
may prime it. Thus, by having at least one word in the high and low sentence constraint
conditions that was the same, even though the high semantic constraint sentences predicted the
last word of the sentence better than the low semantic constraint sentences, the extra semantic
information was not used to facilitate processing. The advanced L2 learners showed a
significant interaction in their accuracy data for grammatical class and semantic similarity.
When the targets were nouns, accuracies were higher for the low semantic similarity conditions,
but when the targets were verbs, the accuracies were approximately the same for both high and
low semantic similarity targets. The intermediate L2 learners only showed an effect for
grammatical class in their accuracy results. These learners were more accurate with target verbs
than target nouns. There is some evidence, therefore, that the L2 learners found it easier to
respond to verbs over nouns. Recall that when the materials were normed, there still was a
significant difference between nouns and verbs in relation to their semantic similarity to the last
word in the sentence. Ideally, the nouns and verbs would have been equally related to the last
word in the sentence context. It is possible that verbs had higher accuracies than nouns because
they were less similar semantically to the target word in the sentence context. Thus, even
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though the response data did not show effects for grammatical class, L2 learners showed some
sensitivities to grammatical class of the target word in the accuracy results.
In the final chapter, I will discuss the most salient findings from Experiments 1 and 2. I
start the chapter by discussing the most important findings from each experiment. Next, I revisit
models of lexical processing and examine the implications the results from Experiments 1 and 2
may have on these models. Finally, I also make connections between the findings and L2
pedagogy and give conclusion and directions for future research.
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CHAPTER SIX
CONCLUSIONS
In this last chapter, I return to the broad questions that motivated this study: how does
sentence context affect lexical processing? Are L2 learners, Spanish-English bilinguals, and
native Spanish speakers sensitive to semantics and syntax in the same way when processing
words in a sentence? The results of Experiments 1 and 2 are discussed in length at the end of
chapters 4 and 5, respectively. In this chapter, I discuss the most salient topics from each
experiment. Next, I discuss the implications for the models of lexical processing and sentence
processing models. Then, I discuss how the findings from this study can be applied to L2
pedagogy. Finally, I end with a conclusion and discussion of future areas of research.
Summary of Main Findings from Experiment 1
The main research question for this study was how sentence context modulated the
processing of lexical items for L2 Spanish learners and L1 Spanish speakers. Specifically, I was
concerned with two linguistic factors: semantics and syntax. These factors were tested both at
the sentence level and lexical level. Previous studies (Schwartz & Kroll, 2006; Van Hell & De
Groot, 2008) found that the cognate facilitation effect disappeared for high semantic constraint
sentences, but not low semantic constraint sentences. Likewise, Gullifer, Dussias, and Kroll
(2010) found a similar pattern of processing for high syntactic constraint sentences. In other
words, the high constraint sentences yielded selective processing, while the low constraint
sentences showed evidence of non-selective processing. In high constraint sentences, bilinguals
were able to overcome lexical level effects that occur for cognates due to the overlap of
orthography, phonology, and semantics. The constrained sentence level factors (i.e., semantics
or syntax) eliminated these word level facilitation effects. With low constraint sentences, the
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sentence level factors did not override the lexical level cognate facilitation effect. These
findings motivated Experiment 1 where sentences were constrained for high and low semantic
and/or syntactic constraint.
The results from Experiment 1 showed that the intermediate L2 learners were the only
participant group that maintained non-selective processing in all conditions. In other words,
cognates were always processed faster than non-cognates. This finding shows that intermediate
L2 learners had strong lexical connections between their L1 and L2. When the targets had an
overlap of orthography, phonology, and semantics between the L1 and L2, this facilitated
processing. The Revised Hierarchical Model (Kroll & Stewart, 1994) proposes that less
proficient L2 learners maintain strong lexical level connections between the L1 and L2. As
proficiency increases, lexical access gradually depends less on the L1. Since cognates were
processed more quickly in Experiment 1, intermediate L2 learners showed evidence of relying
more on a lexical translation strategy. Due to the L2 being closely linked to the L1, lexical
activation is not selective. Even when the sentences were constrained for both semantics and
syntax, these sentence level factors were unable to overcome the strong lexical level effects.
Therefore, intermediate L2 learners are more bound to lexical level processing.
The other three participant groups, who all had much higher Spanish proficiency, were
processing all sentence conditions, even the low semantic low syntactic constraint sentences,
selectively. The sentence context itself was enough to overcome lexical level effects. This
finding differs from what was found in Schwartz and Kroll (2006), Van Hell and De Groot
(2008), and Gullifer, Dussias, and Kroll (2010). In these studies, only the high constraint
sentences eliminated the cognate facilitation effect, but cognates were processed more quickly
than non-cognates in the low constraint sentences. In the current study, no evidence for
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processing non-selectively was found. It is possible that because these participants were reading
sentences only in Spanish that the sentence context itself served as a cue to effectively “shut
off” their other language. Interestingly, the results from the LDT in isolation showed evidence
of a cognate facilitation effect (i.e., non-selective processing) for all of the groups except for the
native Spanish speakers. The native Spanish speakers were not expected to have a cognate
facilitation effect as they were living in a Spanish-speaking country and had lower levels of
English proficiency. The results from the LDT in isolation are significant because they show
that the very same materials used in Experiment 1 with the sentence context were able to
manifest lexical level effects. This finding strengthens the argument that it is the presence of the
sentence context that made the advanced L2 learners and the Spanish-English bilinguals process
more selectively in Experiment 1.
If we compare the roles of semantics and syntax, we find different processing patterns.
The high semantic constraint sentences in Experiment 1 generally showed facilitatory effects,
not just for the intermediate L2 learners, but also the advanced L2 learners and Spanish-English
bilinguals. Targets were processed more quickly and accurately after high semantic constraint
than low semantic constraint. Semantics, therefore, was used by L2 learners at various levels
and Spanish-English bilinguals to facilitate access to lexical items. Syntax, however, did not
show evidence for the same facilitatory effects. High syntactic constraint did not seem to
facilitate nor interfere with processing for the L2 learners and bilinguals. The overall findings
from Experiment 1 show that semantic and syntactic sentential cues were processed differently.
Task Demands
While there is evidence that the sentence context eliminated the cognate facilitation
effect, it is important to consider the task demands. The task required participants to read a
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sentence for 4 seconds and then after a set time, respond to the target word. Since all
participants, ranging from intermediate L2 learners to native Spanish speakers, had the same
amount of time (4 seconds) to read the entire sentence context, this time may have been too long
for the participants with higher Spanish proficiency. Recall that Libben and Titone (2009) only
found evidence of non-selective processing in the early-stage comprehension measures in eyetracking. It is possible that by having such a long period to read the sentence context, the
window of non-selectivity was closed for the participants with higher Spanish proficiency, and
for this reason no cognate facilitation effect was found. In order to test the task effects, the
sentence could be shown on the screen for a shorter duration (e.g., 3 seconds) to see if less
processing time yields different semantic and syntactic effects. The task could also be changed
to a self-paced reading task where participants read two sentences, as shown in (1):
(1)
Me gusta mucho la música. Yo toco el piano en el recital.
I really like music. I play the piano in the recital.
In this task, two sentences would be shown in order to increase the semantic activation of the
target word. Participants would read each word in a moving-window format. RTs would be
measured for the reading time of the target words. Participants would not know that the target
word is the cognate “piano.” After reading the sentence, they could respond to a comprehension
measure, such as responding whether a picture matches the sentence they just read. By using a
self-paced reading task, participants’ lexical processing in a sentence context can be measured
in a way that does not separate the target word from the sentence.
Another element to the task demands of the LDT is that participants may have
developed a strategy to predict the target word as they were reading the sentence context. Once
participants realized that some of the target words were not semantically related to the sentence
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context, they may have abandoned this strategy, and thus paid less attention to the meaning of
the sentence. The results, however, show that participants were paying attention to meaning, but
it would be worthwhile to analyze the first half of data in comparison to the second half. This
analysis may show more semantic effects. Likewise, participants may have been surprised by
the high syntactic constraint sentences at first, but then adjusted their processing strategy as they
saw the same structure repeated throughout the experiment. Thus, comparing the results from
the first and second halves of the experiment may give more insight into the syntactic effects, as
well.
Summary of Main Findings from Experiment 2
The motivation for Experiment 2 was to examine whether a sentence context could be
used to strengthen the grammatical class cues in lexical processing. Previous studies
(Sunderman & Kroll, 2006; Campbell, 2009) showed that L2 learners of Spanish were sensitive
to grammatical class effects when processing at the lexical level. For example, Campbell (2009)
used a LDT in isolation and found that lexical items with mis-matching grammatical class in the
prime-target word pair were processed more quickly than words with matching grammatical
class. These findings were true even for intermediate L2 learners. Yet, these lexical level
grammatical class effects disappeared when semantics was involved. High semantic similarity
between the word prime and target eliminated grammatical class effects. Thus, L2 learners
seemed to be sensitive to both the grammatical class and semantics of the target word.
In the current study, Experiment 2 asked: could a sentence context be used to strengthen
the grammatical class (e.g., noun) of the last word in the sentence and thus affect processing of
the target word? Besides examining grammatical class, Experiment 2 also investigated the role
of semantics at both the lexical and sentence levels. Sentence contexts were created with high
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and low semantic constraint. Likewise, there was also high and low semantic similarity between
the last word in the sentence context and the target word. Semantics was included in order to
ask: does semantic similarity and semantic sentence constraint override grammatical class
effects? The results from Experiment 2 showed that, unlike Campbell (2009), L2 learners of
Spanish did not show any response time sensitivities for grammatical class, semantic similarity
of the target, nor sentence constraint. In other words, L2 learners with intermediate and
advanced proficiency in Spanish did not respond differently to nouns and verbs and did not
respond differently to target words when they were semantically similar to the last word in the
sentence context nor when the sentence context was semantically constrained to push toward
the target word. Recall that Campbell (2009) used a word prime, instead of a sentence prime.
Thus, the grammatical class of the word prime may have been more salient than the sentence
prime. Although the last word in the sentence always ended with a noun, the grammatical class
of the other words in the sentence may have affected the lexical processing of the target, which
explains why there were no response time sensitivities to grammatical class.
The accuracy data of the L2 learners, however, did show some sensitivities. Intermediate
L2 learners were significantly more accurate responding to verbs than nouns. One explanation
for this finding was that the nouns used in this study were concrete and referred to discrete
entities. The verbs, however, referred to actions or events. Nouns, therefore, may have activated
more lexical items during lexical access due to their concrete meanings. It may have been more
difficult to suppress the activation of nouns if these words had more competition during lexical
activation than verbs. Also, recall that during the norming process, verbs were judged to be
significantly less similar to the targets than nouns. Since the verbs were less similar
semantically, they may have caused less lexical level interference as well, thus causing higher
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accuracy scores. Although this finding was not replicated in the response time data, it is clear
that intermediate L2 learners processed verbs differently than nouns.
The advanced L2 learners had a significant interaction between grammatical class and
semantic similarity in their accuracy data. When the target was a noun, accuracy was higher for
targets with low semantic similarity compared to targets with high semantic similarity. Once
again, semantics seems to interfere with the processing of nouns. When the target was a verb,
targets with both high and low semantic similarity were processed with similarly high
accuracies. As proficiency in the L2 increases, it appears that L2 learners are able to reduce
lexical level interference for nouns if semantic relatedness is low. The intermediate L2 learners
were bound to whether the target was a verb or noun regardless of semantic similarity, but the
advanced L2 learners only showed evidence of lexical level interference for nouns with
increased semantic activation. This finding may show that the advanced L2 learners were able
to process the sentences for meaning, while the processing load may have been too great for the
intermediate L2 learners, resulting in lower accuracies for nouns for all conditions.
