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Theses and Dissertations
2014
Sensory input and mental imagery in second
language acquisition
Sultana Mahbuba Nargis
University of Toledo
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A Thesis
entitled
Sensory Input and Mental Imagery in Second Language Acquisition
by
Sultana Mahbuba Nargis
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the
Master of Arts Degree in English with a Concentration in ESL
_________________________________________
Dr. Douglas W. Coleman, Committee Chair
_________________________________________
Dr. Stephen Christman, Committee Member
_________________________________________
Dr. Gaby Semaan, Committee Member
_________________________________________
Dr. Patricia R. Komuniecki, Dean
College of Graduate Studies
The University of Toledo
December 2014
Copyright 2014, Sultana Mahbuba Nargis
This document is copyrighted material. Under copyright law, no parts of this document
may be reproduced without the expressed permission of the author.
An Abstract of
Sensory Input and Mental Imagery in Second Language Acquisition
by
Sultana Mahbuba Nargis
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the
Master of Arts Degree in
English with a Concentration in ESL
The University of Toledo
December 2014
In the field of second language acquisition, there is a dominant theory of language
learning, specifically, the claims that the input for language learning consists of language
by famous linguist Noam Chomsky (1964) and his followers. However, Douglas W
Coleman (Thesis adviser), Samuel Johnston, Yifei Xin, and the author hypothesized in
ENGL 6150, a master’s course in Fall 2011, that a person cannot learn a target language
from the speech input alone. The findings of that study were published in Xin’s (2012)
study which showed that the relevant input for language learning consists of the sound of
speech in parallel with other sensory experiences, and what people learn is the ability to
communicate. An even earlier study by Postica and Coleman (2006) indicated that mental
imagery, the capacity of human brain to recreate sensory experience without external
stimuli, could be a substitute to the required ‘parallel sensory input’ in Second Language
Acquisition.
Implementing a variation of Postica (2006) and Xin (2012) experiments, this
study aimed to further investigate the role of mental imagery in generating sensory input
for communication learning. Thus, the study set out to explore if mental imagery could be
a substitute to the sensory input required for learning to communicate in a target
iii
language. The findings of the study differ from the findings of Postica’s (2006). The
variation in instrument design of this study from Postica’s (2006) design might have
contributed to the different outcome of this study.
iv
To my husband without whose support, sacrifice, and inspiration, I could not complete
this thesis and the master’s study at the University of Toledo.
Acknowledgements
My greatest gratitude goes to my thesis and graduate advisor, Dr. Coleman,
without whose guidance, advice, support, and concern I would not be able to overcome
the mental challenges I went through, and finally complete this study. He opened my eyes
to see people interacting from a scientific point of view and better understand how
humans communicate.
I specially need to thank my thesis committee members, Dr. Coleman, Dr.
Christman, and Dr. Semaan for their valuable time and support by providing their
constructive feedback throughout the thesis process and report writing.
I am grateful to my parents who inspire me in my academic endeavor, support me
unconditionally in my good times and difficult times.
I would also like to acknowledge my following classmates of Applied Linguistics
class, Department of English, University of Toledo, who took part in conducting the
experiments: Yifei Xin, Samuel Johnston, Rosemary Song, Timothy Escondo, Jeremy
Holloway, and Yifan Zhao.
Above all, I express my deepest gratitude to Almighty God for everything I
achieved in my life, including the knowledge I gained from this study.
v
Table of Contents
Abstract
iii
Acknowledgements
v
Table of Contents
vi
List of Tables
viii
List of Figures
ix
I. Input in Second Language Learning: from a traditional view to a scientific view
1
A. What is language?
1
a. Traditional view of language learning
1
B. A device for language learning – how realistic is it?
6
C. Input is still the focus
8
D. Problems surrounding the assumed “input” in language learning
9
E. The true nature of input: viewing input from a real world perspective
12
F. An alternate view of language learning: shifting focus from language learning
to learning to communicate
13
G. Building a case for mental imagery – historical background
16
H. Mental imagery: from the perspectives of behaviorists to neuroscientists
16
I. Recent studies on mental imagery and its usefulness
23
J. Research questions
25
II. Methodology
26
A. Design
26
a. Study Selection of a target language
26
b. Participants
27
vi
B. Instrument
27
C. Procedure
28
a. Study sheet
29
b. The assessment test
29
D. Hypotheses
32
III. Data Analysis
33
IV. Discussion
38
A. How the results of this study differ from previous studies
38
B. Limitations and Suggestions for Future Research
39
C. Concluding Remarks
42
References
43
Appendices
A. Participant’s Consent Form
53
B. Instructions to Participants
55
C. Mini-dialogs
58
D. Study Sheet
64
E. Comprehension Test
65
F. Answer Sheet and Questionnaire
66
G. Recoding Sheet
68
vii
List of Tables
Table 1
Recoding (scoring) guide .................................................................................31
Table 2
Example recoding of test answers....................................................................32
Table 3
Possible predictors of learning ........................................................................37
viii
List of Figures
Figure 1
Chomsky’s (1964, p. 26) assumed devices for (a) language processing, and
(b) language acquisition. ...................................................................................4
Figure 2
Functional areas of the human brain (Curtesy: Mayfield Clinic). ..................20
Figure 3
Sample mini-dialog used in the experiment. ...................................................28
ix
Chapter One
Input in Second Language Learning: from a traditional view to a scientific view
What is language?
The definition of language changed from time to time to serve the intellectual
need of the time. Plato introduced a debate on the origin of language through his dialogue
Cratylus which is considered to be the first linguistic text in Western linguistics (Seuren,
1998, p. 5). The center of the Cratylus debate is the difference between the two
competing perspectives of language. According to one of the perspectives, language is
“inherently ‘true to life’, since words are given by nature, and not by convention”
(Seuren, 1998, p. 6); and according to the alternative perspective, “word forms are
arbitrary and conventional” (Seuren, 1998, p. 7). At times, language is seen as something
abstract, something social or cultural, something behavioral, something mental (Botha,
1992, p. xii), and at other times language is seen something material, a natural
phenomenon, the object of a science, a type of a system, as something used, as something
processed, as something organic, as something structural, as something produced and
comprehended, and as data (Yngve, 1996, p. 10). However, this study is based on the
premises of scientific study of learning to communicate.
Traditional view of language learning. Since early ages, humans developed
communicative behavior. Humans communicate in numerous ways. According to a
traditional view, the most common and distinct way humans communicate is called the
use of language; in other words, humans can exchange messages and convey meaning
through language. Nowadays, the term “language” applies to a wide variety of
perspectives and conceptions (Botha, 1992, p. xii; Yngve, 1996, pp. 10-11) not only in
1
linguistics but also in other fields of study that are concerned with the human ability to
communicate (psycholinguistics: Miller, 1967, Macphail, 1998; neuroscience: Bloom
2001, Lieberman, 2002; sociolinguistics: Mesthrie, 2000; second language acquisition:
Cairns, 1996, Rosenthal, 2000; second language teaching: Johnson, 1997, Danesi, 2003,
Mackey, 2005; multidisciplinary studies: Banich, 2003).
The ability to communicate beyond one’s own community gained importance as
the early human communities came in contact with each other and engaged in trade,
commerce, and exchange of knowledge. The emergence in the ancient world of grammar
as part of an explanation of speaking and writing made it possible to encode human
communicative behaviors on a large scale (Postica, 2006, p. 5), and eventually helped
natives of different cultures learn how to communicate with each other, and to establish
relationships.
Merchants quickly understood the advantages of being able to manifest culturally
appropriate communicative behaviors to their customers and trade partners: “even a
smattering of your client’s mother tongue works wonders in business. It also helps to
safeguard against sharp practice” (Howatt, 1984, p. 6). The invention of the printing
machine helped develop a medium which could dispatch information to a wider mass.
William Caxton printed the first ever text book for teaching English to non-English
natives. The objective of the book was to satisfy merchants’ communicative needs: “Who
this booke shall wylle lerne may well enterprise or take on honed merchandises from one
land to another” (Howatt, 1984, p. 7).
During the nineteenth century, the study and research in philosophy and sciences
grew tremendously. Language teaching drew its approaches and methods from the
2
research and findings of those fields, and experienced a similar growth which continued
through the twentieth century. Additionally, attempts to apply scientific approaches to
study of language started in the late nineteenth century and emerged in the twentieth
century (Kelly, 1969; Howatt, 1984).
A shift in analysis of language from diachronic to synchronic analysis took place
in the late nineteenth to early twentieth century when Saussure first viewed language as a
“semantic code” (Clarke, 1990, p. 143). In linguistics, “diachronic” refers to the
development of a language over time by paying attention to affinity between languages
and historical transmutations of sounds and by striving for the reconstruction of principal
languages. Again, “synchronic” refers to the state of a language at a particular time by
focusing on its structural features and characteristics and by using phonological,
morphological and syntactic explanations including semantic and pragmatic aspects of it.
Codification which emerged as part of shift from diachronic to synchronic analysis is an
agreement among the users of a sign as they recognize the relation between the signifier
and the signified, and respect the agreement in practice. Roman Jakobson, a prominent
linguist, significantly contributed to Saussure’s project for developing a general theory of
signs. Jakobson calls this theory “semiotics” by analogy to the disciplines of linguistics
and semantics (ibid).
Over the centuries, the study of language became diversified, and led to the
emergence of what is known today as the fields of Second Language Acquisition and
Second Language Teaching. One of the most influential of all the linguistics theories of
recent times is Chomsky’s “Innateness Hypothesis”. Chomsky (1964) suggests the
existence of a ‘mental device’ that receives the morphemes and other abstract units or
3
well-formed sentences (‘primary linguistic data’) of language as input and produces a
generative grammar as output.
