Mem Cogn
DOI 10.3758/s13421-013-0378-6
Grammatical context constrains lexical competition in spoken
word recognition
Julia Strand & Andrea Simenstad & Allison Cooperman &
Jonathon Rowe
# Psychonomic Society, Inc. 2013
Abstract When perceiving spoken language, listeners must
match the incoming acoustic phonetic input to lexical representations in memory. Models that quantify this process propose that the input activates multiple lexical representations in
parallel and that these activated representations compete for
recognition (Weber & Scharenborg, 2012). In two experiments, we assessed how grammatically constraining contexts
alter the process of lexical competition. The results suggest
that grammatical context constrains the lexical candidates that
are activated to grammatically appropriate competitors. Stimulus words with little competition from items of the same
grammatical class benefit more from the addition of grammatical context than do words with more within-class competition. The results provide evidence that top-down contextual
information is integrated in the early stages of word recognition. We propose adding a grammatical class level of analysis
to existing models of word recognition to account for these
findings.
Keywords Auditory word recognition . Language
comprehension . Lexical processing . Psycholinguistics .
Speech perception
Recognizing spoken words seems to occur instantly and effortlessly for most people, but it requires that the listener
rapidly and accurately map the incoming speech signal onto
meaningful representations stored in the mental lexicon. Many
models of spoken word recognition (see Weber &
Scharenborg, 2012, for a recent review) agree that stimulus
input activates multiple representations in the mental lexicon
and that the selected representations compete for recognition.
J. Strand (*) : A. Simenstad : A. Cooperman : J. Rowe
Carleton College, Northfield, USA
e-mail: [email protected]
For example, hearing the word cat will also activate phonologically similar words, including sat and cap. Each similar
competitor in the mental lexicon, often called a neighbor,
competes with the stimulus word, causing words with many
neighbors to be recognized more slowly and less accurately
than those with few neighbors (Goldinger, Luce, & Pisoni,
1989; Kaiser, 2003; Luce & Pisoni, 1998; Sommers, 1996).
In addition to the amount of competition present, other
lexical factors that affect spoken word recognition include
word frequency (frequently used words are recognized more
quickly and accurately; see, e.g., Dahan, Magnuson, &
Tanenhaus, 2001; Luce & Pisoni, 1998), and neighbor frequency (words with high-frequency neighbors are recognized
less accurately than words with low-frequency neighbors; see
Luce & Pisoni, 1998). Although the specific implementations
differ, models of word recognition, including jTRACE
(Strauss, Harris, & Magnuson, 2007), the neighborhood activation model (NAM; see Luce & Pisoni, 1998), MERGE
(Norris & McQueen, 2000), PARSYN (Luce, Goldinger,
Auer, & Vitevitch, 2000), and Shortlist B (Norris &
McQueen, 2008), all incorporate mechanisms that explain
the effects of word frequency and competition on spoken
word recognition.
Although models of word recognition have not included the
grammatical class (e.g., noun, verb) of competitors as a factor
that may influence recognition, a large body of evidence
demonstrates that grammatical class can have strong influences
on language-processing domains (for reviews, see Mirman,
Strauss, Dixon, & Magnuson, 2010; Vigliocco, Vinson,
Druks, Barber, & Cappa, 2011). For example, neuroimaging
and case studies suggest some neuroanatomical distinctions
between the regions required for processing nouns and verbs
(Miceli, Silveri, Nocentini, & Caramazza, 1988; Shapiro,
Pascual-Leone, Mottaghy, Gangitano, & Caramazza, 2001),
including double dissociations for verb and noun retrieval
among patients with aphasia (Damasio & Tranel, 1993). In
Mem Cogn
speech production, word substitution errors are almost always
of the same grammatical class as the target word, providing
evidence that selection of grammatical class occurs earlier in
speech production than does phonological assignment (Fay &
Cutler, 1977). In addition, the tip-of-the-tongue (TOT) phenomenon, in which an individual is temporarily unable to
retrieve and produce a known word, demonstrates the influence of grammatical class on word selection (Abrams &
Rodriguez, 2005; Abrams, Trunk, & Merrill, 2007). When an
individual experiences a TOT state, exposure to a phonologically similar prime word of a different grammatical class than
the target word—for example, a verb prime for a noun target
word—facilitates the resolution of the TOT state, whereas
priming with a word of the same class does not (Abrams &
Rodriguez, 2005). This suggests that class activates grammatically appropriate words during word production and phonologically similar words of the same grammatical class provide
competition.