The native Spanish speakers and Spanish-English bilinguals did show sensitivities to
grammatical class in their response time data. The native Spanish speakers showed a marginally
significant interaction between grammatical class and semantic similarity. When the target was
a noun, low semantic similarity targets had faster response times than high semantic similarity
targets. As with the accuracy data results from the advanced L2 learners, semantics seems to
interfere with the processing of nouns, but not verbs. The response time data from the SpanishEnglish bilinguals also yielded a significant interaction between grammatical class and semantic
similarity. However, the nature of the interaction was different. In this case, semantics appeared
to facilitate processing for both nouns and verbs, but the facilitation was greater for verbs. The
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Spanish-English bilinguals, therefore, are using semantics to help facilitate processing, unlike
the advanced L2 learners and native Spanish speakers.
None of the different participant groups showed any effects for high and low sentence
constraint. Whether the sentence had high or low semantic constraint did not affect response
times nor accuracies. Semantics at the sentence level, therefore, was not utilized as a cue to
strengthen word level grammatical class effects or increase semantic activation of the target
word. Since both the high and low sentence context conditions each ended with the same last
word, even the low semantic sentence condition still may have primed the target word when the
last word in the sentence and the target were related semantically. The work by Sereno and
Rayner (1992) found that even one word in a sentence context can prime a target, as long as
they are semantically related. For this reason, intralexical priming between the last word in the
sentence and the target may have been sufficient to affect processing, and the other semantic
information provided by the rest of the sentence was not utilized.
Implications for Models of Lexical Processing and Models of Sentence Processing
The goal of this dissertation was to examine how sentence context affected lexical
processing, specifically looking at the factors of semantics and syntax. The BIA+ Model
(Dijkstra & Van Heuven, 2002), which is shown in Figure 6.1, predicts that the presence of a
sentence context may be enough to constrain selectivity. This prediction was confirmed by the
advanced L2 learners and Spanish-English bilinguals. The intermediate L2 learners, however,
were still processing non-selectively for all sentence conditions. Upon reflecting on the findings
from Experiment 1, the BIA+ Model may incorporate a Sentence Context node in the
Identification System. This node could help pre-activate lexical items from one of the
learner’s/bilingual’s Language nodes. This pre-activation may help facilitate selective
126
processing when processing in a sentence context. However, the BIA+ Model must also account
for very dominant L1s, as with the intermediate L2 learners, where the Sentence Context node
would be unable to influence the dominant L1 Language node. Both languages would remain
activated, even when processing words in sentence context, due to the strong lexical level
effects.
Figure 6.1. Revisiting the BIA+ Model
(adapted from Dijkstra & Van Heuven,
2002)
The results from Experiment 2 were less clear: no response time effects were found for
L2 learners, but some sensitivities were found in the accuracy data. Grammatical class encodes
both semantics and syntax. Nouns and verbs serve very different syntactic roles, but it is not
possible to entirely separate these lexical items from their semantics. For example, both the
noun vino “wine” and the verb tomar “to drink” are related semantically to the noun copa
“glass.” Since it is difficult to disentangle effects of grammatical class and semantic similarity,
127
it makes it difficult to tease apart individual grammatical class and semantic effects. Campbell
(2009) argued that the BIA+ Model should incorporate a Grammatical Class node. However,
what still remains unclear is whether this node overlaps with the already existing Semantics
node, or whether it should be a separate entity. Since verbs were responded to more accurately
than nouns by the intermediate L2 learners, this finding gives preliminary evidence that the
Grammatical Class node may be separate from the Semantics node. However, other groups
showed interactions between grammatical class and semantic similarity, suggesting that
grammatical class overlaps or at least interacts with semantics. Due to the nature of the inherent
overlap between grammatical class and semantics, it is difficult to disentangle one from the
other.
The feature restrictions hypothesis (Schwanenflugel & LaCount, 1988; Kellas et al.,
1991) proposes that sentence contexts can be used to generate different types of restrictions to
help process subsequent words at the sentence level. Since high semantic constraint sentences
generate more feature restrictions, only a limited number of lexical items that match the features
given from the sentence context are pre-activated, which means that there is less competition to
access the target word. This finding was confirmed by the facilitatory effects for the targets that
followed high semantic sentences. Interestingly, the feature restrictions hypothesis suggests that
semantics is not the only type of linguistic factor that can restrict activation of subsequent
lexical items. This hypothesis also proposes that syntax may be used to restrict features of
words. The findings from Experiment 1 do not support this hypothesis. The high syntactic
constraint sentences showed no significant effects. Syntax, therefore, does not seem to restrict
the future activation of words in a sentence, at least in this task.
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The results from Experiment 1 also support context-dependent models. The high
semantic constraint sentences helped steer L2 learners and bilinguals towards a possible
interpretation, thus resulting in faster reaction times and accuracies for target words in this
condition. The sentence context, therefore, interacts with lexical processing. The predictability
of the sentence context may have been one factor that facilitated processing (as supported by
Altarriba et al., 1996; Ehrlich & Rayner, 1981). Morris and colleagues also found that
semantically constrained sentence contexts can facilitate lexical processing (Duffy et al., 1994;
Morris, 1994; Morris & Folk, 1998), giving further evidence to support context-dependent
models. Another factor besides predictability that may explain the facilitatory effects of high
semantic sentence contexts is that these sentences prime the target word due to intralexical
priming. In other words, the words in the sentence context with high semantic similarity to the
target word are effectively priming the target word. Past research has suggested that intralexical
priming disappears very quickly, but it appears that within the LDT of Experiment 1, 4 seconds
was enough to generate effects for high semantic constraint at the sentence level. The results
from Experiment 1, therefore, support the idea that sentences constrained for high semantics can
facilitate lexical processing.
When analyzing how semantic and syntactic factors affect lexical processing, the
semantic-mediation model (Wong & Chen, 2012) purports that semantic processing proceeds
syntactic processing when processing words outside of context. In other words, semantics is
resolved before syntax. Folk and Morris (2003), however, found a privileged role for syntax,
such that syntactic category was assigned before meaning resolution when processing in a
sentence context. The results from Experiment 2 showed that the semantics at the sentence level
did not affect lexical processing. Lexical level semantics, however, interfered with the
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processing of nouns for the advanced L2 learners (as shown in the accuracy data) and native
Spanish speakers (as shown in the response time data), but facilitated the processing for both
nouns and verbs for the Spanish-English bilinguals. From these results, it is not clear that
semantics precedes syntactic processing. As mentioned earlier, these findings were not as clear,
which may be due to the overlap between semantics and grammatical class.
Implications for L2 Pedagogy
The findings from this study show that as proficiency in the L2 increases, lexical
processing changes. A significant finding from Experiment 1 is that intermediate L2 learners
process non-selectively even with sentence contexts constrained for semantics and syntax. Thus,
even linguistic factors at the sentence level were unable to overcome lexical level effects when
the L1 was highly dominant. Recall that these intermediate L2 learners were enrolled in a 3000
level Spanish course. These participants were mostly Spanish majors and minors who had taken
Spanish courses for at least four semesters. Even these L2 learners who were enrolled in an
upper level Spanish course were unable to shut off their more dominant L1 when reading
entirely in Spanish. Sunderman and Fancher (2013) discuss how L2 teachers should expect that
their students are unable to shut off their L1. L2 teachers may not be fully aware how even
upper level students are simply unable to shut off their dominant L1. While it is still unclear at
what exact point L2 learners begin to process non-selectively, teachers should not presume that
their L2 learners in upper-level courses are able to turn off their L1. The findings from
Experiment 1 suggest that by the time the L2 learners are graduate students of Spanish (and also
teachers of Spanish), these L2 learners are able to process more selectively.
Another implication based on the findings from Experiment 1 is that sentences with high
semantic constraint can be used as a way to facilitate processing for not only intermediate L2
130
learners, but more advanced L2 learners and balanced bilinguals. Even if these highly
constrained semantic sentences are not helping shut off the more dominant L1 for the
intermediate L2 learners, they are still facilitating lexical processing. When L2 teachers are
presenting new vocabulary to their students, it may be helpful to not have students simply
memorize a list of words, but instead to have L2 teachers present these new lexical items in a
meaningful context. Sunderman and Fancher argue that L2 teachers should use semantically
rich sentences when presenting new vocabulary so that meaningful associations will be
activated when processing lexical items. Future work needs to be completed in the area of L2
vocabulary acquisition in order to confirm that facilitation in processing leads to better
vocabulary retention.
Conclusion and Directions for Future Research
In conclusion, this dissertation has examined how sentence context mediates lexical
processing, specifically with regards to the factors of semantics and syntax. The findings from
these experiments provide more insight into the complexities of bilingual word recognition,
especially in regards to how many factors affect lexical processing. These findings also make
implications for models of both lexical processing and sentence comprehension. Although the
results from this dissertation begin to help answer questions, further research is necessary to
build on these preliminary findings about lexical processing within sentence context.
One future direction is to test different participant groups to gain even more
understanding as to how L2 development affects processing. In particular, it is important to
discover at what point L2 learners begin to process more selectively. A participant group with
higher L2 proficiency than the L2 learners in this study and lower L2 proficiency than the
advanced L2 learners should be tested. For example, participants enrolled in a 4000 level
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undergraduate course or with at least six semesters of Spanish should be investigated.
Furthermore, during testing for this study, it became clear that some of the Spanish-English
bilinguals were native Spanish speakers that grew up in Spanish-speaking countries, while other
participants were native Spanish speakers that grew up in the United States. A group of heritage
Spanish learners (participants matching the latter description) should be tested, as well, to see if
their processing differs from those native Spanish speakers who did not grow up in the United
States.
Another future direction is to manipulate the procedure of Experiment 1. All participants
were given 4 seconds to read the sentence context. Since proficiency levels affect processing
speed, it may be necessary to shorten the window where the sentence is shown on the screen,
especially for the L2 learners with higher Spanish proficiency and for the Spanish-English
bilinguals. Does shortening the window from 4 seconds to 3 seconds modulate effects of nonselectivity? The Task Schema in the BIA+ was created to account for differences in processing
due to the nature of the task. By changing the procedure of the task, more insight will be gained
into how bilinguals with higher proficiency in Spanish process lexical items. Another possibility
is to use a different task altogether. Instead of a LDT, participants could be tested using a selfpaced reading task where each word is shown individually on the screen at a pace controlled by
the participant. RTs would be measured for the target word, which would be shown in the
middle of the sentence, instead of having to wait until processing the entire sentence. The
advantage of this methodology is that it imitates more natural reading processing.
A third direction for future investigation is the role of individual differences. It is clear
that proficiency levels affect lexical processing, but working memory abilities may also play a
role. By having to read a sentence context before responding to a lexical item, the processing
132
demands are greatly increased in comparison to reading words in isolation. For example, in
lexical processing, only one item is held in the participant’s short term memory. Therefore, the
variability of working memory may not affect processing since the demand on working memory
is not as great. In these experiments, however, participants are required to store a whole
sentence in their working memory before responding to the target item. This requires more
demand, especially when the cumulative effect of reading sentence after sentence is considered.
The amount of working memory an individual has to process an entire sentence, especially for
intermediate L2 learners who are slower to process in their L2, may be an important factor for
lexical processing and is worth further investigation.