(a)
utterance
A
structural
description
(b)
primary
linguistic data
B
generative
grammar
Figure 1. Chomsky’s (1964, p. 26) assumed devices for (a) language processing, and (b)
language acquisition
According to Chomsky, the child's mind is like a black box, a Language
Acquisition Device (LAD), the internal workings of which cannot be inspected. Into it
goes the language data, samples of performance, and from it comes out grammatical
competence. The child’s LAD takes in input and produces output. If something is found
in the output that cannot be derived from the input, it must have come from the LAD
itself. Chomsky (1968) postulated LAD as the following:
Having some knowledge of the characteristics of the acquired grammars and the
limitations on the available data, we can formulate quite reasonable and fairly
strong empirical hypothesis regarding the internal structure of the language
acquisition device that constructs the postulated grammars from the given data.
(p. 113)
Another one of the second language acquisition theories that shape the recent trend in
SLA, and FLA is Krashen’s Input Hypothesis. Krashen (1985) introduced the term
4
“comprehensible input”, and made it central to second language acquisition theory. Five
hypotheses are part of this theory, the most important one being the Input Hypothesis:
[W]e acquire language in an amazingly simple way–when we understand
messages. We have tried everything else–learning grammar rules, memorizing
vocabulary, using expensive machinery, forms of group therapy, etc. What has
escaped us all these years, however, is the one essential ingredient:
comprehensible input. (p. vii)
But, only input of a very specific kind (i + 1) will be useful in altering a learner’s
grammar. In Krashen’s view, the Input Hypothesis is central to all of acquisition, and it
also has implications for the way Krashen defined “comprehensible input”. According to
Krashen, “comprehensible input” is a bit of language (i + 1) that is heard or read and that
is slightly ahead of a learner’s current state of grammatical knowledge (i). It is given that,
without understanding the language, no learning can take place. Second languages are
acquired “by understanding messages, or by receiving ‘comprehensible input’ ”
(Krashen, 1985, p. 2).
According to Krashen, language containing structures a learner already knows essentially
serves no purpose in acquisition. Similarly, he claims that language containing structures
way ahead of a learner’s current knowledge is not useful, and a learner does not have the
ability to “do” anything with those structures. Krashen defined a learner’s current state of
knowledge as i and the next stage as i + 1. Thus, the input a learner is exposed to must be
at the i + 1 level in order for it to be of use in terms of acquisition. “We move from i, our
current level to i + 1, the next level along the natural order, by understanding input
containing i + 1” (1985, p. 2).
5
A device for language learning – how realistic is it?
Cook (1991, 1993) summarized the following four steps of the argument in
support of Chomsky’s Universal Grammar (UG).
(1) A native speaker of language knows something (feature) about language.
(2) The language feature cannot be learnt from primary linguistic data.
(3) The language feature is not learned from experience.
(4) The language feature must be built-in to human mind.
In the first step of the above argument, the native speaker’s knowledge is defined in
terms of competence which is theoretically invalid. In order to determine the true nature
of competence, we must know which aspects of performance are irrelevant data. The
problem is that the only way to filter out the irrelevant data is to know ahead of time what
constitutes competence (Reich, 1973).
The conclusion drawn in the step three of the above argument, that a given language
feature is not learned from experience, depends upon accepting the following two
assumptions: (a) we can ascribe to people an idealized, flawless competence, and (b) the
only available information available to the learner is “primary linguistic data”, not any of
the other sensory information. However, the evidence shows (a) to be false, and Klein's
(1986) Chinese Room analogy shows (b) to be false as well. From the perspective of
human linguistics which is the premise of this study, step four implicitly creates a
brain/mind — physical/mental “domain confusion” (Yngve, 1996) promoted by
Chomsky in many of his writings. This domain confusion is very prominent in Chomsky
(1986): “Mind and matter, mind and brain, have converged” (p. xxii); “suppose we
proceed further to regard talk of mind as talk about the brain undertaken at a certain level
6
of abstraction” (p. 22); “statements about I-language... are... statements about something
real and definite, about actual states of the mind/brain and their components” (pp. 26-27).
Similarly, there are numerous difficulties with Krashen’s hypothesis as well. First, the
hypothesis comes short in specifying how to define and measure the level of knowledge.
To validate the hypothesis, a particular level (say, level 101) must be defined so that
input contains of next linguistic level (say, level 102) can be assessed. Defining the level
of competence is also required for the testing the learner for the next higher (target)
linguistic level after the learner receives inputs for the target level. But, the definition of
different levels of linguistic competence is missing from the hypothesis. Krashen (1982)
only stated that “We acquire by understanding language that contains structure a bit
beyond our current level of competence (i + 1). This is done with the help of context or
extra-linguistic information” (p. 21).
Second, Krashen states that there has to be a sufficient quantity of the appropriate
input, but he fails to explain what defines sufficient. Here, Krashen invalidly assumes a
threshold at which the learning occurs; that there is some point where the learner has not
yet learned something and then suddenly he has learned it (the point at which hearing the
same input supposedly “makes no difference”). However, that is not how the brain
works: each experience adds something new; what it adds seems to be less and less each
time because each new experience is smaller and smaller part of all of the person's
experiences. Furthermore, learning, at a neurological level, involves changes in synaptic
strength; these changes are biochemical (Lawson 2003). Unlike the neuron's internal
“digital” on/off (an electrical signal), the change in synaptic strength (a chemical signal)
7
is analog. So, there is no threshold at which a learner didn't know (at all) and now he/she
does (completely).
Additionally, Krashen did not explain how extra-linguistic information aids in
actual acquisition, or internalization of a linguistic rule. If by understanding Krashen
meant understanding at the level of meaning, we may be able to understand something
that is beyond our grammatical knowledge. But, understanding does not automatically
translate into grammatical acquisition. Thus, Krashen is right that a successful
communication requires “comprehensible input” (extra linguistic context), however he is
wrong when he assumes that language exists in the physical world, and it can be a part of
input (Postica & Coleman 2006, p. 475).
Input is still the focus
Even though there have been controversies surrounding the treatment of language
as input for language acquisition, input is still the focus in theories of language
acquisition. The following are a few excerpts from different theories surrounding input
and language acquisition. Input is a body of second language data, and UG is at the core
of the learning process (Gass and Selinker, 2008, pp. 304-310). We first deal with the
nature of the input to second language learners. We then focus on the interrelationship of
second language use (especially conversation) and language learning. Corder (1967)
made an important distinction between what he called input and intake. Input refers to
what is available to the learner, whereas intake refers to what is actually internalized (or,
in Corder’s terms, “taken in”) by the learner.
In response to Chomsky's (1964) assumption that the primary language data (PLD)
consists of well-formed sentences in the target language, Morgan (1986) introduces an
8
alternative learning theory called the Bracketed Input Hypothesis. According to Morgan
(1986), “input” for language learning not only consists of “primary linguistic data”, it
also contains bracketing information about the hierarchical structures present in it. He
further insists that bracketing information is necessary for language acquisition to
proceed in the face of strict limits on data, for children to learn complex grammars from
simple input (Morgan, 1986, pp. 108-109).
Thus, although we see a number of variations of input for language learning, all of
the variations of input contain ‘language’ in them (Saleemi, 1992). Saleemi (1992) shows
that Chomsky and Miller, Fodor, Pinker, and Wexler and Culicover, all share the above
assumption although input is referred to in different terms, such as ‘data’ (p. 8), ‘the
available evidence’ (p. 3), and ‘environmental input’ (p. 10).
Problems surrounding the assumed input in language learning
Generative grammar in Chomsky’s innateness hypothesis is ‘unlearnable’ (Gold,
1967), because of the assumption that the input consists of “primary linguistic data”.
Additionally, in order for Chomsky’s process to work learners need to have access to
both positive evidence — a set of grammatical sentences, and negative evidence — what
is not grammatical (ibid). Chomsky (1975) responded to the above argument laid out by
Gold (1967), but he did not change his assumptions so that his theories could meet Gold’s
challenge. Instead, he added another theory; the Universal Grammar, an important part of
Language Acquisition Device (LAD) that cannot be tested scientifically by comparison
with observation.
Krashen consistently deviates from Chomsky’s assumption, and proposes that in SLA
mere linguistic input is not enough, it must be comprehensible; however, as demonstrated
9
in the preceding section, his arguments are self-contradictory (Carroll, 2001). Krashen
(1985, p. 2), for example,has always explicitly recognized that additional input is
necessary for SLA, when he has said, “We are able to understand language containing
acquired grammar with the help of context, which includes extra linguistic information,
our knowledge of the world, and previously acquired linguistic competence.” Even
Pinker (1994, p. 278), who championed the idea of Chomsky’s theory of Universal
Grammar, agrees with Krashen, and says, “Though speech input is necessary for speech
development, a mere soundtrack is not sufficient”. Thus Krashen and Pinker indirectly
support Klein’s conclusion that “input consists of a full range of sensory experience”
(Postica & Coleman 2006, p. 475).
Klein (1986) with his simple thought experiment shows that Chomsky’s “primary
linguistic data” (1964) cannot be a part of “comprehensible input”. He explained that if
we put a person in an empty room, and is exposed him/her to nothing but the sound of
recorded Chinese, the person would not learn Chinese, no matter how long he/she stayed
in the room. Apparently, Klein seems to support Krashen’s “comprehensible input”
however, he distinguishes himself from Krashen’s proposition, when he says “what
makes learning possible is the information received in parallel to the linguistic input in
the narrower sense” (Postica & Coleman 2006, p. 475). Linguistic input in the narrower
sense is “the sound waves” which are physically real, not “language” (ibid). In fact if
language were contained in the “comprehensible input”, the Chinese room would work,
and the learner would acquire some grammatical knowledge of Chinese.
Contrary to this view, a question may arise in our mind, if language is not a part
of “comprehensible input”, how do learners in traditional classroom environments
10
apparently lacking in “comprehensible input” acquire communicative ability? We will get
the answer to the preceding question if we understand how comprehension and input
affect a person learning to communicate, and explain them in the real world terms of
Yngve (1996), and how mental imagery works in absence of sensory input from the
perspective of neuroscience. In human linguistics perspectives (HLS), “comprehension in
a real world sense is a physical change in state in one of the participants as a result of
linkage events” (Postica & Coleman 2006, p. 475). The following example given by
Klein (1986) will illustrate how comprehension takes place as a result of a linkage event.