Despite evidence that grammatical class can influence other domains of language processing, no research to date has
explicitly assessed how grammatical class influences competition among lexical neighbors. Related research has demonstrated that ambiguous words with interpretations that are both
nouns (e.g., glass) are recognized more slowly than ambiguous words with interpretations that are nouns and verbs (e.g.,
bark) (Mirman et al., 2010), suggesting that competition is
stronger between words of the same class. However, Mirman
et al. did not assess the role of lexical competition or the
grammatical classes of the neighbors. In addition, no research
has directly evaluated whether the processes of lexical competition differ when words are presented in grammatical context. Indeed, models of lexical competition are primarily based
on data derived from words presented in isolation or within
carrier phrases that provide no context for the target word
(e.g., “repeat the word cat”) rather than in more meaningful
frames (e.g., “the boy had a pet cat”). However, there is ample
evidence that words are recognized differently in meaningful
context than in isolation. For example, spoken word recognition occurs more quickly in a syntactically meaningful context
than in a nonmeaningful one (Marslen-Wilson & Tyler, 1980).
Listeners are more likely to perceive an ambiguous sound as
to when it precedes a verb (e.g., “we tried . . . go”) than when
it precedes a noun (“we tried . . . gold”) (Isenberg, 1980). In
addition, when presented with an ambiguous phoneme between /b/ and /p/, listeners’ perception of the phoneme shifts
to accommodate sentence context—that is, “she ran hot water
for the p/bath” (Connine, 1987).
Although it is clear that context influences word recognition in some manner, models of word recognition are divided
about how and when that influence occurs. Models of word
recognition that are modular and primarily stimulus driven
(i.e., give priority to the bottom-up signal; Forster, 1978)
would predict that all neighbors are unavoidably activated
even when they are incongruent with grammatical context.
Therefore, in the sentence, “the boy had a pet cat,” these
models predict that sat will be partially activated, even though
it is contextually inappropriate. To address whether contextually inappropriate candidates are considered, Zwitserlood
(1989) presented participants with semantically constraining
sentences that ended in a word fragment, followed by primes
related either to the final word of the sentence or a neighbor of
that word. For example, when a sentence strongly biased the
final word to be captain , after hearing the first syllable,
participants completed a lexical decision on ship (an associate
of captain) or money (an associate of capital ). Zwisterlood
found some facilitation for both associates, suggesting that
context does not constrain activation (but Janse & Quené
2004, for methodological critiques).
In contrast to the modular models (e.g., Forster, 1978),
more interactive models argue that context can serve to limit
or constrain the lexical alternatives that are activated, such that
context influences processing at the earliest stages (Brock &
Nation, 2013; Dahan, Swingley, Tanenhaus, & Magnuson,
2000; Dahan & Tanenhaus, 2004). Using a visual world
paradigm and tracking participants’ eye movements, Dahan
et al. (2000) found that presenting a gender-marked determiner in French reduced activation of grammatically incongruent
neighbors. For example, when a feminine determiner was
presented just before a feminine noun, participants did not
fixate on phonologically similar but grammatically incongruous masculine nouns. Supporting event-related potential data
also show that words that are violations of predictions based
on grammatical context elicit distinctive patterns of neural
activation that differ from those observed from semantic violations (Hagoort, 2003; Kutas & Hillyard, 1984; Van Berkum,
Brown, Zwitserlood, Kooijman, & Hagoort, 2005). These
findings have been used to support a word recognition model
in which context eliminates incongruent candidates and facilitates word recognition in real time, rather than as a secondary
processor that evaluates the lexical selection.
Additional evidence that context is immediately integrated
with phonological information comes from an artificial lexicon study (Magnuson, Tanenhaus, & Aslin, 2008). In this
study, participants learned words representing a series of
shapes (nouns) and textures that could be applied to the shapes
(adjectives). Although some nouns and adjectives were phonologically similar, they did not compete with one another,
provided that syntactic context was available to explicitly
predict the class of the target word. These results further
support the idea that top-down, contextual expectations about
grammatical class may influence which phonologically appropriate competitors are activated.