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APPENDIX A
TRANSLATION RECOGNITION TASK MATERIALS
CRITICAL ITEMS
Spanish Target
acto
afeita
artista
asiento
autobús
barba
basura
bomba
bosque
camino
camisa
comedia
crece
cuaderno
describe
dieta
duda
escuela
figura
garaje
gente
hijo
hotel
humano
insiste
madre
minute
número
oferta
original
padre
pasa
playa
pon
privado
propósito
pueblo
English Translation
act
shave
artist
seat
bus
chin
trash
bomb
forest
road
shirt
comedy
grows
notebook
describe
diet
doubt
school
figure
garage
people
son
hotel
human
insist
mother
minute
number
offer
original
father
pass
beach
put
private
purpose
town
134
puro
rata
sangre
semana
sociedad
tema
Tesoro
tierra
vestido
viaje
vital
pure
rat
blood
week
society
theme
treasure
earth
dress
trip
vital
FILLER ITEMS
Spanish Filler
jabón
cama
perro
frío
carta
corre
oveja
cuerda
gato
lápiz
casa
seda
libre
piensa
espejo
mesa
piso
nariz
azúcar
taza
lleva
conejo
rueda
viejo
dedo
brazo
dinero
película
llave
sale
English Translation
bath
scholar
bone
bad
postal
ball
graze
dry
mouth
pen
mountain
wool
slave
cloud
dressed
step
tiles
style
candy
mussel
hand
fab
steer
pallid
thumb
note
wallet
budget
close
petition
135
ciego
cuarto
pan
cara
reloj
tiza
casado
arregla
nieve
caballo
arroz
coche
puente
ola
cosa
harina
plata
hierva
see
apply
butter
part
time
vigor
boyfriend
strategy
white
love
potato
lord
river
kid
use
plot
wear
liver
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APPENDIX B
LANGUAGE HISTORY QUESTIONNAIRE (ENGLISH)
Language History Questionnaire
This questionnaire is designed to give us a better understanding of your experience with other
languages. We ask that you be as accurate and thorough as possible when answering the
following questions.
General Background Questions:
1. Gender
 Female
 Male
2. Age: ______ years old
3. Do you have any known visual problems (corrected or uncorrected)?
 No
 Yes [Please explain] __________________________________________
4. Native Country
 United States
 Other ___________________
If other, at what age did you come to the US? _________________
Home Language:
5. What is your native language?
 English
 Other: ___________________________________
6. Language spoken at home:
 English
 Spanish
 Other: ___________________________
7. Estimate the amount of time you used Spanish overall in your home growing up: (circle one)
not at all
1
2
3
4
5
6
7
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8
9
all the time
10
8. Estimate the amount of time you used Spanish with your parents in your home growing up:
(circle one)
not at all
1
2
3
4
5
6
7
8
9
all the time
10
9. Estimate the amount of time you used Spanish with your siblings in your home growing up:
(circle one)
not at all
1
2
3
4
5
6
7
8
9
all the time
10
10. Estimate the amount of time you used Spanish with your friends in your home growing up:
(circle one)
not at all
1
2
3
4
5
6
7
8
9
all the time
10
Education:
11. Please indicate where you have studied Spanish.
Please check all that apply and indicate length of study.

High School
 1 year
 2 years
 3 years
 4 years

College
 Less than a one semester
 1-2 semesters
 3-4 semesters
 5-6 semesters
 7-8 semesters
 8+ semesters
Additional Information:
___________________________________________________________________________
___________________________________________________________________________
___________________________________________________________________________
138
12. Are you: (Please check all that apply.)
 Taking Spanish for a requirement and interested in being a major or minor.
 Taking Spanish for a requirement; NOT interested in being a major or minor.
 A Spanish minor
 A Spanish major
Additional Information:
___________________________________________________________________________
___________________________________________________________________________
Experience Abroad:
13. Have you studied / lived abroad in the past? If No continue to question 16)
 Yes
 No
14. If Yes, describe the location dates, length of stay and language of where and when you
studied.
Country
Approx. dates
Length of Stay
Language
Additional Information:
___________________________________________________________________________
___________________________________________________________________________
15. Estimate the amount of time you have spent in a Spanish-speaking country, including
visits to family members, vacations, etc.
Describe the location, dates, length of stay and language of where and when you visited/stayed:
Country
Approx. dates
Length of Stay
Language
139
16. If enrolled in a Spanish course, please circle the appropriate course reference:
SPN 1120
SPN1121
SPN2200
SPN2240
other:_______________
Rate your English Skills:
17. Please rate your English reading proficiency. (1 = not literate and 10 = very literate)
not literate
1
2
3
4
5
6
7
8
9
very literate
10
18. Please rate your English writing proficiency. (1 = not literate and 10 = very literate)
not literate
1
2
3
4
5
6
7
8
9
very literate
10
19. Please rate your English speaking ability. (1 = not fluent and 10 = very fluent)
not fluent
1
2
3
4
5
6
7
8
9
very fluent
10
20. Please rate your English speech comprehension ability. (1 = unable to understand
conversation and 10=perfectly able to understand)
unable to
understand
1
2
3
4
5
6
7
8
9
perfectly able
to understand
10
21. Rate how comfortable you feel expressing yourself in English:
not comfortable at all
1
2
3
4
5
6
7
8
9
very comfortable
10
Rate your Spanish skills:
22. Please rate your Spanish reading proficiency. (1=not literate and 10=very literate)
not literate
1
2
3
4
5
6
7
8
9
very literate
10
23. Please rate your Spanish writing proficiency. (1=not literate and 10=very literate)
not literate
very literate
140
1
2
3
4
5
6
7
8
9
10
24. Please rate your Spanish speaking ability. (1=not fluent and 10=very fluent)
not literate
1
2
3
4
5
6
7
8
9
very literate
10
25. Please rate your Spanish speech comprehension ability. (1=unable to understand
conversation and 10=perfectly able to understand)
unable to
perfectly able
understand
to understand
1
2
3
4
5
6
7
8
9
10
26. Rate how comfortable you feel expressing yourself in Spanish:
not comfortable at all
1
2
3
4
5
6
7
8
9
very comfortable
10
27. Is there anything else that we should know about your language abilities? Other languages
you may speak, etc. Please explain:
______________________________________________________________________________
______________________________________________________________________________
______________________________________________________________________________
Thank you for participating!
141
APPENDIX C
LANGUAGE HISTORY QUESTIONNAIRE (SPANISH)
Cuestionario de las lenguas
Este cuestionario sirve para darnos un mejor entendimiento de tu experiencia con las lenguas. Te
pedimos que contestes las preguntas lo más exacto que puedas.
Preguntas generales:
1. Género
 Mujer
 Hombre
2. Edad: ______ (en años)
3. ¿Tienes algún problema de la visión?
 No
 Sí [Explica, por favor] __________________________________________
4. País de nacimiento
 España
 Otro ___________________
Si es otro, ¿cuándo llegaste a España? _________________
5. ¿Cuál es tu lengua materna?
 Castellano
 Valencià
 Otra: ________________________
6. Lengua que se habla en casa:
 Castellano
 Valencià
 Inglés
 Otra ___________
7. Calcula el tiempo que usabas el inglés en casa cuando eras niño/a. Traza un círculo sobre
un número.
nunca
1
2
3
4
5
6
7
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8
9
todo el tiempo
10
8.
Calcula el tiempo que usabas el inglés con tus padres en casa cuando eras niño/a.
Traza un círculo sobre un número.
nunca
1
2
9.
3
4
5
6
7
8
9
todo el tiempo
10
Calcula el tiempo que usabas el inglés con tus hermanos en casa cuando eras niño/a.
Traza un círculo sobre un número.
nunca
1
2
3
4
5
6
7
8
9
todo el tiempo
10
10. Calcula el tiempo que usabas el inglés con tus amigos cuando eras niño/a. Traza un círculo
sobre un número.
nunca
1
2
3
4
5
6
7
8
9
todo el tiempo
10
Experiencia en otros países:
11. ¿Has vivido o estudiado en otro país en el pasado?
 Sí
 No
12. Si marcaste “sí,” describe el país, las fechas, la duración y la lengua que hablaste.
País
Fechas approx.
Duración
Lengua
Otra información
______________________________________________________________________________
____________________________________________________________________________
Tu competencia en castellano:
17. Considera tu nivel en leer en castellano. (1= no competente y 10 = muy competente)
no competente
1
2
3
4
5
6
7
143
8
9
muy competente
10
18. Considera tu nivel en escribir en castellano. (1 = no competente y 10 = muy competente)
no competente
muy competente
1
2
3
4
5
6
7
8
9
10
19. Considera tu nivel en hablar en castellano. (1 = no competente y 10 = muy competente)
no competente
1
2
3
4
5
6
7
8
9
muy competente
10
20. Considera tu nivel en comprender una conversación en castellano. (1 = no competente y
10 = muy competente)
no competente
1
2
3
4
5
6
7
8
9
muy competente
10
21. ¿Qué nivel de comodidad te sientes cuando te expresas en castellano?
no cómodo
1
2
3
4
5
6
7
8
9
muy cómodo
10
Tu competencia en inglés:
22. Considera tu nivel en leer en inglés. (1= no competente y 10 = muy competente)
no competente
1
2
3
4
5
6
7
8
9
muy competente
10
23. Considera tu nivel en escribir en inglés. (1=no competente y 10 = muy competente)
no competente
1
2
3
4
5
6
7
8
9
muy competente
10
24. Considera tu nivel en hablar en inglés. (1 = no competente y 10 = muy competente)
no competente
1
2
3
4
5
6
7
8
9
muy competente
10
25. Considera tu nivel en comprender una conversación en inglés. (1 = no competente y 10 =
muy competente)
no competente
1
2
3
4
5
6
7
144
8
9
muy competente
10
26. ¿Qué nivel de comodidad te sientes cuando te expresas en inglés?
no cómodo
1
2
3
4
5
6
7
8
muy cómodo
9
10
27. ¿Hay algo más que crees que debemos saber sobre tus experiencias con las lenguas? Por
ejemplo, ¿hablas otras lenguas? Por favor, explícalo:
______________________________________________________________________________
______________________________________________________________________________
___________________________________________________________________________
¡Gracias por tu
participación!
145
APPENDIX D
MATERIALS FOR OFFLINE PROFICIENCY MEASURE
Actividad A (Adaptada de Diplomas de español como lengua extranjera)
En las siguientes oraciones, hay una palabra (en negrita) que es incorrecta. Selecciona la palabra
correcta de la lista y escribe la letra que corresponde a la palabra a la derecha.
1. --¿Viste a Juan?-- --Sí, él vi.
____
2. Ayer estaba viendo la televisión y de momento se fue la luz.
____
3. Pasado mañana fui a una conferencia sobre cultura criolla.
____
4. Ada dijo que quizá ha salido tarde esta noche.
____
5. El avión llegó a Santiago hasta las seis de la tarde.
____
6. No quedan tacos ya que comeremos enchiladas.
____
7. El concierto está en el Palacio de Congresos.
____
8. --Me gustan las faldas.-- --¡Yo también! Alguien llevo todos los días.
____
9. El año pasado hicimos un recorrido hacia toda Europa.
____
10. Mañana no iré a la fiesta porque no conozco a ninguno.
____
a) es
d) por
g) repente
b) las
e) asistiré
h) así
c) nadie
f) lo
i) llegaría
j) sobre
Actividad B (Adaptada de Diplomas de español como lengua extranjera)
Selecciona la palabra correcta para llenar los espacios.
Mafalda es el personaje protagonista de una historieta argentina creada por el dibujante Quino en
1964. Nació en la vida real, 11)__________ ella misma dice en su autobiografía, el 15 de marzo
de 1962.
Es hija de un matrimonio 12)__________ clase media. Su padre es un agente de seguros
146
pero Mafalda no 13)__________ respeta mucho. Su padre se pasa el día haciendo cuentas en el
despacho de su casa para 14)__________ a fin de mes y le encantan las plantas, por lo que las
hormigas son 15)__________ peores enemigas. Su madre fue a la universidad, donde un día
16)__________ al padre de Mafalda pero decidió dejar de estudiar y abandonar la carrera
17)__________ se casaron.