Suppose you are a Japanese visitor and you happen to be in Germany without
knowing a single word of German. You are having breakfast in your hotel with a
couple of Germans. One of the Germans turns to you and produces a sequence of
speech sounds like this:
(1) [axkoenənzi:mi:rma:ldaszaltsraIçənbItəšoe:n] (p. 59).
Klein points out that the concurrent events may (or may not) lead to language
learning. For example, suppose the speaker looks at you, raises his eyebrows, glances
down at the table toward the salt and pepper shakers, gestures toward them and then
speaks, holding out an open hand afterwards? He/she might be perplexed as to which
thing the German wanted, but would certainly be aware that the speaker was making a
request. He/she might offer both, and let the German choose. If you know English,
however, you would be likely to guess that one part of the sequence, [szalt], refers to salt,
and would simply pass the salt. In “pass the salt” event learning and comprehension may
occur due to the linkage event, whereas in absence of linkage in the “Chinese room”
neither learning nor comprehension takes place.
11
The true nature of input: viewing input from a real world perspective
Krashen (1985) proposes that mere linguistic input is not enough; input must be
understood. However, Klein (1986), Yngve (1996), and Coleman (2005, 2007) show that
input must be a “full range of sensory experience parallel to the linguistic input”. Now
the question arises “what is the nature of input?” The most recent view of input for
language learning suggests shifting focus from language learning input to input for
learning to communicate (Coleman, 2005), which requires investigating of the observable
properties and actions of people in the real world.
In addition to conveying information about the phonological, grammatical, and
lexical nature of the English language input to second language learners in an Englishspeaking social milieu includes cultural information within which the emergent meaning
of the code must be situated and interpreted. Communicative competence can be defined
simply as “what a speaker needs to know to communicate appropriately within a
particular language community” (Saville-Troike, 2006). It involves knowing not only
aspects of linguistic structure (although that is critical component of knowledge) but also
when to speak (or not), what to say to whom, and how to say it appropriately in any given
situation (ibid).
The learner must know who is speaking to whom, when and where, he must be
able to watch the accompanying ‘body language’ (gesture, facial expression, etc.), and he
must note the reactions of the listener. Eventually, he should be able to establish a
relationship between identifiable segments of the sound stream and particular pieces of
parallel information (Klein, 1986, p. 44).
12
An alternate view of language learning: shifting focus from language learning to
learning to communicate
Knowledge of acquisition is not merely a matter of direct recording of sensory
impressions, nor is the mere passage of time sufficient for innate structure become
functional. Rather, acquiring new knowledge appears to involve a complex
“construction” process in which undifferentiated sensory impressions, properties of the
developing organism’s brain and the organism’s unsuccessful (i.e. contradicted)
behaviors interact in a dynamic and changing environment (Lawson, 2003, pp. 4-5).
Yngve (1996) points out problems with contemporary linguistics studies. Yngve
argues that in conventional linguistics, the objects of language are studied more like
philosophy which exists in mental domain than like science which exists in physical
domain. He calls these problems “domain confusions”. Yngve (1996, pp. 4, 21-22)
outlines the criteria for scientific study of language, and points out that input should be
the real world objects — “who is speaking to whom”, “when and where”, “the reactions
of the listener”, all of sensory experiences involved in a communicative channel. In fact,
if language were contained in the “comprehensible input”, the Chinese Room would
work to the extent that the learner would exit the room with grammatical knowledge of
Chinese, even if he/she still could not apply that knowledge to real-life interactions
(Postica & Coleman, 2006, p. 475). But, that is not the case, as Klein (1986) explains and
shows that the relevant input consists of the full range of sensory experience available to
the learner at a given time. This apparently supports Krashen’s (1985) “comprehensible
input” theory. Klein, however, makes it clear that linguistic input does not make language
13
learning possible. Instead, “what makes learning possible is the information received in
parallel to the linguistic input in the narrower sense (the sound waves)” (p. 44).
Xin’s study (2012) to which the author contributed too, showed that the subjects
who were exposed solely to speech and text (considered to be primary linguistic data) did
not show any results of learning. On the other hand, parallel sensory input seems to
contribute significantly to language learning, even when the exposure was only less than
10 minutes in total. Second, the subjects who received parallel sensory input not only
performed better in terms of meaning comprehension, but also in terms of grammatical
pattern recognition (what I have referred to loosely as “linguistic structure, ” above).
Thus, looking for input in language learning does not lead us anywhere but to
further controversies. Instead, we should focus on how people learn to communicate.
Meaning can be approached in physical-domain terms by considering communicating
individuals who are participants in a linkage, in the Hard-Science Linguistics framework
of Yngve (1996), as he explains it “in a human linguistics, context would no longer be
assumed to reside in texts and utterances. It would be accommodated where it belongs, in
the heads of the speakers and hearers themselves, where it would be understood in terms
of postulated properties of the people and the structures and dynamic changes of these
properties associated with the production and reception of the sound waves of speech and
other forms of energy flow” (Yngve, 1996, p. 80). Yngve steers us towards real world
objects as he further suggests “the part of the real world we are interested in studying
includes people interacting by means of sound waves, light waves, and other physical
means” (Yngve, 1996, p. 97). The analysis of comprehension and input in the physical
domain leads to the following conclusion: “Comprehension” in a real-world sense is a
14
physical change in state in one of the participants as a result of linkage events. Its causes
can include information in any of several channels in the linkage (Coleman 2005, p. 208).
Input is “the full range of sensory experience available to the learner at a given time”
(Coleman, 2005, p. 207). Thus, in human linguistics, input is the full range of sensory
experience available to learners. In order to avoid further domain confusions (Yngve,
1996), Postica & Coleman (2006) refer to the full range of sensory experience available
to learners that triggers a change in their physical state as input for learning to
communicate. Postica & Coleman (2006, p. 2) point out that in traditional second or
foreign language class room settings, the most common teaching materials used are
textbooks containing translated dialogues, vocabulary lists, and the occasional illustration
provided for window-dressing, accompanied by audiotapes and perhaps videotapes (but
the latter all too often with speakers appearing as mere “talking heads” against a
backdrop) . These classrooms apparently lack necessary information “received in
parallel” (Klein 1986, p. 44) to the speech or text, “the linguistic input in the narrower
sense” (ibid). There is not enough parallel sensory experience for the students to
associate linguistic input to anything else in their experiences. According to Krashen’s
Input/Comprehension Hypothesis, learning (what Krashen refers as language acquisition)
should not be possible in these circumstances. Thus, an apparent Klein's (1986) Chinese
Room like environment occurs in the traditional classroom setting of SLA or FLA due to
lack of input for learning to communicate. However, the students acquire communicative
ability from these learning environments.
For exploring the aforementioned apparent anomaly in the traditional second or
foreign language classrooms, let us start with an understanding of how learning occurs
15
from a neuroscience perspective. Neuro-scientific research has long established that
internal changes occurring in the human brain lead to the learned communicative
behaviors (Bloom et al., 2001, pp. 316-58). Some learners manifest the same internal
changes and their related behaviors in the absence of external input. This observation
allows Postica and Coleman (2006) to hypothesize that using mental imagery students in
the traditional classroom settings may substitute input for learning to communicate, in
other words, wider contextual input, such as investigating observable properties and
actions of people in the real world, and get out of the apparent Klein’s (1986) Chinese
Room like environment. Following Postica and Coleman’s footstep, the author would
like to further explore the role of mental imagery as an alternate to parallel sensory input
in the SLA or FLA classroom settings.
Building a case for mental imagery – historical background
Plato brought imagery into limelight (center stage) with his wax tablet metaphor.
Plato compared imagery to patterns engraved in wax as individual differences could be
understood in terms of properties of wax, like its temperature, purity, etc.
Imagery has been playing a significant role in the field of memory (Yates, 1966)
and motivation (McMahon, 1973). It is also believed to play an active role in visuospatial reasoning and creative thought. According to a dominant philosophical school of
thought, imagery involves in all thought process, and provides the semantic foundation
for language (Stanford University, 1997).
Mental imagery: from the perspectives of behaviorists to neuroscientists
Although mental imagery, a result of brain activity, has been recognized since the
time of Plato, it remained as a puzzle for researchers until 1970s when a significant
16
amount of research took place. It is now an accepted fact that a change in the properties
of a human organism, external or internal, voluntary or involuntary, is the result of brain
activities taking place in specific, definable locations within the brain (Bloom et al.,
2001, p. 3). Due to the complex nature of the brain and the limited knowledge about
internal function of the brain, all the events that occur in the brain have not yet been
explained in terms of particular parts of the brain involved and the precise roles played by
those parts of the brain (Bloom et al., 2001, pp. 3-4). However, recent advancement in
neuroimaging technologies, such as positron emission tomography (PET) and functional
magnetic resonance imaging (fMRI) opens the door for theories of imagery to be tested
objectively in humans. The study of the brain currently interests scientists from a wide
range of disciplines, and neuroscience is the modern field of research on the brain. The
general purpose of neuroscience is “to link the biological and chemical properties of the
brain and its component cells to behavior” (Bloom et al., 2001, p. 10), and the
fundamental premise on which it builds can be described as follows. All the normal
functions of the healthy brain and the disorders of the diseased brain, no matter how
complex, are ultimately explainable in terms of the basic structural components of the
brain and their function (Bloom et al., 2001, p. 3). Thus, the advancement in
neuroscience enables researchers to show that mental imagery draws on much of the
same neural machinery as a perception does in the same modality, and can engage in
mechanisms used in memory, emotion, and motor control (Kosslyn et al., 2006, pp. 19596).