The studies above demonstrate the strong influences that
contextual information can have on word recognition. However, no research to date has assessed how the processes of
lexical competition change in the presence or absence of
Mem Cogn
context. Given the influence of syntactic expectations on word
recognition (Dahan et al., 2000; Dahan & Tanenhaus, 2004;
Magnuson et al., 2008) and the other domains in which topdown information influences bottom-up processing of speech
(Reicher, 1969; Warren, 1970), we predicted that the grammatical context in which words are presented would influence
the processes of lexical competition. If the preceding grammatical context constrains activation to grammatically appropriate words (in line with more interactive models), then only
competitors that share the grammatical class of the stimulus
word should be expected to compete with it. For example, if
context constrains which words are activated, then in the
sentence “The girl thought about the cat,” the noun mat would
provide competition for the final word, whereas the verb sat
would not. Thus, we hypothesized that words with little
within-class competition (e.g., nouns with primarily verb
competitors) should show a significant benefit from the addition of grammatical context, because the grammatical context
would eliminate the majority of the competitors. However,
words with many within-class competitors should not show as
large an advantage, because the grammatical context does not
substantially reduce the number of activated competitors.
Experiment 1
Lexical variables
Our hypothesis requires a method for quantifying the amount
of lexical competition a given word encounters, as well as a
way of determining how much of that competition comes
from words of the same grammatical class. The most common
method for quantifying lexical competition is to define a
neighbor as any word that can be formed by a single phoneme
substitution, addition, or deletion. For example, neighbors of
boat include moat (a substitution), boast (an addition), and
bow (a deletion). Although the number of neighbors (neighborhood size) a word has predicts word recognition accuracy
(Goldinger et al., 1989; Luce & Pisoni, 1998), neighbor-based
approaches have limitations: They implicitly assume that all
neighbors provide the same amount of competition to the
stimulus word and that words that differ by more than one
phoneme do not provide any competition.
More recently, an alternative measure of calculating lexical
competition, called phi-square density, was proposed (Strand,
2013; Strand & Sommers, 2011). The major difference between neighbor-based approaches and phi-square density is
that the latter quantifies the amount of competition that each
competitor word provides to the stimulus word continuously.
Neighbor-based measures quantify how many words serve as
competitors; phi-square density quantifies how much each
word competes on a graded scale. Given that graded activation
(meaning words are activated to the extent that they are
perceptually similar to the target) is a feature of many models
of word recognition (e.g., Luce & Pisoni, 1998; McClelland &
Elman, 1986), this method more accurately represents the
assumptions of the models. Phi-square density correlates with
measures of neighborhood size but accounts for significant
unique variance in word recognition accuracy beyond that
which is explained by measures of neighborhood size (Feld
& Sommers, 2011; Strand, 2013; Strand & Sommers, 2011).
Phi-square density is based on the phi-square statistic,
which quantifies the perceptual similarity of any phoneme
pair on the basis of the pattern of responses to those phonemes
in a forced choice identification task (Iverson, Bernstein, &
Auer, 1998). For example, when asked to identify /b/ or /v/ in
noise, listeners demonstrate similar patterns of responses (e.g.,
often mistaking them for each other or for /m/, but very rarely
for /h/ or /z/). Therefore, /b/ and /v/ have a high phi-square
value, reflecting this similarity. However, /b/ and /n/ have a
low phi-square value, indicating that listeners choose different
response alternatives for each (instead, opting for /t/ or /d/
when presented with /n/). Phi-square values range from 0
(indicating no overlap in the response categories for two
phonemes) to 1 (indicating identical patterns of responses).
These values correlate highly with forced choice phoneme
identification scores but avoid the limitations of using these
values, including response biases (see Iverson et al., 1998, for
a detailed discussion of these issues). Phi-square values were
obtained from an existing data set (Strand & Sommers, 2011).
To calculate the perceptual similarity of two words, the phisquare values for each phoneme of the stimulus word and each
phoneme of the competitor are multiplied. For example, the
phi-square similarity of vote and boat is Φ2(v|b) * Φ2(o|o) *
Φ2(t|t). Words with more similar segments (e.g., vote and boat)
have higher phi-square similarity values than do words with
few overlapping or less similar segments (e.g., boat and node).