Mafalda tiene un hermano menor, Guille, y una mascota, la tortuga Burocracia, que, al igual que
Mafalda, 18)__________ la sopa.
Al comenzar la historieta Mafalda tiene seis años. Es peleadora, pensadora y soñadora, le
preocupa la humanidad, la paz mundial y la situación social 19)__________ la que se encuentra
su país en los años 60.
20)__________ encantan los Beatles y los dibujos animados del Pájaro Loco. Le gusta
21)__________ leer, escuchar las noticias de la radio, ver la televisión, jugar al ajedrez y correr
al aire libre y cuando 22)__________ mayor quiere ser traductora de la ONU.
Sin 23)__________ duda, Mafalda es el personaje más famoso de las historietas
latinoamericanas pero ella no sería quien es si no fuera 24)__________ sus amigos. Su
25)__________ , Quino, nunca hubiera imaginado que las ideas de esa niña tan ingeniosa iban a
recorrer el mundo traducidas a 26 idiomas.
11. a) como
b) porque
c) cuando
12. a) de
b) con
c) a
13. a) lo
b) se
c) le
14. a) llegar
b) llegando
c) llegado
15. a) suyas
b) su
c) sus
16. a) conocía
b) conoció
c) había conocido
17. a) mientras
b) cuando
c) aunque
18. a) gusta
b) encanta
c) odia
19. a) en
b) para
c) por
20. a) La
b) Se
c) Le
21. a) mucho
b) muy
c) tanto
22. a) será
b) sea
c) es
147
23. a) alguna
b) ninguna
c) cualquier
24. a) para
b) con
c) por
25. a) creador
b) artista
c) ingeniero
148
APPENDIX E
EXPERIMENT 1 NORMING QUESTIONNAIRE
Rate how similar the Spanish word in the main heading is to each of the words or phrases below.
For example, in question 1, rate how similar the meaning of the word "novela" is to the meaning
of the word "leer". Then, rate the similarity of meaning between "novela" and "calle." Continue
with rating the similarity of "novela" and "vender" and then finally "novela" and "biblioteca".
1. novela
leer
calle
vender
biblioteca
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
2. camisa
llevar
verano
comprar
fiesta
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
3. camarero
llamar
restaurante
hablar
veinte minutos
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
4. profesor
escuchar
clase
ver
supermercado
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
5. artista
admirar
dibujar pinturas
buscar
universidad
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
6. abogado
mirar
hablar con juez
conocer
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
149
cine
1
2
3
4
5
6
7
7. película
mirar
cine
prestar
por una semana
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
8. medicina
tomar
hospital
ver
tienda
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
9. cheque
firmar
depositar
enviar
por correo
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
10. premio
ganar
buenas notas
mirar
sala de espera
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
11. caballo
montar
carrera
ver
todos los días
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
12. contador
pagar
ayudar con impuestos
llamar
por teléfono
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
13. elefante
mirar
caminar en el circo
ver
bañándose en el agua
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
14. plato
150
server
mesa de comedor
tirar
suelo
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
15. autobús
coger
llegar al centro
ver
público
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
16. tenedor
usar
comer la pasta
comprar
por un buen precio
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
17. insecto
matar
volar en casa
encontrar
suelo
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
18. artículo
leer
periódico
mirar
pasatiempo
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
19. puente
construir
encima del río
pasar
caminar rápidamente
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
20. imagen
proyectar
pantalla
mirar
anuncio
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
21. corbata
llevar
alrededor del cuello
regular
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
151
cumpleaños
1
2
3
4
5
6
7
22. oficina
limpiar
trabajar hasta las cinco
visitar
descanso
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
23. reloj
verificar
saber la hora
comprar
tienda
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
24. hotel
llamar
confirmar la reservación
visitar
cada año en octubre
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
25. piano
tocar
recital
llevar
escaleras
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
26. sangre
perder
herida
ver
mano
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
27. música
escuchar
concierto
escribir
gente
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
28. avión
pilotar
salir del aeropuerto
vender
no tener dinero
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
29. pastel
152
comer
celebrar cumpleaños
comprar
cliente
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
30. perro
mirar
perseguir al gato
ver
coche
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
31. coche
manejar
calle
pasear
ciudad
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
32. piscina
llenar
con agua cada verano
limpiar
viento
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
33. montaña
escalar
esquiar
ver
afueras
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
34. dentista
observar
limpiar dientes
llamar
buscar trabajo
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
35. hospital
visitar
estar enfermo
encontrar
mapa
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
36. vecino
saludar
salir de casa
robar
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
153
noche
1
2
3
4
5
6
7
37. chocolate
comer
postre
poner
refrigerador
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
38. bolígrafo
usar
escribir la carta
vender
internet
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
39. planeta
ver
telescopio
respetar
belleza
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
40. cerebro
estudiar
psicología
cortar
clase
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
41. examen
tomar
estudiar mucho
completar
viernes por la tarde
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
42. equipaje
facturar
subir al avión
encontrar
garaje
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
43. marinero
ver
barco
oír
decir cuentos
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
44. pasaporte
154
mostrar
aeropuerto
recibir
correo
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
45. banco
robar
sacar dinero
ver
esquina de la calle
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
46. clase
tomar
aprender de la instructora
entender
trabajar mucho
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
47. revista
leer
mirar fotos de celebridades
comprar
tienda cerca de mi casa
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
48. flor
comprar
día de San Valentín
mirar
mesa de comedor
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
49. círculo
dibujar
representar el sol redondo
encontrar
página del libro
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
50. televisión
poner
mirar mi programa favorito
limpiar
quitar el polvo
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
51. edificio
construir
centro comercial
ver
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
155
en la distancia
1
2
3
4
5
6
7
53. sombrero
llevar
para proteger la cabeza
lavar
lavadora
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
54. ventana
abrir
para mirar la vista
romper
usar una pelota de básquetbol
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
55. vino
beber
abrir la botella
seleccionar
cena importante
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
56. actor
admirar
nueva película
recibir
aeropuerto
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
156
APPENDIX F
EXPERIMENT 1 LEXICAL DECISION TASK MATERIALS
Sentence Context:
1. High semantic, low syntactic
2. High semantic, high syntactic
3. Low semantic, low syntactic
4. Low semantic, high syntactic
Target Word
1. Yo leo la ___ en la biblioteca.
La ___ la leo en la biblioteca.
Yo vendo la ___ en la calle.
La ___ la vendo en la calle.
2. Yo admiro al ___ por dibujar sus pinturas.
Al ___ lo admiro por dibujar sus pinturas.
Yo busco al ___ en la universidad.
Al ___ lo busco en la universidad.
3. Yo firmo el ___ antes de depositarlo.
El ___ lo firmo antes de depositarlo.
Yo envié el ___ por correo.
El ___ lo envié por correo.
4. Yo miro al ___ caminando en el circo.
Al ___ lo miro caminando en el circo.
Yo veo al ___ bañándose en el agua.
Al ___ lo veo bañándose en el agua.
5. Yo maté al ___ volando en mi casa.
Al ___ lo maté volando en mi casa.
Yo encontré al ___ en el suelo.
Al ___ lo encontré en el suelo.
6. Yo toco el ___ en el recital.
El ___ lo toco en el recital.
Yo llevo el ___ por las escaleras.
El ___ lo llevo por las escaleras.
7. Yo sirvo el ___ en la mesa del comedor.
El ___ lo sirvo en la mesa del comedor.
Yo tiro el ___ al suelo.
El ___ lo tiro al suelo.
8. Yo leí el ___ en el periódico.
El ___ lo leí en el periódico.
Yo miro el ___ como un pasatiempo.
El ___ lo miro como un pasatiempo.
9. Yo limpio la ___ para terminar antes de las
cinco.
157
Cognate Status
novela
Cognate
artista
Cognate
cheque
Cognate
elefante
Cognate
insecto
Cognate
piano
Cognate
plato
Cognate
artículo
Cognate
oficina
Cognate
La ___ la limpio para terminar antes de las
cinco.
Yo visito la ___ durante el descanso.
La ___ la visito durante el descanso.
10. Yo escalo la ___ para esquiar.
La ___ la escalo para esquiar.
Yo veo la ___ en las afueras.
La ___ la veo en las afueras.
11. Yo como el ___ como de postre.
El ___ lo como de postre.
Yo pongo el ___ en el refrigerador.
El ___ lo pongo en el refrigerador.
12. Yo tomo el ___ después de estudiar mucho.
El ___ lo tomo después de estudiar mucho.
Yo completo el ___ el viernes por la tarde.
El ___ lo completo el viernes por la tarde.
13. Yo entro el ___ para sacar dinero.
Al ___ lo entro para sacar dinero.
Yo veo el ___ en la esquina de la calle.
El ___ lo veo en la esquina de la calle.
14. Yo dibujo el ___ para representar el sol
redondo.
El ___ lo dibujo para representar el sol
redondo.
Yo encuentro un ___ en la página de mi libro.
El ___ lo encuentro en la página de mi libro.
15. Yo cojo el ___ para llegar al centro.
El ___ lo cojo para llegar al centro.
Yo veo el ___ con mis amigos.
El ___ lo veo con mis amigos.
16. Yo escucho la ___ en el concierto.
La ___ la escucho en el concierto.
Yo escribo la ___ para la gente.
La ___ la escribo para la gente.
17. Yo admiro al ___ en la nueva película.
Al ___ lo admiro en la nueva película.
Yo recibí al ___ en el aeropuerto.
Al ___ lo recibí en el aeropuerto.
18. Yo observo al ___ limpiando los dientes.
Al ___ lo observo limpiando los dientes.
Yo llamo al ___ buscando trabajo.
Al ___ lo llamo buscando trabajo.
19. Yo tomo la ___ para aprender de la
profesora.
La ___ la tomo para aprender de la profesora.
Yo entendí la ___ después de trabajar mucho.
158
montaña
Cognate
chocolate
Cognate
examen
Cognate
banco
Cognate
círculo
Cognate
autobús
Cognate
música
Cognate
actor
Cognate
dentista
Cognate
clase
Cognate
La ___ la entendí después de trabajar mucho.
20. Yo tomo el ___ para evitar el tráfico en la
calle.
El ___ lo tomo para evitar el tráfico en la calle.
Yo escucho el ___ pasando cerca de mi casa.
El ___ lo escucho pasando cerca de mi casa.
21. Yo escucho al ___ en la clase.
Al ___ lo escucho en la clase.
Yo veo al ___ en el supermercado.
Al ___ lo veo en el supermercado.
22. Yo tomo la ___ en el hospital.
La ___ la tomo en el hospital.
Yo veo la ___ en la tienda.
La ___ la veo en la tienda.
23. Yo proyecto la ___ en la pantalla.
La ___ la proyecto en la pantalla.
Yo miro la ___ en el anuncio.
La ___ la miro en el anuncio.
24. Yo llamo al ___ para confirmar la
reservación.
Al ___ lo llamo para confirmar la reservación.
Yo visito el ___ cada año en octubre.
El ___ lo visito cada año en octubre.
25. Yo visito el ___ por estar muy enfermo.
El ___ lo visito por estar muy enfermo.
Yo encuentro el ___ en el mapa.
El ___ lo encuentro en el mapa.
26. Yo veo el ___ por el telescopio.
El ___ lo veo por el telescopio.
Yo respeto el ___ por toda su belleza.
El ___ lo respeto por toda su belleza.