Over time, imagery has been seen playing an important role in memory, problem
solving, creativity, emotion, and communication (Kosslyn et al., 2006, p. 4). According
17
to Kosslyn (1983), “Imagery is a basic form of cognition and plays a central role in many
human activities - ranging from navigation to memory to creative problem solving… It is
likely to be one of the first higher cognitive functions that will be firmly rooted in the
brain” (p. 1). A mental image is defined as the neurological event that occurs when a
representation of the type created during the initial phases of perception is present, but
the stimulus is not actually being perceived; such representations preserve the perceptible
properties of the stimulus and ultimately give rise to the subjective experience of
perception (Kosslyn et al., 2006, p. 4). Again, Paivio (1986) explains mental imagery
through Dual Coding Theory. Dual coding theory begins with the coding of verbal and
non-verbal mental representations into two separate cognitive subsystems. In this theory,
coding refers to capturing the external world and converting it to internal forms (Sadoski
& Paivio, 2001). Kosslyn et al. (2006) portrays mental imagery as embedded within and
depended upon a mental representational system called “mentalese” from which imagery
derives much or all of its semantic content. Researchers posit that the brain recreates
visual, auditory, tactile experiences, etc. when perceptual memories are retrieved
(Kosslyn et al., 2006, p. 4). Mental imagery is thus viewed as a main constituent of an
integrated and unified composite of diverse sensory images: visual, auditory, tactile,
olfactory, and others (Damasio, 1999, p. 115).
The Dual Coding Theory (DCT) of memory research focused initially on memory
and soon expanded to other cognitive phenomena. Memory remains crucial because it is a
common basis of knowledge and thought. The emphasis on memory is further justified
here because learning and memory are at the heart of educational goals. Especially
important for DCT and its applications are the beneficial effects of concreteness and
18
imagery on memory (Paivio, 2006, Chapter 4). In regard to concreteness, memory
performance generally increases from abstract words (e.g., truth, justice), to concrete
words (e.g., chair, lobster), to objects (or their pictures). In the case of studies of
language, results show that the concreteness effect occurs with materials ranging in
length from words, to sentences, to long passages, with concrete memory exceeding
abstract memory performance by a 2:1 ratio on average. The concreteness advantage is
even more striking in associative memory tasks in which recalling of response items is
cued by concrete stimulus words or by pictures.
Broca-Wernicke model, based on anatomical location of areas of the brain that
have distinct functions, is considered the basis for human linguistic ability. Broca’s study
published in 1861 ascribed the expressive language deficits (word-finding difficulties and
impediments in speech production) of a patient who had suffered a series of strokes to
damage to “Broca’s area,” a frontal region of the neo-cortex. In 1874, “receptive” deficits
in the comprehension of language were ascribed to damage to a posterior area of the
cortex, “Wernicke’s area”.
19
Figure 2. Functional areas of the human brain (Courtesy: Mayfield Clinic)
Subsequent research has shown that patients diagnosed with Broca’s aphasia often
produced sentences having simplified syntax and had difficulties comprehending
distinctions in meaning conveyed by syntax (Zurif, Carramazza & Meyerson, 1972).
Lichtheim claimed in 1885, which Geschwind restated in1970, that the
neurological basis of human language was a system linking Wernicke’s area with Broca’s
area. According to Lichtheim-Geschwind theory, incoming speech signals are first
processed in Wernicke’s area; information is then transmitted via a hypothetical cortical
pathway to Broca’s area which is served as the “expressive” language output device.
The Lichtheim-Geschwind theory was picked up by linguists, such as Chomsky
(1986) and Pinker (1994) to be a valid model of the neural architecture underlying human
linguistic ability.
20
Chomsky (1980a, 1980b) retained Broca’s claim, and stated that the human brain
contains a unique localized “language organ,” which regulates language independently of
the neural mechanisms that are implicated in other aspects of human behavior or the
behavior of other animals. Indeed, in this respect Chomsky owes much to Descartes, who
in his letter of 1646 to the Marquis of Newcastle stated that “language belongs to man
alone.” Chomsky focused on language “competence” or “knowledge of language” rather
than the processes by which people make use of their knowledge of language
(Lieberman, 2000, p. 7). Similarly, Pinker (1994) states that “Genuine language . . . is
seated in the cerebral cortex, primarily the left perisylvian region” (p. 334). He
specifically identifies the “the human language areas . . . Wernicke’s and Broca’s areas
and a band of fibers connecting the two” (p. 350). Deacon (1997) differs from Pinker as
he views those areas of the brain as non-language-specific computational centers, and
parts in a larger symbolic computational chain. According to Deacon (1997), a symbolic
learning algorithm drives language acquisition, and learning occurs in a particular
context, particular senses, types of motor actions, and ways of organizing the information
(p. 48).
However, Barsalou (1999) through his perceptual symbol systems shows that
higher order cognitive functions including categorization, concepts, attention, working
memory, long term memory, language, problem solving, decision making, skill,
reasoning, and formal symbol manipulation are grounded in lower-level sensory-motor
processing areas of the brain. Similarly, while explaining what the calls the “functional
language system” (FLS), Lieberman (2000) argues that neural mechanisms that enable
human language and cognition evolved from mechanisms adapted for motor control
21
similar to Darwinian process of evolution. Language has a long evolutionary history, and
via neural system it is integrated with nonlinguistic and motor capacities.
Thus, Lieberman rejected Chomsky’s nativism, Fodorian modularity and
algorithmic (symbolic/sequential) accounts of language summary. Lieberman argues that
language is not an instinct but a learned skill enabled by a distributed parallel network
involving many brain structures — the functional language system. The FLS, though
uniquely human, derives from neural structures that regulate motor control.
Thus, the focus has been shifted from the neocortex to deep subcortical structures
of which the basal ganglia, structures with reptilian origins, are particularly implicated
(Coleman, 2013). According to Lieberman, the key feature of language is speech, not
syntax; lexical and syntactic abilities have simpler parallels in apes, but speech reflects
species-specific facets of the human brain; understanding speech’s origins is the key to
the evolution of language.
Lieberman (2000) explains language as a learned skill, based on a functional
language system (FLS) that is distributed over many parts of the human brain; he thus
shows that we cannot logically explain language as an instinct based on genetically
transmitted knowledge coded in a discrete cortical “language organ.” He further argues
that FLS regulates the comprehension and production of spoken language, which alone
exists in no other living species. Moreover, the FLS is based on sensorimotor systems
that originally evolved to do other things and continue to do them now. Although the
neural bases of language include the neo-cortex, some of the key structures of the FLS
are subcortical basal ganglia (p. 1).
22
The above finding that the neural functions are involved for human
communicative process through speech lays the ground for exploring the role of mental
imagery, the result of brain functions, in language learning.
Recent studies on mental imagery and its usefulness
Bunzeck et al. (2005) show auditory imagery and the perception of complex
sounds share the same neural pathways. They demonstrate that the imagery and
perception of complex sounds which do not contain speech or music rely on overlapping
neural correlates of the secondary auditory cortex, but not the primary auditory cortex.
Another study by Mar (2011) shows a correlation between mental imagery and
fiction reading suggesting that a shared network exists for Theory of Mind (ToM) and
narrative comprehension (fiction reading). Since the brain structures involved in mental
imagery and sensory perception overlap, it might so happen that the needed information
received in parallel might be supplied within the brain from perceptual memories (as
mental images) in the absence of external perceptual stimuli of the same type.
Additionally, Helene and Xavier (2006) demonstrate that training through
imagery (without performing the actual motor task) can lead to the acquisition of implicit
knowledge associated with the task performance. Furthermore, a study by Taktek et al.
(2004) shows that through mental imagery, young children can improve performance of
physical exercise (discrete motor tasks). In another recent study, Berger and Ehrsson
(2013), through multiple experiments with three classic multisensory illusions (crossbounce, ventriloquism, and McGurk illusions), demonstrate that neural signals generated
by mental imagery (senses produced without actual stimuli) are capable of integrating
23
with the neural signals generated by real stimuli of a different type (modality), and can
create multisensory percepts (p. 1367).
In the domain of motor skills and sports psychology, mental imagery has become
an important component of a strategically organized learning experience. Several studies
have demonstrated that the use of mental imagery, combined with physical practice,
contributes to the optimization of motor performance (Grouios, 1992; Lesley & Gretchen,
1997; Martin & Hall, 1995; Overby, Hall, & Haslam, 1998; Screws & Surburg, 1997;
Taktek, 2000; Wrisberg & Ansel, 1989 as cited in Taktek, 2004, p. 80).
Mental imagery capacity refers to person’s aptitude to imagine scenes, objects, or
movements. Vividness refers to the degree of activation applied to representational units
in order to generate an image. We can classify people in three categories based on their
capacity of mental imagery creation: imager, occasional imager, and non-imager. Nonimagers generally do not benefit from imagery experiences. However, imagers and
occasional imagers can effectively use mental imagery to better adjust their movements
to the learning activities in which they are engaged (Chevaier, 1995; Denis, 1985, 1989;
Fishburne & Hall, 1987; Marks, 1977; Roure et al., 1999; Ryan & Simons, 1982 as cited
in Taktek, 2004, p. 84).
Mental imagery works better for the tasks in which the cognitive component is
dominant (Feltz & Landers, 1983; Paivio, 1985). For example, throwing toward a target,
a discrete motor task, requires cognitive ability of fine visuo-motor adjustments. Thus,
those tasks prove to be sensitive to mental imagery (Denis, 1985, 1989; Denis, Chevalier,
& Eloi, 1989). However, mental imagery is ineffective when the task involves a muscular
24
endurance activity (Denis, 1989; Denis, Chevalier, & Eloi, 1989; Hinshaw, 1991 as cited
in Taktek, 2004, pp. 87-88).
Ahsen (2001), Hinshaw (1991) and Murphy (1994) have suggested that the
emotional and motivational states (level of anxiety, personality traits, cognitive style and
confidence level) of the participant are important factors in analyzing the effects of
mental imagery on motor skills acquisition and performance. Learner may perform better
in acquiring communicative ability while learning with mental imagery (Postica &
Coleman, 2006, Postica, 2006).
The above discussion and findings that mental imagery triggers sensory
perception lead the author to formulate the following research question pertinent to the
study.