As target words (see Appendix 1), we randomly selected 240
consonant–vowel–consonant (CVC) words (half nouns, half
verbs) from a 1,575-word lexicon that included all CVC words
that have entries in both the Subtitle word frequency norms
(Brysbaert, New, & Keuleers, 2012) and the English Lexicon
Project (ELP; an online database of 40,000 phonologically
transcribed word forms; Balota et al., 2007)].1 These databases
provided the phonological transcriptions of the words and the
frequency with which each word occurs as a given grammatical class. The phi-square similarity values comparing each of
the 240 stimulus words and every other word in the 1,575word reference lexicon were calculated. Critically, given the
evidence that words outside the neighborhood can provide
competition (Strand & Sommers, 2011), all CVCs were included as competitors, regardless of whether they would be considered neighbors. Then, each phi-square similarity value was
1
Multiple instances of the same homophone (e.g., feet and feat) were
replaced, as were proper nouns.
Mem Cogn
weighted by the competitor word’s log-transformed frequency
of occurrence (Brysbaert et al., 2012; see Luce & Pisoni, 1998,
for a similar procedure). Therefore, the frequency-weighted
phi-square similarity for vote |boat is higher than that for
moat|boat, both because vote and boat are more perceptually
similar than moat and boat and because vote occurs more
frequently than moat. The frequency-weighted phi-square similarities of the target word and every other word in the 1,575word lexicon were summed to arrive at phi-square density.
To determine how the grammatical classes of the competitors influence recognition for each word, we also calculated the
amount of each word’s phi-square density that comes from
within-class competitors. In this measure (grammatical density), words provide competition only if they have the same
grammatical class as the target word. For example, when the
grammatical density for boat is calculated, the frequencyweighted phi-square density of moat is included, because it
is a noun, but the frequency-weighted phi-square density of
bode is not, because it is a verb. Although this procedure is
relatively straightforward for words that can serve as only one
part of speech, another process is necessary for the vast majority of English words, which can occur as multiple grammatical classes. For example, the word vote occurs most frequently as a verb (in 52% of cases reported in the Subtitle Norms;
Brysbaert et al., 2012) but serves as a noun in the remainder of
the cases. It would be misleading to entirely exclude vote from
the grammatical density of boat because it is not primarily a
noun, but it is equally misleading to treat it similarly to moat,
which occurs as a noun in 100% of cases. Therefore, we
calculated the log-transformed frequency that each word
serves as each grammatical class (Brysbaert et al., 2012) and
weighted the phi-square similarity value by that value. For
example, the phi-square similarity of vote|boat is weighted
by the log-transformed frequency with which vote occurs as a
noun, rather than the log-transformed frequency with which it
occurs overall. Therefore, grammatical density will necessarily
be smaller than phi-square density, because some words (e.g.,
bode in this example) are excluded altogether, and others (e.g.,
vote in this example) are weighted by less than their full
frequency. Finally, these frequency-weighted values were
summed to arrive at the grammatical density.
By calculating both phi-square density and grammatical
density, we can determine the overall amount of competition a
word encounters and the amount of competition that comes
from within-class words. For example, the nouns cake and
wine have relatively similar phi-square densities but differ in
grammatical densities. Cake is grammatically dense, because
the words that provide the most competition to it (e.g., cork
and lake) tend to be nouns. Wine is relatively grammatically
sparse, because much of its competition comes from words of
a different grammatical class (e.g., while and fine).
The goal of this investigation was to isolate the influence of
grammatical density on recognition accuracy in constrained
and unconstrained contexts. However, there are multiple other
lexical variables that influence word recognition accuracy,
including phi-square density, frequency, the length of the word
in milliseconds, and the phonotactic probability (Vitevitch &
Luce, 2004). In order to remove the influence of these variables
on recognition accuracy and ensure that the effects obtained
were attributable to grammatical density alone, it was necessary to generate a measure that represents grammatical density,
with the variance attributable to the other lexical variables
removed. Therefore, we conducted a simple linear regression
predicting the grammatical density of both nouns and verbs
from their phi-square density, frequency, length, and phonotactic probability. This process allowed us to represent the standardized grammatical density, with the influence of the other
lexical variables partialled out (see Gahl, Yao, & Johnson,
2012, for a similar procedure). Because there are many more
nouns than verbs in the word norms (Brysbaert et al., 2012),
nouns will necessarily have higher grammatical density; standardizing the noun and verb grammatical density separately
allows us to compare nouns and verbs on the same scale.2
Table 1 shows the comparison between cake and wine, which
are similar on grammatical density and other lexical variables,
but higher grammatical density value for cake leads to large
differences in the standardized grammatical density values.