27. Yo muestro el ___ en el aeropuerto.
El ___ lo muestro en el aeropuerto.
Yo recibí el ___ por correo ayer.
El ___ lo recibí por correo ayer.
28. Yo pongo la ___ para mirar mi programa
favorito.
La ___ la pongo para mirar mi programa
favorito.
Yo limpio la ___ para quitar polvo.
La ___ la limpio para quitar polvo.
29. Yo llevo la ___ alrededor del cuello.
La ___ la llevo alrededor del cuello.
Yo regalé la ___ para el cumpleaños.
La ___ la regalé para el cumpleaños.
159
tren
Cognate
profesor
Cognate
medicina
Cognate
imagen
Cognate
hotel
Cognate
hospital
Cognate
planeta
Cognate
pasaporte
Cognate
televisión
Cognate
corbata
Non-cognate
30. Yo como el ___ para celebrar mi
cumpleaños.
El ___ lo como para celebrar mi cumpleaños.
Yo compro el ___ para el cliente.
El ___ lo compro para el cliente.
31. Yo lleno la ___ con agua cada verano.
La ___ la lleno con agua cada verano.
Yo limpio la ___ después del viento.
La ___ la limpio después del viento.
32. Yo saludo al ___ saliendo de su casa.
Al ___ lo saludo saliendo de su casa.
Yo robo al ___ por la noche.
Al ___ lo robo por la noche.
33. Yo estudio el ___ en psicología.
El ___ lo estudio en psicología.
Yo corto el ___ en clase.
El ___ lo corto en clase.
34. Yo veo al ___ en el barco.
Al ___ lo veo en el barco.
Yo oigo al ___ contando cuentos.
Al ___ lo oigo contando cuentos.
35. Yo leo la ___ mirando las fotos de las
celebridades.
La ___ la leo mirando las fotos de las
celebridades.
Yo compro la ___ en la tienda cerca de mi casa.
La ___ la compro en la tienda cerca de mi casa.
36. Yo construí el ___ en el centro comercial.
El ___ lo construí en el centro comercial.
Yo veo el ___ en la distancia.
El ___ lo veo en la distancia.
37. Yo llevo la ___ en el verano.
La ___ la llevo en el verano.
Yo compro la ___ para la fiesta.
La ___ la compro para la fiesta.
38. Yo veo al ___ hablando con el juez.
Al ___ lo veo hablando con el juez.
Yo conocí al ___ en el cine.
Al ___ lo conocí en el cine.
39. Yo gano el ___ por mis buenas notas.
El ___ lo gano por mis buenas notas.
Yo miro el ___ en la sala de espera.
El ___ lo miro en la sala de espera.
40. Yo pierdo la ___ de la herida.
La ___ la pierdo de la herida.
Yo veo la ___ en la mano.
160
pastel
Non-cognate
piscina
Non-cognate
vecino
Non-cognate
cerebro
Non-cognate
marinero
Non-cognate
revista
Non-cognate
edificio
Non-cognate
camisa
Non-cognate
abogado
Non-cognate
premio
Non-cognate
sangre
Non-cognate
La ___ la veo en la mano.
41. Yo miro al ___ persiguiendo al gato.
Al ___ lo miro persiguiendo al gato.
Yo veo al ___ en el coche.
Al ___ lo veo en el coche.
42. Yo llevo el ___ para proteger la cabeza.
El ___ lo llevo para proteger la cabeza.
Yo lavo el ___ en la lavadora.
El ___ lo lavo en la lavadora.
43. Yo llamo al ___ en el restaurante.
Al ___ lo llamo en el restaurante.
Yo miré al ___ por veinte minutos.
Al ___ lo miré por veinte minutos.
44. Yo veo la ___ en el cine.
La ___ la veo en el cine.
Yo presto la ___ por una semana.
La ___ la presto por una semana.
45. Yo monto el ___ antes de la carrera.
El ___ lo monto antes de la carrera.
Yo veo el ___ todos los días.
El ___ lo veo todos los días.
46. Yo construí el ___ encima del río.
El ___ lo construí encima del río.
Yo paso el ___ caminando rápidamente.
El ___ lo paso caminando rápidamente.
47. Yo verifico el ___ para saber la hora.
El ___ lo verifico para saber la hora.
Yo compro el ___ en la tienda.
El ___ lo compro en la tienda.
48. Yo uso el ___ para escribir la carta.
El ___ lo uso para escribir la carta.
Yo vendo el ___ por Internet.
El ___ lo vendo por Internet.
49. Yo facturé el ___ antes de subir al avión.
El ___ lo facturé antes de subir al avión.
Yo encontré el ___ en el garaje.
El ___ lo encontré en el garaje.
50. Yo abro la ___ para ver la vista.
La ___ la abro para ver la vista.
Yo rompo la ___ con una pelota de básquetbol.
La ___ la rompo con una pelota de básquetbol.
51. Yo pago al ___ por ayudarme con los
impuestos.
Al ___ lo pago por ayudarme con los
impuestos.
Yo llamo al ___ por teléfono.
161
perro
Non-cognate
sombrero
Non-cognate
camarero
Non-cognate
película
Non-cognate
caballo
Non-cognate
puente
Non-cognate
reloj
Non-cognate
bolígrafo
Non-cognate
equipaje
Non-cognate
ventana
Non-cognate
contador
Non-cognate
Al ___ lo llamo por teléfono.
52. Yo uso el ___ para comer pasta.
El ___ lo uso para comer pasta.
Yo compro el ___ por un buen precio.
El ___ lo compro por un buen precio.
53. Yo piloto el ___ para salir del aeropuerto.
El ___ lo piloto para salir del aeropuerto.
Yo vendo el ___ por no tener dinero.
El ___ lo vendo por no tener dinero.
54. Yo manejo el ___ en la calle.
El ___ lo manejo en la calle.
Yo paseo el ___ por la ciudad.
El ___ lo paseo por la ciudad.
55. Yo compro la ___ para el día de San
Valentín.
La ___ la compro para el día de San Valentín.
Yo veo la ___ en la mesa del comedor.
La ___ la veo en la mesa del comedor.
56. Yo bebo el ___ después de abrir la botella.
El ___ lo bebo después de abrir la botella.
Yo selecciono el ___ para una cena importante.
El ___ lo selecciono para una cena importante.
tenedor
Non-cognate
avión
Non-cognate
coche
Non-cognate
flor
Non-cognate
vino
Non-cognate
FILLER ITEMS
Sentence context
1. Yo veo el ___ en el parque zoológico.
2. Yo traigo la ___ a la casa de mi amiga.
3. Al ___ lo conocí examinando las estrellas.
4. Al ___ lo vi en su oficina.
5. Yo usa la ___ para sacar fotos.
6. Yo observo al ___ caminando en la selva.
7. Al ___ lo temo respirando el fuego.
8. La ___ la estudio en la universidad.
9. Yo gasto el ___ comprando las papitas.
10. Yo vendo el ___ en la bodega.
11. La ___ la encendí para destruir el edificio.
12. El ___ lo veo en el dormitorio.
13. Yo escuché la ___ de la bomba.
14. Yo oigo la ___ por la mañana.
15. Al ___ lo veo explorando ciudades nuevas.
16. El ___ lo tengo en mi mochila.
17. Yo hablo el ___ más común de este idioma.
18. Yo como la ___ para mejorar la salud.
19. Al ___ lo escuché describiendo el crimen.
162
Spanish non-word
anomal
biciclefa
astróbomo
empleago
cádara
chimlancé
fragón
constegación
dómar
dialante
didamita
sadio
exflosión
frompeta
tudista
diccionadio
idiola
hierfa
gestigo
20. El ___ lo pago cada año en abril.
21. Yo muevo el ___ para sentarme cerca de la tele.
22. Yo apago la ___ antes de salir de la casa.
23. La ___ la conquisté en la batalla final.
24. El ___ lo dije después de cenar.
25. Yo compro el ___ por un precio razonable.
26. Yo compro el ___ en la tienda.
27. El ___ lo preparo para comer por la mañana.
28. El ___ lo compro todos los días.
29. Yo entro la ___ para evitar el hambre.
30. Yo veo a la ___ caminando por la tarde.
31. El ___ lo uso para cambiar respuestas en el examen.
32. El ___ lo perdí después de volver al hotel.
33. Yo uso el ___ para cortar la carne.
34. Yo examino la ___ en el laboratorio.
35. La ___ la trabajo cultivando las verduras.
36. La ___ la temo por su misterio.
37. Yo limpio el ___ para hacerlo muy brillante.
38. Yo sirvo el ___ en la casa.
39. El ___ lo temo por vivir en la selva.
40. El ___ lo como por la tarde.
41. Yo oigo el ___ durante la cirugía.
42. Yo conocí al ___ en la iglesia.
43. El ___ lo escribo para la clase de literatura.
44. Al ___ lo conocí en el bar.
45. Yo veo al ___ con un libro en la biblioteca.
46. Yo pido el ___ para la sopa.
47. El ___ lo tejo para el invierno.
48. El ___ lo vendí para ganar dinero.
49. Yo uso el ___ para comprar una casa.
50. Yo veo el ___ en el árbol.
51. El ___ lo uso para viajar por la ciudad.
52. La ___ la tomo de mi madre.
53. Yo encuentro el ___ entre las montañas.
54. Yo corto la ___ por la mañana.
55. El ___ lo fertilizo en el patio.
56. La ___ la cocino para la cena.
163
imcuesto
pillón
plincha
tarre
choste
sarco
craje
huego
almuerbo
cafebería
mareja
barrador
bolejo
cachillo
lariposa
tiorra
selda
gasco
cebeal
pigre
quepo
lorazón
joten
poela
cófico
estubiante
aguafate
suéber
guadro
hinero
cavario
lapa
fruva
nalle
mafera
jarbín
tormuga
APPENDIX G
EXPERIMENT 2 NORMING QUESTIONNAIRES
QUESTIONNAIRE, VERSION A: The purpose of this survey is to assign semantic similarity
scores to Spanish words. "Semantic Similarity" is the strength of a relationship between two
ideas or concepts.
For instance, in English, the words "couch" and "sofa" are highly semantically related - they
basically mean the same thing. The words "truck" and "bus" are also semantically related. They
are similar: both are large vehicles, transport people, etc. But they are slightly different: a truck is
smaller and holds less people. Therefore, "couch-sofa" has a higher semantic similarity rating
than "truck-bus."
The words "mouse" and "cheese," however, although they often occur together, are NOT
semantically similar. We often associate these two words together, but they do not share features.
A mouse is NOT similar to cheese in any way - a mouse is an animal, but cheese is a dairy
product.
Below you will see a list of Spanish words. Rate how semantically similar the Spanish word in
the main heading is to each of the words below.
For example, in question 1, rate how semantically similar the word "vaso" is with "copa". Then
rate the semantic similarity between "vaso" and "decidir." Continue rating the relationship
between "vaso" and "estrella" and finally, "vaso" and "beber."