Research questions
Can parallel sensory experience generated by mental imagery help acquire the
appropriate form (grammar) and appropriate order of coherent and relevant speech
articulation motor tasks in a target language? In other words, can mental imagery be a
substitute for the sensory input required for learning to communicate in a target
language?
25
Chapter 2
Methodology
Design
Based on the above discussion, we assume that input in the traditional classroom
setting where there is a lack of sensory input, a learner’s brain receives any amount of
input available to the brain, and it produces mental images as substitutes to sensory input.
Brain generated mental images cause changes to the internal properties of the learner, and
they enable him/her learning to communicate in English in the case of SLA/FLA
classroom setting. For investigating the research question (presented at the end of chapter
1), we set up an experiment in a target language.
Selection of a target language. The potential participants were expected to be
composed of native English speakers, and they were likely to have some level of
experience in communicating through a non-native language as majority of the U.S high
school students undertake at least one foreign language class. One of the key features in
designing the experiment was to minimize the confounding factor due to participants’
previous language experience. To achieve this goal, the research team (see below) looked
for a target language to which the participants would have the least possible exposure.
The potential subjects were expected to having minimal, if any, exposure to a simulated
(artificial) language than to a natural language. For this reason, the team adopted an
artificial language called Térus as the target language. Térus was designed by Professor
Douglas Coleman of The University of Toledo, and it was previously used in other
similar studies (Postica, 2006; Xin, 2012). Inspired by the methodology used in
Saussure’s analogy of the chess game, Dr. Coleman created Térus by systematically
26
rotating the place of articulation of Polish consonants while preserving a simplified
syntactical structure. For instance, [t] became [k] and [k] became [p]; thus, Polish [tak]
became Térus [kap] with [a] remaining unchanged (Postica, 2006, p. 34). The dialogs
used in the experiment were originally drafted in English, and later translated into Térus
by Dr. Coleman.
Participants. Participants of the study were university students selected from
Composition I and II classes during Fall 2013 and Spring 2014 semesters of the
University of Toledo. The instructors of the targeted Composition I and II classes were
contacted via email, and their assistance was requested in conducting the experiment with
their students for 15-20 minutes during one of the regular class sessions. After obtaining
the permission from the instructor of the class, the members of the research team visited
the class at the prescheduled time, briefed the students about the experiment, and asked
them if they would like to participate in the experiment. All the participants were adult
(18+ years old) unpaid volunteers, both male and female. There was no academic credit
awarded for participating in the experiment. Participants were divided into two groups:
Experimental group and Control group. The grouping of the participants was done
following random stratified method (group matched by size and level). The entire class
was selected either as an experimental group or as a control group.
Instrument
The participants of the study read and heard a series of mini-dialogues
(conversations) among three characters (students) in a classroom scene. Timothy
Escondo, Jeremy Holloway, Sultana Nargis, Rosemary Song, and Yifan Zhao, students in
Applied Linguistics I class of the University of Toledo, worked with Professor Douglas
27
Coleman and produced the mini-dialogues used in this study. The texts of the mini
dialogs in Térus (target language) along with English translations were shown on the
Power Point slides. The participants in the experimental group saw (for generating mental
imagery) the following instructions on a separate (additional) slide: “Try to see the action
in your mind as you are reading and listening.” The experimental group further saw
parenthetical materials (stage directions) next to each dialog text. The participants in the
control group did not see the additional slides and the parenthetical “stage directions”.
The control group saw the exact same texts of the mini dialogs in Térus along with
English translations. A sample of the dialogs with parenthetical stage direction used in
the experimental group is shown in Figure 3.
Figure 3. Sample mini-dialog with parenthetical stage direction used in the experimental
group
Procedure
Each session of the experiment was administered by the members of the research
team. The experimenters started the session by starting the Power Point slides on the
28
overhead projector. The experimenters distributed consent forms, explained the consent
process, and collected the forms after the participants read and signed the forms. A
sample of the consent form is presented in Appendix A. Following the consent procedure,
the instructions to participants were shown on the Power Point slides. The text of the
instructions is presented in Appendix B. Each dialog was played three times
automatically with a three second interval. There were three mini dialogs. The minidialogs are presented in Appendix C. The participants were allowed to read along the
dialogs. Following the dialogs, the participants were given a five minute study period for
comprehending the target language.
Study sheet. The participants used a study sheet consisting of i) the texts of the mini
dialogs as seen in the Power Point slides in target language along with English
translations, and ii) a vocabulary list (not shown on the Power Point slides) of the words
used in the mini dialogues. The study sheet is presented in Appendix D.
The assessment test. Following the study period, the participants were given an
assessment test. The test consisted of ten questions to measure knowledge of accuracy
(what is usually considered to be syntactically and grammatically accurate in the target
language) and knowledge of meaning – sensible (coherent and relevant) in the target
language from a pragmatic (HSL), not a linguistic-semantics point of view. Each question
had the following four types of responses.
1) Both syntactically accurate and pragmatically meaningful in the target
language (accurate and sensible)
2) Syntactically accurate but not pragmatically meaningful in the target language
(accurate but not sensible)
29
3) Pragmatically meaningful but not syntactically accurate in the target language
(not accurate but sensible)
4) Neither syntactically accurate nor pragmatically meaningful in the target
language (neither accurate nor sensible)
Order of the responses was randomized from question to question. In each of the test
items, all four responses are constructed to measure if the participants of the study
learned something generalized from the mini dialogues instead of simply having the part
of dialogues memorized. None of the four responses actually matched a segment of the
mini dialogues.
In addition to the assessment test, the participants were asked several other survey
questions to identify the learning strategies used by the subjects during their study period.
The test is presented in Appendix E. The participants got five minutes to complete the
assessment test and the learning strategy questionnaire. The answer sheet and the strategy
questionnaire are presented in Appendix F. The participants were not asked to answer
demographic information (age, gender, etc.).
In a total of eight sessions (sections of the English Composition I and II class), a
total of 175 students participated in the experiment, and 173 effective tests were used for
the data analysis, 115 in the experimental group, and 58 in the control group. Due to
incomplete responses, assessment tests of 2 participants were excluded from the data
analysis. Initially, an equal number of participants (58 each) were selected for each of the
groups. However, there were not enough self-reported imagery participants in the group
available for a secondary test where assessment test results among self-reported imagery
users and self-reported non-imagery users were compared. As a result, additional
30
participants were recruited for the experimental group to create a balance of self-reported
imagery and self-reported non-imagery users in the group.
The tests were then scored twice according to the coding guide (see Fig. 4). Each
test was scored for two factors: (1) accuracy, meaning what is usually considered to be
acceptability of grammatical structures and (2) sense, which referred to comprehension of
the meaning from a pragmatic (HSL), not a linguistic-semantics point of view. When a
subject chose either of the answers that have the accurate form, he/she would accumulate
1 point for the accuracy score, and similarly, for either of the answers that have the
correct meaning he/she would receive 1 point for the sense score. Since there were ten
question items in total, each test received two scores each on the scale of 0-10. See
Appendix G for the complete recoding (scoring) sheet.
Table 1
Recoding (scoring) guide
Answer
Accurate form and
Sensible
Recoded Variables and Assigned
Values
it_correct = 1
it_accurate = 1
it_sense = 1
Accurate form and
Nonsensical
it_correct = 0
it_accurate = 1
it_sense = 0
it_correct = 0
Inaccurate form and
Sensible
it_accurate = 0
it_sense = 1
Inaccurate form and
Nonsensical
it_correct = 0
it_accurate = 0
it_sense = 0
31
Table 2
Example of recoding of test answers
Item # 1. Tsost.
Recoded Variables and Assigned
Values
Answer
it_correct1 = 0
A ku tsost.
it_accurate1 = 0
it_sense1 = 1
it_correct1 = 0
Kap, ku na?
it_accurate1 = 0
it_sense1 = 0
it_correct1 = 1
Tsost. Wap so na?
it_accurate1 = 1
it_sense1 = 1
it_correct1 = 0
Eshkákmwo.
it_accurate1 = 1
it_sense1 = 0
Hypotheses
HA1 = Subjects instructed to create mental imagery by visualizing of the event
(the experimental group) will score higher than the subjects without instruction
for visualization (the control group).
HA2 = Subjects reported using mental imagery (self-reported imagers) as a
learning strategy will score higher than the subjects reported not using mental
imagery (self-reported non-imagers) as a learning strategy.
H0 = Subjects without instruction for visualization (the control group), will score
equal to or higher than the subjects instructed to create mental imagery by
visualizing of the event (the experimental group).
32
Chapter 3
Data Analysis
The collected data were first entered into a spreadsheet. The data from the
spreadsheet were then loaded into SPSS (Statistical Package for the Social Sciences) for
further analysis. Using the recoding guide mentioned in chapter 2, the data were recoded
into different variables. The data contained one grouping variable in which mental
imagery group was coded as Experimental, and the control group was coded as Control.
Since each subject received two scores, the scores were coded into two variables - one on
accuracy, and one on sense. A response on the test was labelled “accurate” if it followed
a pattern that was seen somewhere in the stimulus dialogues. For example, a speaker's
references to their own states and actions typically end with –an, e.g., nan '(I) have',
tlotlasan '(I) am sorry', nisan 'I have to', etc. Similarly, a response was categorized as
“sensible/meaningful” if there was evidence somewhere in the stimulus dialogues that it
fits the context created by the test item. For example, if someone hands you a pencil and
says, tlesan ('here you go') and you answer gzampewan ('thanks' / 'I thank'), that's
sensible/meaningful because it fits the context according to available evidence. But if you
answer tsost ('hi'), the available evidence shows that the response doesn't fit the context.
Two more variables, each having possible value of 0 to 10, were created to
represent the sum of accuracy (total_accur) and a sum of sense (total_sense) scores of all
the ten items for each subject.