Although measures based on the phi-square statistic are
relatively new to the field, they provide several advantages to
the traditional neighbor-based approach. First, they allow lexical competition to be quantified on a continuous scale, rather
than categorically, meaning that the extent to which words
compete depends on their perceptual similarity. Second, continuous measures provide a way to overcome the problem of
words that serve as more than one part of speech. If, instead,
we were to define grammatical density as the proportion of
neighbors that are the same grammatical class, we would likely
need to establish some cutoff above which words are classified
as being one part of speech or another. In the example above,
for instance, should vote be classified as a within-class neighbor of boat, or should it be excluded altogether? Using continuous measures of perceptual similarity and weighting them
by continuous measures of frequency that words occur as each
part of speech allows a more fine-grained approach to modeling the influence of grammatical class on lexical competition.
Method
Participants
Forty native English speakers with self-reported normal hearing
and normal or corrected-to-normal vision were recruited from
2
When standardized grammatical density was calculated for nouns and
verbs combined (rather than both classes separately), the main findings
did not differ.
Mem Cogn
Table 1 Descriptive data for sample items to illustrate standardization
process
cake
Phi-square density
Grammatical density
Frequency
Phonotactic probability
Length
Standardized grammatical density
wine
30.69
22.61
3.36
1.18
30.15
16.95
3.49
1.15
551
1.11
602
−1.75
Note . Frequency values were obtained from Brysbaert, New, and
Keuleers (2012); phonotactic probability values were obtained from
Vitevitch and Luce (2004).
the Carleton College community. Participants (28 female, 12
male) ranged in age from 19 to 27 years (M = 20.8, SD = 2.2).
Testing took approximately 30 min, and participants were
awarded $5 for their time. Carleton College’s Institutional
Review Board approved all of the research procedures.
Stimuli
The 240 CVC stimulus words (half nouns, half verbs) were
presented as the final word of short sentences. Half of the
sentences were grammatically unconstrained and allowed the
target word to be either a noun or a verb—for example, “Type
the word + [target].” The other half were grammatically
constrained according to the syntactical norms of simple verb
phrases and noun phrases, such that each sentence led to the
exclusive expectation of a nonfinite verb or a noun—for
example, “The boy began to + [target]” for verbs and “The
boy considered the + [target]” for nouns. The sentences were
constructed in order to limit the influence of semantic constraints on the target word. Then sentences were piloted to
ensure that the above properties were demonstrated appropriately. Stimuli were recorded (using a Blue Microphones Yeti
USB Microphone) and equated on RMS (using Adobe Audacity, version 2.0.2) by a female speaker with a standard
midwestern accent. Words were recorded in isolation and then
edited together with the sentences to eliminate the possibility
of coarticulation in the speech preceding the target word.
that the participants were successfully able to understand the
sentences and, therefore, form grammatical expectations.
Each participant identified 60 nouns and 60 verbs in each
context (i.e., unconstrained and constrained). Stimulus presentations were counterbalanced across participants so that
no participant responded to the same word more than once,
and the order of stimulus presentation was randomized, with
all sentence types intermixed. Critically, for each target word,
the same speech token was presented at the end of the
grammatically unconstrained and constrained contexts, so
the comparison of identification accuracy should depend only
on the constraining context, and not on any idiosyncrasies of
the recording. Participants were presented with sentence
stimuli and were asked to identify the final word of the
sentence by typing their response on a keyboard. They were
encouraged to guess even when unsure. After entering their
response, there was a 1-s intertrial interval before the next
sentence began.
Prior to analysis, the responses were hand-checked for
obvious entry errors. Following the procedure described in
Luce and Pisoni (1998), responses that were phonologically
identical to the target (e.g., guise to guys) were counted as
correct, superfluous punctuation was removed (e.g., fan // to
fan), and, when the entry did not represent a real word but
differed from the target by one letter, the entry was corrected
(e.g., guidew to guide or shein to shine). These corrections
represented approximately 1% of the responses. Word recognition accuracy was then calculated for each of the target
words in both grammatically unconstrained and constrained
contexts. Due to experimental error, 16 words that serve as
both nouns and verbs were presented in an inappropriate
grammatical context. For example, the word vote was presented in noun-constraining context, despite occurring as a
verb slightly more often. Due to this error, these words were
excluded, and the analysis was conducted on the 224 remaining words. Descriptive statistics for the lexical variables for
both nouns and verbs are shown in Table 2.