1. vaso
copa
decidir
estrella
beber
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
2. queso
leche
comer
flor
encontrar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
3. azúcar
pastel
cocinar
rosa
asistir
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
164
4. barco
canoa
navegar
oveja
empujar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
5. edificio
biblioteca
construir
nariz
crear
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
6. equipaje
maleta
viajar
servilleta
adoptar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
7. vino
viña
tomar
novela
firmar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
8. cuarto
alcoba
entrar
bombilla
querer
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
9. noticiero
noticias
informar
llegada
perdonar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
10. músculo
ejercicio
estirar
cebolla
gritar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
11. comedia
chiste
1
2
3
4
5
6
7
165
reír
araña
jurar
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
12. deseo
necesidad
realizar
periodista
prometer
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
13. doctorado
enseñanza
enseñar
torre
perder
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
14. silla
asiento
sentarse
pista
mentir
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
15. botella
taza
servir
aceite
comprar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
16. tijeras
cuchillo
cortar
cama
matar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
17. tierra
césped
plantar
espejo
olvidar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
18. actriz
película
mirar
arroz
compartir
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
166
19. playa
mar
bucear
traje
saltar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
20. escuela
maestro
aprender
precio
publicar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
21. basura
reciclaje
tirar
cerdo
renunciar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
22. boda
esposo
casarse
jabón
negar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
23. caballo
caballero
montar
testigo
sufrir
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
24. computadora
teclado
conectar
toro
definir
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
25. tiza
pizarra
escribir
senador
animar
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26. fiesta
cumpleaños
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bailar
otoño
cubrir
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27. vecindad
vecino
quedarse
pavo
recibir
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28. lluvia
nube
caer
bistec
salvar
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29. naranja
manzana
madurar
madera
ofrecer
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30. muñeca
pelota
jugar
fotografía
visitar
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1
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31. canción
cantante
cantar
prima
ocurrir
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32. falda
ropa
llevar
conejo
mejorar
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2
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3
3
3
3
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4
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33. ventana
vidrio
limpiar
dedo
discutir
1
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2
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2
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3
3
3
3
4
4
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34. iglesia
capilla
rezar
fábrica
eliminar
1
1
1
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2
2
2
2
3
3
3
3
4
4
4
4
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6
6
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7
7
35. ganga
rebaja
vender
revista
destruir
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
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5
5
6
6
6
6
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7
7
7
36. boca
labio
besar
literatura
avisar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
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5
5
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6
6
6
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7
7
37. farmacia
pastilla
recetar
pareja
conservar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
38. montaña
colina
escalar
conquista
descubrir
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
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7
7
7
39. muerte
tumba
morir
zapatillas
obedecer
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
40. guerra
lucha
luchar
moda
odiar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
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5
5
6
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6
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41. camión
carro
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2
3
4
5
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7
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conducir
pecas
dormir
1
1
1
2
2
2
3
3
3
4
4
4
5
5
5
6
6
6
7
7
7
42. delfín
tiburón
nadar
cena
romper
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
43. colegio
tarea
evaluar
vestido
sonreír
1
1
1
1
2
2
2
2
3
3
3
3
4
4
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4
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44. libro
cuaderno
leer
armario
esperar
1
1
1
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2
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2
2
3
3
3
3
4
4
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45. novio
marido
amar
borde
quemar
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2
2
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3
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3
4
4
4
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46. partido
estadio
competir
almuerzo
juzgar
1
1
1
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2
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2
2
3
3
3
3
4
4
4
4
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7
7
47. empleado
gerente
emplear
sujeto
subir
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
7
7
7
7
48. pulmón
corazón
respirar
televisor
comenzar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
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6
6
6
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7
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7
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49. trabajo
quehacer
trabajar
marzo
valer
1
1
1
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2
2
2
2
3
3
3
3
4
4
4
4
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7
50. bebé
nacimiento
nacer
dibujo
repetir
1
1
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2
2
3
3
3
3
4
4
4
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7
7
7
51. programa
telenovela
entretener
terremoto
acabar
1
1
1
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2
2
2
2
3
3
3
3
4
4
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4
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6
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7
52. apartamento
alquiler
alquilar
polvo
robar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
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7
53. gato
perro
perseguir
abrigo
morir
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
6
6
6
6
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7
7
7
54. dinero
ahorros
gastar
pantalones
preferir
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
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5
5
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7
55. puerta
llave
cerrar
mente
empezar
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
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56. caja
regalo
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2
3
4
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7
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empacar
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2
3
4
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6
7
peligro
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7
confiar
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2
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7
For the following section, read each sentence below. Rate how well the sentence predicts the last
word in the sentence by marking on the scale (from "sentence does NOT predict last word" to
"sentence STRONGLY predicts last word").
For example, in a sentence such as “Para hacer mi tarea, escribo con un lápiz,” the sentence
strongly predicts the last word to be “lápiz” because it is very typical to write with a pencil when
doing homework. On the other hand, a sentence like “Me gusta mucho caminar con mis amigos”
does not predict the word “amigos” because there are many words (e.g., padres, hermanos,
primos) that could also logically fit in the blank.
Cuando se comportaban mal, los niños
rompieron el vaso.
Cuando caminé en el mercado, yo
compré el queso.
En el campo los rancheros cultivan el
azúcar.
Yo vi a muchas personas relajándose
en el barco.
Después de estar perdido, finalmente
encontré el edificio.
En las tiendas en el centro comercial yo
miro el equipaje.
Para mi cumpleaños mi amigo me dio
el vino.
El cliente pregunta al empleado por el
cuarto.
En mi cuarto oigo en la distancia el
noticiero.
Después de trabajar yo estiro los
músculos.
En el cine los amigos vieron la
comedia.
Estoy triste porque vivo una vida sin
deseo.
Este año en el diciembre voy a recibir
el doctorado.
Al pasar al almacén por la mañana yo
veo la silla.
Antes de salir de casa en el suelo tiro la
botella.
Ayer yo compré por Internet las tijeras.
Yo camino cada día por la tierra.
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Yo estoy muy alegre porque conocí a la
actriz.
Yo como helado cuando camino en la
playa.
Yo conocí a muchos amigos este año
en la escuela.
En la calle, yo siempre trato de recoger
la basura.
Por la tarde yo miro a las personas en la
boda.
En el libro los niños miraron la imagen
del caballo.
El mundo está regalado por la
computadora.
Cuando jugaban, los niños rompieron
toda la tiza.
Cada semana yo cocino muchas
galletas para la fiesta.
Los martes por la noche yo camino por
la vecindad.
Por la noche en mi casa escucho el
sonido de la lluvia.
Cuando era niña, la fruta que siempre
comía era la naranja.
Yo hago un vestido pequeño para la
muñeca.
En el concierto yo escucho la canción.
Para vestirme más a la moda yo llevo la
falda.
Cuando hace calor en casa, abro la
ventana.
Cada domingo yo rezo y canto en la
iglesia.
Cuando fui de compras toda la ropa era
una ganga.
Los dientes y la lengua forman parte de
la boca.
Después de visitar al doctor, yo voy a
la farmacia.
Cuando quiero esquiar, yo subo a la
montaña.
En el matadero se puede oler la muerte.
Tuve que huir como un refugiado
cuando empezó la guerra.
Para transportar cosas grandes, uso mi
camión.
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7
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173
El mamífero marino más inteligente
podría ser el delfín.
Empezando en el otoño los estudiantes
asistirán al colegio.
En la biblioteca de la universidad yo
busco el libro.
La chica compró un regalo romántico
para su novio.
No tengo un equipo favorito en el
partido.
Por teléfono yo digo los requisitos del
trabajo al empleado.
Después de correr, me duele el pulmón.
Para ganar la vida es necesario tener el
trabajo.
Por la noche, está llorando en la cuna el
bebé.
Ayer en el festival de guitarra se
mostró el programa.
Los estudiantes de la universidad viven
en el apartamento.
Corriendo por el campo el perro caza al
gato.
Después de cerrar el banco cuenta el
dinero.
Cuando regresé a mi casa ayer, abrí la
puerta.
Cuando me mudé, mi madre me envió
mis cajas.
Después de cerrar el banco cuenta el
dinero.
Cuando regresé a mi casa ayer, abrí la
puerta.
Cuando me mudé, mi madre me envió
mis cajas.
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7
QUESTIONNAIRE, VERSION B: (the first part was the same, the only difference was the
sentence ratings)
For the following section, read each sentence below. Rate how well the sentence predicts the last
word in the sentence by marking on the scale (from "sentence does NOT predict last word" to
"sentence STRONGLY predicts last word").
For example, in a sentence such as “Para hacer mi tarea, escribo con un lápiz,” the sentence
strongly predicts the last word to be “lápiz” because it is very typical to write with a pencil when
174
doing homework. On the other hand, a sentence like “Me gusta mucho caminar con mis amigos”
does not predict the word “amigos” because there are many words (e.g., padres, hermanos,
primos) that could also logically fit in the blank.
Cuando tengo sed tomo agua de un
vaso.
Para hacer una pizza muy buena, yo
pongo el queso.
Para un café delicioso, añado el azúcar.
Para cruzar el océano manejé el barco.
En la ciudad, escuché a los obreros
trabajando en el edificio.
En el aeropuerto y el avión llevo el
equipaje.
Por la noche y con tapas yo tomo el
vino.
Después de comer con la familia me
acuesto en el cuarto.
En la televisión yo veo por la noche el
noticiero.
Me duele la pierna porque me he roto
los músculos.
La película que era muy cómica es una
comedia.
Estoy muy contento porque cumplí el
deseo.
Por muchos años estudié para
conseguir el doctorado.
Prefiero no estar de pie, por eso
necesito encontrar una silla.
Cuando hace calor bebo agua de una
botella.
Cada tres meses, yo corto el pelo con
las tijeras.
Yo descubrí los fósiles en la tierra.
Yo voy al cine para ver la obra de la
actriz.
Yo llevo el traje de baño cuando nado
en la playa.
Yo aprendo ciencias cuando asisto a la
escuela.
Cuando se acumulan los papeles, yo
saco la basura.
Los novios se dan los anillos en la
boda.
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175
El jockey ganó la carrera con la ayuda
de su caballo.
Navego por Internet usando la
computadora.
La profesora escribe en la pizarra con
la tiza.
Yo invito a muchas personas a la casa
para la fiesta.
Yo protejo las casas del crimen en la
vecindad.
Durante la tormenta además del viento
se puede oír la lluvia.
En el mercado los domingos yo compro
la naranja.
Por tener buenas notas ella recibió la
muñeca.
Paseando por el parque Juan oye la
canción.
Cuando yo voy al centro comercial
compro la falda.
Cada mes, el chico tiene que limpiar la
ventana.
Compré el pan después de salir de la
iglesia.
En la calle toda la comida que se
compra es una ganga.
La mayor parte del tiempo la gente usa
la boca.
Cuando me gradúe, deseo tener un
empleo en la farmacia.
Sería una vida difícil viviendo en la
montaña.
A los niños no les gusta la muerte.
Como niño, leía en los libros historias
de la guerra.
Cerca de la playa yo escucho el sonido
del camión.
Hay muchas personas que trabajan
entrenando al delfín.
Los obreros están modificando los
edificios del colegio.
En la tienda cerca de mi casa creo que
se vende el libro.
Ella vio a una chica describiendo su día
a su novio.
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176
Cuando esté disponible en la tele veré
el partido.
Afuera del edificio en su coche yo veo
al empleado.
Tenía mala salud cuando recibí cirugía
en el pulmón.
Necesitamos un grupo animado para el
trabajo.
Ella tiene mucha responsabilidad con el
bebé.
Antes de salir el fin de semana yo
compruebo el programa.
Detrás del restaurante italiano está
ubicado el apartamento.
En el silencio de la mañana oigo al
gato.
Yo presto poca atención al dinero.
Después de muchos años, finalmente
pinté la puerta.
Antes de salir para mi trabajo, encontré
las cajas.
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
1
2
2
3
3
4
4
5
5
6
6
7
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
177
APPENDIX H
EXPERIMENT 2 LEXICAL DECISION TASK MATERIALS
CRITICAL ITEMS
Sentence Context
+GC +SS
-GC +SS
+GC -SS
-GC -SS
1. +Sem: Cuando tengo sed tomo agua
de un vaso.
copa
beber
estrella
decidir
leche
comer
flor
encontrar
pastel
cocinar
rosa
asistir
canoa
navegar
oveja
empujar
biblioteca
construir
nariz
crear
maleta
viajar
servilleta
adoptar
viña
tomar
novela
firmar
-Sem: Cuando se comportaban mal,
los niños rompieron el vaso.