Since the data were not certain to be interval level, the data were analyzed
through a frequency test to determine if the skewness and kurtosis of the two groups were
within the acceptable range of -2 and +2. The results from the descriptive statistical
33
analysis revealed that the skewness of -.152 for the sense scores, and -.376 for the
accuracy scores. Similarly, the results from the analysis showed kurtosis of -.611 and .239 for the sense and accuracy scores respectively. Thus, both skewness and kurtosis of
the data were within the acceptable range.
In order to examine the normality of distribution, a one-sample KolmogorovSmirnov (K-S) test was performed. If the p-value was greater than .05, the test
distribution would be considered normal, meaning the data could be treated as being at
the interval level. The p-value for the K-S test performed on the sense scores was less
than .05, indicating that the data were not normally distributed, and it could not be treated
as being at the interval level. Likewise, the p-value for the K-S test performed on the
accuracy scores was also less than .05, suggesting that the data were not normally
distributed, and it could not be treated as being at the interval level.
Based on the ordinal nature of the data, and the number of groups, the median
scores were compared by using the Mann-Whitney test to see if there was a significant
difference between the two groups: the experimental and control group. The test results
showed that the p-values of both sense and accuracy scores were greater than .05,
indicating that there was not a significant difference in terms of the central tendencies of
the test scores from the two groups. The mean rank of the mental imagery experimenta l
group’s test scores on sense was 93.73 (N= 58), whereas the mean rank of the control
groups’ test scores on sense was 83.60 (N=115). Similarly, on the accuracy test, the mean
rank for the mental imagery experimental group was 83.91 (N= 58), and for the control
group was 88.56 (N=115). The above analysis shows that the mental imagery
experimental group got higher sense score than the control group. However, the
34
difference in sense scores between the two groups is not statistically significant (pValue=.101). Similarly, there was not a significant difference in accuracy scores of the
two groups (p-Value=.278). Thus, I was not able to reject the null hypothesis as the
subjects without instruction for visualization (the control group) scored statistically equal
to the subjects instructed to create mental imagery by visualizing of the event (the
experimental group).
Again, I performed another set of Mann-Whitney tests of the entire sample for
finding out if there is a significant difference of sense or accuracy scores between the
following two subgroups of the subjects: (i) imagery user, who reported using imagery
(responded “yes” to the second question of the strategy questionnaire), and (ii) nonimagery user, who reported not using imagery (responded “no” to the second question of
the strategy questionnaire). The results from the tests show that the mean rank of the
sense scores of the imagery users is 90.17 (N= 59), and the mean rank of the sense scores
of the non-imagery users is 85.36 (N= 114), p-Value = .271. The mean rank of the
accuracy scores of the imagery users is 90.82 (N= 59), and the mean rank of the accuracy
scores of the non-imagery users is 85.02 (N= 114), p-Value = .231. Subjects who
reported using imagery did not perform better either in sense scores or accuracy scores
than the subjects who reported not using imagery since the difference of the mean rank of
the scores between the two subgroups is not statistically significant.
Similar tests were performed among the self-reported imagery users and nonimagery users of the experimental group and the control group separately. The results of
the control group show that there is no difference in either sense or accuracy scores
between the two subgroups. The results of the experimental group show that there is no
35
difference in sense scores between the two subgroups. But, among the control group, the
self-reported imagery subgroup scored higher in accuracy score than the non-imagery
subgroup did. However, the difference of accuracy scores between the two subgroups of
the control group is not statistically significant.
Since I was also interested in finding out whether or not there was any learning
occurred for either of the groups of the subjects, I decided to run a second set of analysis
to compare the assessment test results to chance. As mentioned previously in chapter two,
each of the test items was designed in a way that there was a fifty-fifty chance to get a
correct answer for both sense and accuracy scores. Thus, if learning occurred during the
experiment, the majority of the subjects from a group would receive a score higher than
5, since there were10 test items in total. Similarly, if no learning occurred, the majority of
the subjects would receive a score around 5. For this, I created two new variables,
learning_accur derived from total accuracy (total_accur) , and learning_sense derived
from total sense (total_sense) each of them having two possible values: 0 for total score
ranging from 0 to 5, and 1 for total score ranging from 6 to 10. I compared the learning
accuracy (learning_accur) and learning sense (learning_sense) scores of the two groups
against a fifty percent probability using the Binomial test. In the results for accuracy
learning scores, the mental imagery experimental group performed similar to chance
(48%, which is close to chance; p-Value= .448), meaning they performed no differently
from chance; the control group did demonstrate learning (61%, which is greater than
chance; p-Value=.0125). That is, subjects who received parenthetical stage direction
(mental imagery) did not learn syntactical (grammar) accuracy, but subjects who did not
receive parenthetical direction (control group) did learn. The mental imagery
36
experimental group performed above chance on the learning sense (comprehension) score
(74%, which is greater than chance, p-Value= .00); the control group did the similar
learning on sense score (66%, which is greater than chance, p-Value= .0005).
I had a further interest to find out if any of the six self-reported learning strategies
was a significant predictor of either accuracy (grammar) or sense (comprehension)
learning. Seven factors were considered as possible predictors of learning (Table 3): the
six variables from learning strategy questionnaire, plus the type of treatment the subjects
received. In the statistical analysis, the type of treatment was assigned a variable called
treatment_type, with the values of 1 for the mental imagery experimental group and 2 for
the control group.
Table 3
Possible predictors of learning
treatment_type
Silent
Imagine
Covered
Remember
Dialogue
Vocabulary
I performed a discriminant analysis (stepwise) to identify predictors of learning
scores. From the discriminant analysis, none of the learning strategies or the treatment
type was a significant predictor for accuracy (grammar) or sense (comprehension)
learning. Only somewhat significant predictor of accuracy learning was the silent
reading strategy (Silent): Wilk’s U = .98, Exact F = 3.470, df1 = 1, df2 = 171, p-Value =
.064.
37
Chapter 4
Discussion
How the results of this study differ from previous studies
As mentioned earlier (in chapter one), this study is a follow-up of several previous
studies conducted by faculty and students of English Department at the University of
Toledo, more specifically, the studies of Postica (2006), and Xin (2012). This study is
similar to the study of Postica (2006) in the sense that like Postica’s study, this study is
designed to measure the effect of mental imagery on second language learning. However,
the instruction provided to the experimental group for generating mental imagery of this
study differs from that of Postica’s study. Furthermore, this study has a design feature
similar to Xin’s (2012) study as the experiments of both studies aim to measure subjects’
learning of both syntactical accuracy (form or grammar) and sense (pragmatic
comprehension) accuracy in a target language. Again, this study differs from both the
studies of Postica (2006) and Xin (2012). Unlike Postica’s study, this study did measure
syntactical and sense accuracy separately, and unlike Xin’s study this study did aim to
measure the role of mental imagery in learning to communicate in a second language.
Like the study of Posica, this study also found that there was no difference in
language learning due to subjects' assumed use of mental imagery during the treatment
phase. However, Postica (2006) found a relationship between subjects’ self-reported
imagery usage and language learning which is different from the findings of this study.
The results of Postica’s study indicate that subjects who reported using mental imagery
did better in language learning assessment test than the subjects who reported not using
mental imagery.
38
Limitations and Suggestions for Future Research
Based on Mar (2011) findings that show a positive correlation between mental
imagery and narrative comprehension (fiction reading), there was an assumption that
narratives (parenthetical stage directions) would trigger mental imagery in the experiment
of this study. According to the results of this study, a question occurs whether
parenthetical stage direction is sufficient to create mental imagery. It is rather difficult to
create, control, and measure mental imagery. In this study there was no validity check on
whether the instruction and parenthetical stage direction provided to the subjects of the
experimental group truly attributed to mental imagery during the treatment (language
learning) phase.
Again, the following variation of the design of this study might be a contributing
factor to producing a different outcome of this study from Postica’s.
i) Postica (2006) used a memory test to keep the channel open, whereas this study
did not have a memory test.
ii) Postica (2006) used audiovisual prompts (video clips). On the other hand, this
study used text-only prompts. This may have biased against imagers.
iii) The presence of the parenthetical material ("stage directions") may actually
have been a distraction because their presence gave the participants less time to focus on
the target language (Terus) and its English translations.
iv) The parenthetical material may have had no effect in stimulating non-imagers
to use imagery. If (iii) and (iv) both hold true, we might even expect the imagers
(experimental group) to come out worse off on the comprehension test rather than better.
39
I would like to discuss the following features and properties of mental imagery
which might shed further lights on instrument design of future studies on the role of
mental imagery in language learning.
As mentioned in Chapter One (p. 24), people can be classified into three
categories based on their capacity of generating imagery: imager, occasional imager, and
non-imager (Taktek, 2004, p. 84). Non-imagers generally do not benefit from imagery
experiences, but the other two categories do. Considering the imagery capability of the
subjects, the following would be a better design strategy – the subjects imagery capability
would be assessed first, and then they would be divided into the following categories:
imager, occasional- imager, and non-imager. After the subjects are grouped by imagery
capability, they could be further sub grouped into experimental and control groups. Such
a design could help determine if mental imagery capability of the participants has any
effect on the outcome of the experiments.
The design of the experiment can be further improved by tailoring the instruction.
The instruction of the current study is more likely to generate external imagery which is
less effective than the internal imagery. Mahoney & Avener (1977) defined internal and
external perspectives of mental imagery as the following:
In external imagery, a person views himself from the perspective of an external
observer (much like in home movies). Internal imagery, on the other hand,
requires an approximation of real-life phenomenology such that the person
actually imagines being inside his/her body and experiencing those sensations
which might be expected in the actual situation. (p. 137).
40
Also, a different and irrelevant parenthetical stage direction could be given to the
control group. This would eliminate confounding factor, if any, due to distraction caused
by parenthetical stage direction.
It would also be worthy to explore if gender and handedness had any effect on
mental imagery capacity. According to Paivio and Clark (1990), the results of the
different studies demonstrated that boys possess superior capacities when compared with
girls in terms of dynamic imagery. However, girls have higher capacities for static
imagery. Static imagery is the form of imagery expresses the evocation of stationary and
fixed objects while the dynamic form of imagery expresses the evocation of action scenes
in which the objects are in movement or in the process of transformation (Piaget, &
Inhelder, 1966) or rotation (Kosslyn et al., 1988).