Table 2 Descriptive statistics for lexical variables for nouns and verbs
Procedure
Participants were seated in a sound-attenuating chamber at a
comfortable distance from an iMac computer (OS X, 10.6).
Stimulus presentation and participant responses were controlled with PsyScope (Version X, B57), and sentences were
presented at approximately 68-dB sound pressure level via the
computer’s internal speakers. The final words (but not the
preceding sentences) were presented in a background of
multitalker babble (signal to noise ratio = −2). The preceding
sentences were presented without background noise to ensure
Frequency
Phi-square density
Grammatical density
Phonotactic probability
Length (ms)
Nouns (N = 109)
Verbs (N = 115)
Mean
2.88
41.76
28.61
1.13
551
Mean
3.40
45.93
18.17
1.14
557
SD
0.76
17.76
12.43
0.04
69
SD
0.96
17.06
7.46
0.04
80
Note . Frequency values were obtained from Brysbaert, New, and
Keuleers (2012); phonotactic probability values were obtained from
Vitevitch and Luce (2004). Because standardized grammatical density
was calculated for nouns and verbs separately, both have a mean of 0 and
an SD of 1.
Mem Cogn
Results and discussion
Although predicting word recognition accuracy from lexical
factors is commonly conducted using an analysis of variance
(ANOVA), this method has serious limitations (Baayen,
Davidson, & Bates, 2008; Dixon, 2008; Jaeger, 2008). As a
result, Dixon and Jaeger advocate the use of mixed-effects
logistic regression analyses, rather than ANOVAs, for accuracy
data, a method that many other psycholinguistic investigations
have recently adopted (Adani, 2011; Bicknell, Elman, Hare,
McRae, & Kutas, 2010; Brouwer, Mitterer, & Huettig, 2012;
Kantola & van Gompel, 2011; Kootstra, Van Hell, & Dijkstra,
2010). The advantages of this method are that it can simultaneously include random effects of subject and item variance, as
well as allow binomial dependent measures (such as accuracy)
to be used directly as the input. Data analysis was conducted
using R (R Development Core Team, 2008) and the R packages lme4 and languageR (see Baayen, 2008). We included
items and subjects as the random effects and included context
(constrained or unconstrained), class (noun or verb), and standardized grammatical density as fixed effects, along with the
critical context ×standardized grammatical density interaction.
Conditional R 2 for the model was calculated following the
procedure described in Nakagawa and Schielzeth (2013; see
Appendix 2 for model output).
The analysis revealed that words were identified more accurately in constrained than in unconstrained context, β = 0.62,
SE = 0.05, z = 12.01, p < .001. There was no significant effect
of class, β = 0.33, SE = 0.21, z = 1.63, p = .10, indicating that
nouns and verbs were identified at approximately similar rates.
There was also no significant effect of standardized grammatical density, β = 0.21, SE = 0.13, z = 1.61, p = .10, demonstrating that standardized grammatical density did not influence
recognition accuracy when collapsing across constrained and
unconstrained contexts. Of most interest to the present investigation was a significant standardized grammatical density ×
context interaction, β = −0.15, SE = 0.05, z = −2.88, p = .004,
indicating that the influence of standardized grammatical density differed depending on whether the context was constrained
or unconstrained (see Fig. 1). In unconstrained contexts, the
relationship between standardized grammatical density and
accuracy was positive, whereas in constrained contexts, the
relationship was negative (although the correlations between
standardized grammatical density and accuracy did not approach significance in either context, p > .42 for both).