2. +Sem: Para hacer una pizza muy
buena, yo pongo el queso.
-Sem: Cuando caminé en el mercado,
yo compré el queso.
3. +Sem: Para un café delicioso, añado
el azúcar.
-Sem: En el campo los rancheros
cultivan el azúcar.
4. +Sem: Para cruzar el océano manejé
el barco.
-Sem: Yo vi a muchas personas
relajándose en el barco.
5. +Sem: En la ciudad, escuché a los
obreros trabajando en el edificio.
-Sem: Después de estar perdido,
finalmente encontré el edificio.
6. +Sem: En el aeropuerto y el avión
llevo el equipaje.
-Sem: En las tiendas en el centro
comercial yo miro el equipaje.
7. +Sem: Por la noche y con tapas yo
tomo el vino.
178
-Sem: Para mi cumpleaños mi amigo
me dio el vino.
8. +Sem: Después de comer con la
familia me acuesto en el cuarto.
alcoba
entrar
bombilla
querer
noticias
informar
llegada
perdonar
ejercicio
estirar
cebolla
gritar
chiste
reír
araña
jurar
necesidad
realizar
periodista
prometer
enseñanza
enseñar
torre
perder
asiento
sentarse
pista
mentir
taza
servir
aceite
comprar
-Sem: El cliente pregunta al
empleado por el cuarto.
9. +Sem: En la televisión yo veo por la
noche el noticiero.
-Sem: En mi cuarto oigo en la
distancia el noticiero.
10. +Sem: Me duele la pierna porque
me he roto los músculos.
-Sem: Después de trabajar yo estiro
los músculos.
11. +Sem: La película que era muy
cómica es una comedia.
-Sem: En el cine los amigos vieron
la comedia.
12. +Sem: Estoy muy contento porque
cumplí el deseo.
-Sem: Estoy triste porque vivo una
vida sin deseo.
13. +Sem: Por muchos años estudié
para conseguir el doctorado.
-Sem: Este año en el diciembre voy a
recibir el doctorado.
14.+Sem: Prefiero no estar de pie, por
eso necesito encontrar una silla.
-Sem: Al pasar al almacén por la
mañana yo veo la silla.
15. +Sem: Cuando hace calor bebo
agua de una botella.
-Sem: Antes de salir de casa en el
suelo tiro la botella.
179
16. +Sem: Cada tres meses, yo corto el
pelo con las tijeras.
cuchillo
cortar
cama
matar
césped
plantar
espejo
olvidar
película
mirar
llave
compartir
mar
bucear
traje
saltar
maestro
aprender
precio
publicar
reciclaje
tirar
cerdo
renunciar
esposo
casarse
jabón
negar
caballero
montar
testigo
sufrir
teclado
conectar
toro
definir
-Sem: Ayer yo compré por Internet
las tijeras.
17.+Sem: Yo descubrí los fósiles en la
tierra.
-Sem: Yo camino cada día por la
tierra.
18. +Sem: Yo voy al cine para ver la
obra de la actriz.
-Sem: Yo estoy muy alegre porque
conocí a la actriz.
19. +Sem: Yo llevo el traje de baño
cuando nado en la playa.
-Sem: Yo como helado cuando
camino en la playa.
20. +Sem: Yo aprendo ciencias cuando
asisto a la escuela.
-Sem: Yo conocí a muchos amigos
este año en la escuela.
21. +Sem: Cuando se acumulan los
papeles, yo saco la basura.
-Sem: En la calle, yo siempre trato
de recoger la basura.
22. +Sem: Los novios se dan los anillos
en la boda.
-Sem: Por la tarde yo miro a las
personas en la boda.
23. +Sem: El jockey ganó la carrera
con la ayuda de su caballo.
-Sem: En el libro los niños miraron la
imagen del caballo.
24. +Sem: Navego por Internet usando
la computadora.
180
-Sem: El mundo está regalado por la
computadora.
25. +Sem: La profesora escribe en la
pizarra con la tiza.
pizarra
escribir
senador
animar
cumpleaños
bailar
otoño
cubrir
vecino
quedarse
pavo
recibir
nube
caer
bistec
salvar
manzana
madurar
madera
ofrecer
pelota
jugar
fotografía
visitar
cantante
cantar
prima
ocurrir
ropa
llevar
conejo
mejorar
-Sem: Cuando jugaban, los niños
rompieron toda la tiza.
26. +Sem: Yo invito a muchas personas
a la casa para la fiesta.
-Sem: Cada semana yo cocino
muchas galletas para la fiesta.
27. +Sem: Yo protejo las casas del
crimen en la vecindad.
-Sem: Los martes por la noche yo
camino por la vecindad.
28. +Sem: Durante la tormenta además
del viento se puede oír la lluvia.
-Sem: Por la noche en mi casa
escucho el sonido de la lluvia.
29. +Sem: Cuando era niña, la fruta
que siempre comía era la naranja.
-Sem: En el mercado los domingos
yo compro la naranja.
30. +Sem: Yo hago un vestido pequeño
para la muñeca.
-Sem: Por tener buenas notas ella
recibió la muñeca.
31. +Sem: En el concierto yo escucho
la canción.
-Sem: Paseando por el parque Juan oye
la canción.
32. +Sem: Para vestirme más a la moda
yo llevo la falda.
-Sem: Cuando yo voy al centro
comercial compro la falda.
181
33. +Sem: Cuando hace calor en casa,
abro la ventana.
vidrio
limpiar
dedo
discutir
capilla
rezar
fábrica
eliminar
rebaja
vender
revista
destruir
labio
besar
literatura
avisar
pastilla
recetar
pareja
conservar
colina
escalar
conquista
descubrir
tumba
morir
zapatillas
obedecer
lucha
luchar
moda
odiar
carro
conducir
pecas
dormir
-Sem: Cada mes, el chico tiene que
limpiar la ventana.
34. +Sem: Cada domingo yo rezo y
canto en la iglesia.
-Sem: Compré el pan después de salir
de la iglesia.
35. +Sem: Cuando fui de compras toda
la ropa era una ganga.
-Sem: En la calle toda la comida que
se compra es una ganga.
36. +Sem: Los dientes y la lengua
forman parte de la boca.
-Sem: La mayor parte del tiempo la
gente usa la boca.
37. +Sem: Después de visitar al doctor,
yo voy a la farmacia.
-Sem: Cuando me gradúe, deseo
tener un empleo en la farmacia.
38. +Sem: Cuando quiero esquiar, yo
subo a la montaña.
-Sem: Sería una vida difícil
viviendo en la montaña.
39. +Sem: En el matadero se puede oler
la muerte.
-Sem: A los niños no les gusta la
muerte.
40. +Sem: Tuve que huir como un
refugiado cuando empezó la guerra.
-Sem: Como niño, leía en los libros
historias de la guerra.
41. +Sem: Para transportar cosas
grandes, uso mi camión.
182
-Sem: Cerca de la playa yo escucho
el sonido del camión.
42. +Sem: El mamífero marino más
inteligente podría ser el delfín.
tiburón
nadar
cena
romper
tarea
evaluar
vestido
sonreír
cuaderno
leer
armario
esperar
marido
amar
borde
quemar
estadio
competir
almuerzo
juzgar
gerente
emplear
sujeto
subir
corazón
respirar
televisor
comenzar
quehacer
trabajar
marzo
valer
-Sem: Hay muchas personas que
trabajan entrenando al delfín.
43. +Sem: Empezando en el otoño los
estudiantes asistirán al colegio.
-Sem: Los obreros están
modificando los edificios del colegio.
44. +Sem: En la biblioteca de la
universidadyo busco el libro.
-Sem: En la tienda cerca de mi
casacreo que se vende el libro.
45. +Sem: La chica compró un regalo
romántico para su novio.
-Sem: Ella vio a una chica
describiendo su día a su novio.
46. +Sem: No tengo un equipo favorito
en el partido.
-Sem: Cuando esté disponible en la
tele veré el partido.
47. +Sem: Por teléfono yo digo los
requisitos del trabajo al empleado.
-Sem: Afuera del edificio en su
coche yo veo al empleado.
48. +Sem: Después de correr, me duele
el pulmón.
-Sem: Tenía mala salud cuando
recibí cirugía en el pulmón.
49. +Sem: Para ganar la vida es
necesario tener el trabajo.
-Sem: Necesitamos un grupo
animado para el trabajo.
183
50. +Sem: Por la noche, está llorando
en la cuna el bebé.
nacimiento
nacer
dibujo
repetir
telenovela
entretener
terremoto
acabar
alquiler
alquilar
polvo
robar
perro
perseguir
abrigo
morir
ahorros
gastar
pantalones
preferir
llave
cerrar
mente
empezar
regalo
empacar
peligro
confiar
-Sem: Ella tiene mucha
responsabilidad con el bebé.
51. +Sem: Ayer en el festival de
guitarra se mostró el programa.
-Sem: Antes de salir el fin de semana
yo compruebo el programa.
52. +Sem: Los estudiantes de la
universidad viven en el apartamento.
-Sem: Detrás del restaurante italiano
está ubicado el apartamento.
53. +Sem: Corriendo por el campo el
perro caza al gato.
-Sem: En el silencio de la mañana
oigo al gato.
54. +Sem: Después de cerrar el banco
cuenta el dinero.
-Sem: Yo presto poca atención al
dinero.
55. +Sem: Cuando regresé a mi casa
ayer, abrí la puerta.
-Sem: Después de muchos años,
finalmente pinté la puerta.
56. +Sem: Cuando me mudé, mi madre
me envió mis cajas.
-Sem: Antes de salir para mi trabajo,
encontré las cajas.