The effect of handedness may be considered in future studies as recent studies
indicate that memory performance is related to handedness (Christman, Propper, &
Brown, 2006; Christman, Propper, & Dion, 2004; Propper et al., 2005), and inconsistent
handedness is linked to more successful foreign language vocabulary learning (Kempe et
al., 2009).
Furthermore, emotional and motivational states (level of anxiety, personality
traits, cognitive style, and confidence level) of the participants are important factors
which may affect performance of mental imagery on acquisition of motor skills (Ahsen,
2001; Hinshaw, 1991; & Murphy, 1994). Studies like this one should consider
participants’ emotional and mental sates, along with the other factors mentioned above.
41
Concluding Remarks
A study of this nature demands a much larger support than what is available in a
master’s study. Imagery is difficult to measure, and it is further difficult to generate
imagery in a controlled manner such as the experimental group of this study. Future
studies aiming to explore the role of mental imagery in classroom learning will have a
better foundation to begin with once it is understood how effectively imagery can be
evoked, and how it can be measured.
Although the results of this study did not produce expected outcome, it has
provided important implications for designing similar studies, such as effectiveness of
parenthetical material in stimulating mental imagery. Moreover, this study was a
significant learning experience for me as it encompassed the role of mental imagery
which is a comparatively new and emerging research area of psychology, second
language learning, and human communicative behaviors. I hope the findings of this study
will guide future studies that aim to explore the evocation and the role of mental imagery
in second language acquisition by overcoming the limitations encountered in this study.
42
References
Ahsen, A. (2001). Imagery in sports, general performance and executive excellence.
Journal of Mental Imagery, 25(3&4), 1-46.
Banich, M. T., & Mack, M. Ann. (2003). Mind, brain, and language : multidisciplinary
perspectives. Mahwah, NJ: L. Erlbaum Associates.
Barsalou, L.W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22,
577-660.
Berger, C., & Ehrsson, H. (2013). Mental Imagery Changes Multisensory Perception.
Current Biology, 23(14), 1367-1372.
Bloom, F.,Charles, A., & Lazerson, A. (2001). Brain, mind, and behavior, 3rd ed. New
York: Worth Publishers.
Botha, R. P. (1992). Twentieth century conceptions of language: Mastering the
metaphysics market. Cambridge, MA: Blackwell.
Bunzeck, N., Wuestenberg, T., Lutz, K., Heinze, H., & Jancke, L. (2005). Scanning
silence: Mental imagery of complex sounds. NeuroImage, 26,1119-27.
Cairns, H. S. (1996). The acquisition of language (2nd ed.). Austin, TX: Pro-Ed.
Carroll, S. (2001). Input and evidence: The raw material of Second Language
Acquisition. Amsterdam: J. Benjamins.
Chevalier, N. (1995). Apprentissage, Imagerie et répétition mentale. In J. Bertsch, C. Le
Scanff, Apprentissages moteurs et conditions d’apprentissages. Paris: Presses
Universitaires de France. pp. 153-188.
Chomsky, N. (1964). Current issues in linguistic theory. The Hague: Mouton.
———. (1968). Language and Mind. New York: Brace and World.
43
———. (1972). Studies on semantics in generative grammar. The Hague: Mouton.
———. (1975)[1998]. Reflections on language. Reprinted in On language: Chomsky’s
classic works Language and responsibility and Reflections on language in one
volume. New York: The New Press.
———. (1980a). Initial states and steady states. In Language and learning: The debate
Between Jean Piaget and Noam Chomsky, ed. M. Piattelli-Palmarini. Cambridge,
Mass.: Harvard University Press. pp. 107-130.
———. (1980b). Rules and representations. Behavioral and Brain Sciences, 3, 1-61.
———. (1986). Knowledge of Language: Its nature, origin, and use.
New York: Praeger.
Christman, S. D., Propper, R. E., & Brown, T. J. (2006). Increased interhemispheric
interaction is associated with earlier offset of childhood amnesia.
Neuropsychology, 20, 336 -345.
Christman, S. D., Propper, R. E., & Dion, A. (2004). Increased interhemispheric
interaction is associated with decreased false memories in a verbal converging
semantic associates paradigm. Brain and Cognition, 56, 313-319.
Clarke, D. S. (1990). Sources of semiotic: readings with commentary from antiquity to
the present. Carbondale: Southern Illinois University Press.
Coleman, D. (2005). Language Learning Input and Input for Learning to Communicate.
Lacus Forum, 31, 203-213.
———. (2007). Required Variability in Input. LACUS Forum, 33, 119-131.
———. (2013). Learning. Retrieved from
http://englvm00.utad.utoledo.edu/moodle/file.php/84/Readings/Learning_v05a.pdf
44
Cook, V. (1991). The poverty-of-the-stimulus argument and multicompetence. Second
Language Research, 7(2), 103-117.
———. (1993). Linguistics and Second Language Acquisition. New York: St. Martin's.
Corder, S. P. (1967). The significance of learners' errors. IRAL, 5, 161-170.
Damasio, A. R. (1999). How the brain creates the mind. Scientific American, December
1999, 112-17.
Danesi, M. (2003). Second language teaching: A view from the right side of the brain.
Boston: Kluwer Academic Publishers.
Denis, M. (1985). Visual imagery and the use of mental practice in the development of
motor skills. Canadian Journal of Applied Sport Science, 10, 4S-16S.
Denis, M. (1989). Image et cognition. Paris: Presses Universitaires de France.
Denis M., Chevalier N., Eloi S. (1989). Imagerie et répétition mentale dans
l'acquisition d'habiletés motrices. In Vom Hofe, A. (Ed). Tâches, traitement de
l'information et comportements dans les activités physiques et sportives. Issy-lesMoulineaux : E.A.P., pp. 11 -37.
Deacon, T. W. (1997). The symbolic species: The co-evolution of language and the brain.
New York: W. W. Norton.
Feltz, D. L., & Landers, D. M. (1983). The Effects of Mental Practice on Motor Skill
Learning and Performance: A Meta-analysis. Journal of Sport Psychology, 5, 2557.
45
Fishburne, G.J. & Hall, C.R. (1987). Visual and kinesthetic imagery ability in children:
Implications for teaching motor skills. In G.T. Barrette, R.S. Feingold, C.R. Rees
& M. Pieron (Eds.), Myths, models and methods in sport pedagogy. Champaign,
IL: Human Kinetics. pp. 107-112.
Gass, S. M., & Selinker, L. (2001). Second language acquisition: an introductory course
(3rd ed.). New York: Routledge/Taylor Francis.
Geschwind, N. (1970). The organization of language and the brain. Science, 170, 940944.
Gold, E. M. (1967). Language identification in the limit. Information and control, 10,
447-74.
Grouius, G. (1992). On the reduction of reaction time with mental practice. Journal of
Sport Behavior, 15(2), 141-157.
Helene, A. F., & Xavier, G. F. (2006). Working memory and acquisition of implicit
knowledge by imagery training, without actual task performance. Neuroscience,
139(1), 401-413.
Hinshaw, K.E. (1991). The effects of mental practice on motor skill performance: Critical
evaluation and meta-analysis. Imagination, Cognition and Personality, 11(1), 335.
Howatt, A. P. R. (1984). A history of English language teaching. London: Oxford
University Press.
Johnson, K. & Mullennix, J.W. (1997). Talker Variability in Speech Processing. San
Diego: Academic Press.
Kelly, L. G. (1969). 25 centuries of language teaching. Rowley, MA: Newbury House.
46
Kempe, V., Brooks, P. J., & Christman, S. D. (2009). Inconsistent handedness is linked to
more successful foreign language vocabulary learning. Psychonomic bulletin &
review, 16(3), 480-485.
Klein, W. (1986). Second Language Acquisition. New York: Cambridge University
Press.
Kosslyn, S. M. (1983). Ghosts in the mind’s machine: Creating and using images in our
brain. New York: W. W. Norton.
Kosslyn, S. M., Thompson, W.L. & Ganis, G. (2006). The case for mental imagery. New
York: Oxford University Press.
Kosslyn, S. M., Cave, C. B., Provost, D., & Von Gierke, S. (1988). Sequential processes
in image generation. Cognitive Psychology, 20, 319-343.
Krashen, S. D. (1985). The Input Hypothesis: issues and implications. New York:
Longman.
Krashen, S. D. (1982). Principles and practice in second language acquisition. Pergamon:
Oxford.
Lawson, A. E. (2003). The neurological basis of learning, development and discovery:
Implications for science and mathematics instruction. Boston: Kluwer.
Lesley, J., Gretchen, S. (1997). The use of mental imagery in athletics. An overview.
Applied and Preventive Psychology, 6(2), 101-115.
Lichtheim, L. (1885). On aphasia. Brain, 7, 433-484.
Lieberman, P. (2000). Human Language and Our Reptilian Brain: The subcortical bases
of speech, syntax, and thought. Cambridge, MA: Harvard University Press.
47
Lieberman, P. (2002). On the nature and evolution of the neural bases of human
language. American Journal of Physical Anthropology, 45, 36–62.
Mackey, A., & Gass, S. M. (2005). Second language research: Methodology and design.
Mahwah, NJ: Lawrence Erlbaum Associates.
MacPhail, E. (1998). The Evolution of Consciousness. Oxford: Oxford University Press.
Mahoney, M. J., & Avener, M. (1977). Psychology of the elite athlete: An exploratory
study. Cognitive therapy and research, 1(2), 135-141.
Mar, R. A. (2011). The Neural Bases of Social Cognition and Story Comprehension.
Annual Review of Psychology, 62, 103-134.
Marks, D. F. (1977). Imagery and consciousness: A theoretical review from an individual
differences perspective. Journal of Mental Imagery, 1, 275-290.
Martin, K. A., & Hall, C. R. (1995). Using mental imagery to enhance intrinsic
motivation. Journal of Sport & Exercise Psychology, 17, 54-69.
McMahon, C.E. (1973) Images as motives and motivators: A historical perspective.
American Journal of Psychology, 86 (3), 465-490.
Mesthrie, R., J. Swann, A. Deumert, and W. L. Leap (2000). Introducing
Sociolinguistics. Edinburgh: Edinburgh University Press.
Miller, G. A. (1967). “Psycholinguistic Approaches to the Study of Communication,” in
Arm, D. L., ed., Journeys in Science. Albuquerque: Univ. New Mexico. pp. 2273.
Morgan, J. L. (1986). From simple input to complex grammar. Cambridge, MA: MIT
Press.
48
Murphy S. M. (1994). Imagery intervention in sport. Med. Sci. Sports Exerc, 26, 486–
494.
———. (1990). Models of Imagery in Sport Psychology: A Review. Journal of Mental
Imagery, 14 (3&4), 153-172.
Overby, L., Hall, C., & Haslam, I. (1998). A comparison of imagery used by dance
teachers, figure skating coaches, and soccer coaches. Imagination, Cognition, and
Personality, 17, 323-337.
Paivio, A. (2006). Mind and its evolution; A dual coding theoretical interpretation.
Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
———. (1986). Mental Representations. New York: Oxford University Press.
———. (1985). Cognitive and motivational functions of imagery in human performance.
Canadian Journal of Applied Sport Sciences, 10(4), 22S-28S.
Paivio, A., & Clark, J. M. (1990). Static versus dynamic imagery. In Imagery and
cognition. Springer US. pp. 221-245.
Piaget, J., & Inhelder, B. (1966). L'image mentale chez l'enfant: Étude sur le
développement des représentations imagées. Paris: Presses Universitaires de
France.
Pinker, S. (1994). The language instinct. New York: Morrow.
Postica, A. M. (2006). Changing focus: From second/foreign language teaching to
communication learning. Thesis (M.A.) - University of Toledo. Available via the
OhioLink ETD Center:
http://etd.ohiolink.edu/view.cgi?acc_num=toledo1147275010
49
Postica, A. M. & Coleman, D. W. (2006). The role of mental imagery in SLA. LACUS
Forum, 33, 474-484.
Propper, R. E., Christman, S. D., & Phaneuf, K. A. (2005). A mixedhanded advantage in
episodic memory: A possible role of interhemispheric interaction. Memory &
Cognition, 33, 751–757.
Reich, P. A. (1973). Competence, performance, and relationa l networks. In Adam
Makkai and David G. Lockwood, (Eds.). Readings in Stratificational Linguistics,
pp. 84-91. Alabama: University of Alabama Press.
Rosenthal, J. W. (2000). Handbook of Undergraduate Second Language Education.
Mahwah, New Jersey: Lawrence Erlbaum.
Roure, R., Collet, C., Deschaumes-Molinaro, C., Delhomme, G., Dittmar, A., & VernetMaury, E. (1999). Imagery quality estimated by autonomic response is correlated
to sporting performance enhancement, Physiology of Behavior, 66(1), 63–72.
Ryan, E. D., & Simons, J. (1982). Efficacy of mental imagery in enhancing rehearsal of
motor skills, Journal of Sport Psychology, 4(1), 41-51.
Sadoski, M., & Paivio, A. (2001). Imagery and text: A dual coding theory of reading and
writing. Mahwah, NJ: Lawrence Erlbaum Associates.
Saleemi, A. P. (1992). Universal Grammar and language learnability. New York:
Cambridge University Press.
Saussure, F. (1959). Course in general linguistics. Translation by Wade Baskin of the
book originally published in French in 1915. New York: Philosophical Library.
Saville- Troike, M. (2006). Introducing second language acquisition. New York, NY:
Cambridge University Press.
50
Screws, D. P., & Surburg, P. R. (1997). Motor performance of children with mild mental
disabilities after using mental imagery. Adapted Physical Activity Quarterly, 14,
119-130.
Stanford University. (1997). Mental Imagery. Retrieved from
http://plato.stanford.edu/entries/mental- imagery/.
Seuren, P. A. M. (1998). Western linguistics: An historical introduction. Malden, MA:
Blackwell.
Taktek, K. (2004). The effects of mental imagery on the acquisition of motor skills and
performance: A literature review with theoretical implications. Journal of mental
imagery, 28(1 & 2), 79-114.
Taktek, K. (2000). Stratégies pédagogiques et apprentissage d’une tâche motrice chez
des enfants de huit à dix ans. Thèse de doctorat inédite, Université du Québec à
Montréal, Montréal.
Taktek, K., Salmoni, A., & Rigal, R. (2004). The effect of mental imagery on the learning
and transfer of a discrete motor task by young children. Journal of Mental
Imagery, 28(3&4), 87-120.
Wrisberg, C. A., & Anshel, M. H. (1989). The effect of cognitive strategies on the free
throw shooting performance of young athletes. The Sport Psychologist, 3(2), 95104.
Xin, Y. (2012). Exploring the Chinese Room: Parallel Sensory Input in Second Language
Learning. Thesis (M.A.) - University of Toledo. Available via the OhioLink:
https://etd.ohiolink.edu/ap:10:0::NO:10:P10_ACCESSION_NUM:toledo1333762
798
51
Yates, F.A. (1966). The Art of Memory. London: Routledge & Kegan Paul.
Yngve, V. H. (1996). From grammar to science: New foundations for general linguistics.
Philadelphia: John Benjamins.
Zurif, E. B., Caramazza, A., & Myerson, R. (1972). Grammatical judgments of
agrammatic aphasics. Neuropsychologia, 10(4), 405-417.
52
Appendix A
Participant’s Consent Form
53
54
Appendix B
Instructions to Participants – Control Group
Control Group Slide 1
Control Group Slide 2
55
Control Group Slide 3
Instructions to Participants – Experimental Group
Experimental Group Slide 1
56
Experimental Group Slide 2
Experimental Group Slide 3
57
Appendix C
Mini-dialogs – Control Group
Control Group Mini-dialog 1
58
Control Group Mini-dialog 2
59
Control Group Mini-dialog 3
60
Mini-dialogs – Experimental Group
Experimental Group Mini-dialog 1
61
Experimental Group Mini-dialog 2
62
Experimental Group Mini-dialog 3
63
Appendix D
Study Sheet
Control Group – Study Sheet
Experimental Group – Study Sheet
64
Appendix E
Comprehension Test
65
Appendix F
Answer Sheet and Questionnaire
66
67
Appendix G
it_correct1 = 0
1
2
A
A
it_accurate1 = 0
it_correct1 = 0
B
4
5
6
7
8
9
10
A
A
A
A
A
A
A
Answer
it_correct1 = 1
C
it_accurate1 = 1
Recoded Variables
and Assigned
Values
it_correct1 = 0
D
it_accurate1 = 1
it_sense1 = 0
it_sense1 = 1
it_sense1 = 0
it_correct2 = 0
it_correct2 = 1
it_correct2 = 1
it_correct2 = 0
it_accurate2 = 0
B
it_accurate3 = 1
it_accurate2 = 0
C
it_sense2 = 0
it_correct3 = 1
A
it_accurate1 = 0
Recoded Variables
and Assigned
Values
it_sense1 = 1
it_sense2 = 1
3
Recoded Variables
and Assigned
Values
Answer
Recoded Variables
and Assigned Values
Answer
Answer
Item
Recoding Sheet
it_accurate3 = 0
D
it_sense2 = 1
it_correct3 = 0
B
it_accurate2 = 1
it_sense2 = 0
it_correct3 = 0
C
it_accurate3 = 1
it_accurate2 = 0
it_correct3 = 0
D
it_accurate3 = 0
it_sense3 = 1
it_sense3 = 1
it_sense3 = 0
it_sense3 = 0
it_correct4 = 0
it_correct4 = 0
it_correct4 = 0
it_correct4 = 1
it_accurate4 = 0
B
it_accurate4 = 0
C
it_accurate4 = 1
D
it_accurate4 = 1
it_sense4 = 1
it_sense4 = 0
it_sense4 = 0
it_sense4 = 1
it_correct5 = 0
it_correct5 = 0
it_correct5 = 0
it_correct5 = 1
it_accurate5 = 1
B
it_accurate5 = 0
C
it_accurate5 = 0
D
it_accurate5 = 1
it_sense5 = 0
it_sense5 = 0
it_sense5 = 1
it_sense5 = 1
it_correct6 = 0
it_correct6 = 0
it_correct6 = 1
it_correct6 = 0
it_accurate6 = 1
B
it_accurate6 = 0
C
it_accurate6 = 1
D
it_accurate6 = 0
it_sense6 = 0
it_sense6 = 1
it_sense6 = 1
it_sense6 = 0
it_correct7 = 0
it_correct7 = 1
it_correct7 = 0
it_correct7 = 0
it_accurate7 = 0
B
it_accurate7 = 1
C
it_accurate7 = 0
D
it_accurate7 = 1
it_sense7 = 1
it_sense7 = 1
it_sense7 = 0
it_sense7 = 0
it_correct8 = 0
it_correct8 = 0
it_correct8 = 1
it_correct8 = 0
it_accurate8 = 0
B
it_accurate8 = 0
C
it_accurate8 = 1
D
it_accurate8 = 1
it_sense8 = 1
it_sense8 = 0
it_sense8 = 1
it_sense8 = 0
it_correct9 = 0
it_correct9 = 0
it_correct9 = 0
it_correct9 = 1
it_accurate9 = 1
B
it_accurate9 = 0
C
it_accurate9 = 0
D
it_accurate9 = 1
it_sense9 = 0
it_sense9 = 1
it_sense9 = 0
it_sense9 = 1
it_correct10 = 0
it_correct10 = 1
it_correct10 = 0
it_correct10 = 0
it_accurate10 = 1
it_sense10 = 0
B
it_accurate10 = 1
it_sense10 = 1
68
C
it_accurate10 = 0
it_sense10 = 0
D
it_accurate10 = 0
it_sense10 = 1