FILLER ITEMS
Filler Words
Sentence Context
1. Me gustan los números, por eso enseño las matemáticas.
184
viver
2. Cada mañana yo hablo con la gente en la calle.
vemdedor
3. Este fin de semana, voy a limpiar mi escritorio.
irioma
4. Para saber la hora, yo miro hacia la pared para ver el reloj.
vapa
5. Cuando era niño, mi actividad favorita era jugar en la nieve.
vorrer
6. Después de ganar la lotería, compré un coche.
beño
7. Es increíble ver todas las pinturas en el museo.
puerna
8. Cuando hace calor, prefiero no usar el horno.
hortuga
9. Cuando me visto, tengo miedo de perder mis aretes.
conbar
10. Después de cenar, siempre como el postre.
sata
11. En la calle, a veces doy de comer a los pájaros.
bago
12. Cuando estoy enfermo, tengo una alta fiebre.
ofitina
13. En el parque, me gusta sacar fotos de la estatua.
lantalla
14. Mi amigo cree que no es justo pagar los impuestos.
favilia
15. En el bar, me encanta tomar la cerveza.
penbar
16. Después de trabajar, voy a ir a una cita.
gruvo
17. Hay mucha gente y muchos edificios en la ciudad.
18. En el futuro, no quiero viajar a la selva.
prepanar
rastillo
19. Por conducir rápidamente, me pusieron una multa.
20. En el centro comercial, vi a un médico.
fudar
manfener
21. Cuando nado en el mar, tengo miedo a los peces.
22. En nuestro sistema solar, hay una luna y un sol.
23. Yo como la ensalada con un tenedor.
asfecto
kiaje
necesicar
24. Yo ahorro mi dinero porque quiero ir a un viaje.
vosar
25. En una emergencia, necesito encontrar la salida.
homar
26. Para mi cumpleaños, mi novio me dio una bolsa.
conlejo
27. Por la noche, veo las estrellas en el cielo.
somortar
185
28. Para conducir, siempre llevo el cinturón.
actisud
29. Compré billetes esta mañana para el concierto.
antar
30. Para tarea, tengo que escribir una página.
munbo
31. Este fin de semana, voy a pintar la pared.
pemir
32. Todos los días es necesario lavarse el pelo.
crefer
33. Cuando recibo un mal servicio, no doy una propina.
estuliar
34. De todos los tipos de literatura, mi favorita es la poesía.
apabar
35. Verifico el precio de la computadora en el recibo.
abratar
36. Vamos a la iglesia para escuchar al sacerdote.
pozer
37. El hijo de mi hermana es mi sobrino.
abogaro
38. Mi amiga es una actriz en una telenovela.
zaca
39. Las casas cuestan mucho más en la costa.
alustar
40. Para viajar por avión, voy al aeropuerto.
bombedo
41. Me gusta recibir para mi cumpleaños una carta.
analidar
42. Después de graduarme, voy a comenzar mi carrera.
tozillo
43. Yo pongo todo mi dinero dentro de una cartera.
ayumar
44. En caso de fuego, es importante evitar el humo.
careza
45. Cuando tengo hambre, como una merienda.
tuello
46. Mi madre escribe muchos cuentos porque es una novelista.
lorra
47. Este viernes en mi clase de biología, tengo una prueba.
lefalizar
48. Tengo que completar tres proyectos durante el semestre.
somgrero
49. En mi último empleo, trabajé con un soldado.
50. Cada semana, voy al restaurante con mis primos.
logrir
bangosta
51. Cuando tengo sueño, prefiero tomar una siesta.
apogar
52. Para relajarme, abro la ventana y admiro la vista.
norreo
53. Generalmente, muchas personas no viven en el pueblo.
186
consirmar
54. En la isla, uno puede ver el océano y el volcán.
harfar
55. Muchos de mis amigos viven en este barrio.
nanar
56. En vez de las escaleras, prefiero usar el ascensor.
187
huantes
APPENDIX I
IRB APPROVAL LETTERS
Office of the Vice President For Research
Human Subjects Committee
Tallahassee, Florida 32306-2742
(850) 644-8673 · FAX (850) 644-4392
APPROVAL MEMORANDUM
Date: 4/22/2011
To: Eileen Locke
Address: MC 1540 (Department of Modern Languages & Linguistics)
Dept.: MODERN LANGUAGES AND LINGUISTICS
From: Thomas L. Jacobson, Chair
Re: Use of Human Subjects in Research
Lexical processing in sentence context
The application that you submitted to this office in regard to the use of human subjects in the
proposal referenced above have been reviewed by the Secretary, the Chair, and one member of
the Human Subjects Committee. Your project is determined to be Expedited per 45 CFR §
46.110(7) and has been approved by an expedited review process.
The Human Subjects Committee has not evaluated your proposal for scientific merit, except to
weigh the risk to the human participants and the aspects of the proposal related to potential risk
and benefit. This approval does not replace any departmental or other approvals, which may be
required.
If you submitted a proposed consent form with your application, the approved stamped consent
form is attached to this approval notice. Only the stamped version of the consent form may be
used in recruiting research subjects.
If the project has not been completed by 4/17/2012 you must request a renewal of approval for
continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your
expiration date; however, it is your responsibility as the Principal Investigator to timely request
renewal of your approval from the Committee.
You are advised that any change in protocol for this project must be reviewed and approved by
the Committee prior to implementation of the proposed change in the protocol. A protocol
change/amendment form is required to be submitted for approval by the Committee. In addition,
federal regulations require that the Principal Investigator promptly report, in writing any
188
unanticipated problems or adverse events involving risks to research subjects or others.
By copy of this memorandum, the Chair of your department and/or your major professor is
reminded that he/she is responsible for being informed concerning research projects
involving human subjects in the department, and should review protocols as often as needed to
insure that the project is being conducted in compliance with our institution and with DHHS
regulations.
This institution has an Assurance on file with the Office for Human Research Protection. The
Assurance Number is FWA00000168/IRB number IRB00000446.
Cc: Gretchen Sunderman, Advisor
HSC No. 2011.5840
189
Office of the Vice President For Research
Human Subjects Committee
Tallahassee, Florida 32306-2742
(850) 644-8673 · FAX (850) 644-4392
RE-APPROVAL MEMORANDUM
Date: 5/15/2012
To: Eileen Locke
Address: MC 1540 (Department of Modern Languages & Linguistics)
Dept.: MODERN LANGUAGES AND LINGUISTICS
From: Thomas L. Jacobson, Chair
Re: Re-approval of Use of Human subjects in Research
Lexical processing in sentence context
Your request to continue the research project listed above involving human subjects has been
approved by the Human Subjects Committee. If your project has not been completed by
5/14/2013, you must request a renewal of approval for continuation of the project. As a courtesy,
a renewal notice will be sent to you prior to your expiration date; however, it is your
responsibility as the Principal Investigator to timely request renewal of your approval from the
committee.
If you submitted a proposed consent form with your renewal request, the approved stamped
consent form is attached to this re-approval notice. Only the stamped version of the consent form
may be used in recruiting of research subjects. You are reminded that any change in protocol for
this project must be reviewed and approved by the Committee prior to implementation of the
proposed change in the protocol. A protocol change/amendment form is required to be submitted
for approval by the Committee. In addition, federal regulations require that the Principal
Investigator promptly report in writing, any unanticipated problems or adverse events involving
risks to research subjects or others.
By copy of this memorandum, the Chairman of your department and/or your major professor are
reminded of their responsibility for being informed concerning research projects involving
human subjects in their department. They are advised to review the protocols as often as
necessary to insure that the project is being conducted in compliance with our institution and
with DHHS regulations.
Cc: Gretchen Sunderman, Advisor
HSC No. 2012.7993
190
Office of the Vice President For Research
Human Subjects Committee
P. O. Box 3062742
Tallahassee, Florida 32306-2742
(850) 644-8673 · FAX (850) 644-4392
RE-APPROVAL MEMORANDUM
Date: 04/15/2013
To: Eileen Locke
Address: MC 1540 (Department of Modern Languages & Linguistics)
Dept.: MODERN LANGUAGES AND LINGUISTICS
From: Thomas L. Jacobson, Chair
Re: Re-approval of Use of Human subjects in Research:
Your request to continue the research project listed above involving human subjects has been
approved by the Human Subjects Committee. If your project has not been completed by
4/14/2014, you are must request renewed approval by the Committee.
If you submitted a proposed consent form with your renewal request, the approved stamped
consent form is attached to this re-approval notice. Only the stamped version of the consent form
may be used in recruiting of research subjects. You are reminded that any change in protocol for
this project must be reviewed and approved by the Committee prior to implementation of the
proposed change in the protocol. A protocol change/amendment form is required to be submitted
for approval by the Committee. In addition, federal regulations require that the Principal
Investigator promptly report in writing, any unanticipated problems or adverse events involving
risks to research subjects or others.
By copy of this memorandum, the Chairman of your department and/or your major professor are
reminded of their responsibility for being informed concerning research projects involving
human subjects in their department. They are advised to review the protocols as often as
necessary to insure that the project is being conducted in compliance with our institution and
with DHHS regulations.
Cc:
HSC No. 2013.10129
191
APPENDIX J
INFORMED CONSENT FORM
Informed Consent Form
Lexical Processing in Sentence Context
You are invited to participate in the study “Lexical Processing in Sentence Context.” The
purpose of this study is to provide information about the way people process Spanish. You were
selected as a possible participant because of your proficiency in Spanish. We ask that you read
this form and ask any questions you may have before agreeing to be in the study.
This study is being conducted by Eileen Fancher, Department of Modern Languages and
Linguistics, Florida State University.
If you agree to participate in this study, we would ask you to read sentences and words in
Spanish on a computer and answer questions regarding those sentences and words. The computer
will record the data and your confidentiality will be protected because a participant code will be
used instead of your name. Afterwards, you will complete a questionnaire asking about your past
experience with Spanish. You may decline to answer specific questions. The study should last
about one hour.
Your participation is totally voluntary, and you may stop participation at any time. The only
foreseeable risk is that you may feel frustrated at not being able to understand all of the sentences
in a foreign language. However, you have the right to terminate the session at any time without
penalty.
Your performance and any information obtained will remain confidential to the extent allowed
by law. Rather than using your name, we will use numbers to code the participants. Only the
primary researchers will have access to the codes and the data and all data will be stored
electronically on a flash drive, which will be kept in a locked file drawer in Diffenbaugh 356
when not being analyzed. In accordance with standard procedure, all data will be destroyed by
February 1, 2021.
You are encouraged to ask any questions you might have about the study before, during and after
your participation in the study. However, answers that could influence the results of the
experiment will be deferred to the end of the experiment. You will also receive a debriefing form
upon completion of the study, fully explaining the goals of the research.
There are benefits for participating in the research project. First, you may increase your
awareness of your second language abilities. Also, you will be providing researchers with
valuable information about how individuals process a foreign language. You also will receive
$10 for your participation.
192
The researcher conducting this study is Eileen Fancher, Florida State University, Department of
Modern Languages and Linguistics. You may ask any question you have now. If you have a
question later, you are encouraged to contact Dr. Gretchen Sunderman or Eileen Fancher.
If you have any questions or concerns regarding this study and would like to talk to someone
other than the researcher, you are encouraged to contact the FSU IRB at 2010 Levy Street,
Research Building B, Suite 276, Tallahassee, FL 32306-2742, or 850-644-8633, or by email at
[email protected].
I have read the above information. I have asked questions and have received answers. I consent
to participate in the study.
______________________________________________ _________________
Signature Date
FSU Human Subjects Committee approved on 4/15/2013. Void after 4/14/2014. HSC #
2013.10129
193
REFERENCES
Altarriba, J., Kroll, J. F., Sholl, A., & Rayner, K. (1996). The influence of lexical and conceptual
constraints on reading mixed-language sentences: Evidence from eye-fixation and
naming times. Memory & Cognition, 24, 477-492.
Balota, D. A., Yap, M. J., & Cortese, M. J. (2006). Visual word recognition: The journey from
features to meaning (A travel update). In M. Traxler & M. A. Gernsbacher (Eds.),
Handbook of psycholinguistics (pp. 285-376). Amsterdam: Academic Press.
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BIOGRAPHICAL SKETCH
Eileen Locke Fancher, daughter of Jim and Mary Locke, was born in Mission Viejo,
California in June 1984. She attended her dream school, the University of Notre Dame, majoring
in Spanish and minoring in theology. As part of her undergraduate career, she spent a semester in
Puebla, Mexico. In 2006, Eileen began her graduate studies at Texas Tech University. She
graduated with her M.A. in Applied Linguistics in 2008. Eileen is graduating with her Ph.D. in
Spanish Linguistics from Florida State University in 2014.
Eileen has taught in the Basic Spanish Language Program at both Texas Tech University
and Florida State University. She also has taken on coordinating responsibilities at both
institutions, working to develop curriculum and assessments. At Florida State University, she
served as the graduate student representative for the department, as well as a committee member
on the department’s Technology and Distance Learning Committee. In 2013, she received the
Service Award from the department. She also has received dissertation funding from Language
Learning, Florida State University, and the Ada-Belle Winthrop King Foundation.
Eileen’s research interests center on bilingual and second language lexical processing in
sentence context. She is particularly interested in the psycholinguistic bottom-up processes
involved in word recognition and the top-down processes that may override them. She published
a book chapter with Gretchen Sunderman on bilingual and second language lexical processing.
She also has presented findings from her research at national conferences.
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