The results revealed that standardized grammatical density
had different effects on word recognition when the context
was constrained or unconstrained. When grammatical context
was added, recognition of grammatically sparser words increased, relative to grammatically denser words. This provides
additional evidence (e.g., Dahan et al., 2000; Magnuson et al.,
2008) that grammatical context may serve to constrain the
lexical alternatives that are considered candidates for lexical
Fig. 1 The influence of standardized grammatical density on word
recognition accuracy in constrained and unconstrained contexts
competition. Grammatical context eliminates a larger subset
of the competitors of grammatically sparse words than of
grammatically dense words. Therefore, grammatically sparse
words benefit more from the addition of context than do
grammatically dense words. However, a limitation of Experiment 1 is that the word recognition in the noise task depends
both on online word-parsing processes and on a
postprocessing decisional stage (Pisoni, 1996). The present
results could thus reflect response bias during the decisional
stage; that is, participants opted to report grammatically congruent responses, rather than a process that occurs in real time
during lexical competition. To help address this issue, a second experiment was conducted in which response speed was
also measured.
Experiment 2
Method
Participants
Twenty-six participants with self-reported normal hearing and
normal or corrected-to-normal vision were recruited from the
Carleton College community. Participants (19 female, 7 male)
ranged in age from 18 to 21 years (M = 19.5, SD = 1.2). One
participant was excluded for having a high error rate and long
reaction times (RTs). Testing took approximately 30 min, and
participants were awarded $5 for their time. Carleton College’s Institutional Review Board approved all of the research
procedures.
Procedure
Experiment 2 followed the structure of Experiment 1, but 240
nonwords were also included in addition to the 240 target
words presented in Experiment 1. The nonwords were
Mem Cogn
phonotactically legal English CVCs (e.g., boke and lib). One
hundred twenty of these nonwords were presented in unconstrained contexts, 60 in noun-constraining contexts, and 60 in
verb-constraining contexts. All stimuli were presented in the
absence of background noise at approximately 68 dB SPL.
Each stimulus item was presented to each participant only
once, and stimulus order was randomized, with all sentence
and word types intermixed. Rather than identifying the final
word of the sentence (as in Experiment 1), participants were
asked to determine whether the final word of the sentence was
a real English word as quickly and accurately as possible.
Participants pressed a button with their right hand (using an
IoLab ButtonBox) to indicate that the last word of the sentence was a real English word, and another button with their
left hand to indicate that it was not a word.
Results and discussion
Word analysis
RTs for accurate identifications were obtained and were
square-root transformed. Individual RTs of longer than 2-s
were removed (N = 38)—fewer than 1% of responses. The
analysis paralleled that of Experiment 1, using Markov chain
Monte Carlo sampling to assess significance levels (Baayen,
2008). We included items and subjects as the random effects
and included context (constrained or unconstrained), class
(noun or verb), and standardized grammatical density as fixed
effects, as well as the standardized grammatical density ×
context interaction. The analysis revealed that words were
identified more quickly in constrained than in unconstrained
context, β = −0.85, SE = 0.15, t = −5.72, p < .001. There was
no significant effect of class, β = 0.52, SE = 0.33, t = −1.62,
p = .11. There was a significant effect of standardized grammatical density, (β = −1.13, SE = 0.28, t = −4.04, p < .001),
and a standardized grammatical density × context interaction,
β = 0.57, SE = 0.15, t = 3.78, p < .001, indicating that the
influence of standardized grammatical density differed depending on whether the context was constrained or
unconstrained.
In the unconstrained context, standardized grammatical
density facilitated recognition (led to faster reaction times),
r = −.21, p = .001, but the benefit disappeared in the
constrained context (p = .85; see Fig. 2). The significant
interaction indicates that words that were more grammatically sparse benefitted more from the addition of context
than did more grammatically dense words.
Nonword analysis
The speed with which nonwords are rejected (correctly identified as not being real words) also provides information about
the processes of lexical competition; nonwords with many
Fig. 2 The influence of standardized grammatical density on lexical
decision latency in constrained and unconstrained contexts
neighbors are recognized more slowly than nonwords with
few neighbors (Luce & Pisoni, 1998). RTs to correct lexical
decisions were calculated and square-root transformed. Nonwords in grammatically constrained contexts were rejected
(correctly identified as nonwords) more quickly than nonwords in unconstrained contexts, t(1, 238) = 3.51, p = .001,
Cohen’s d = 0.44. To assess whether nonwords recognition is
influenced by grammatical density as well (independently of
any other lexical factors that may influence recognition), we
also calculated values for lexical competition and grammatical
density for the nonwords that appeared in grammatically
constraining context. We calculated phi-square density values
for each of the nonword stimuli by comparing them with each
of the 1,575 words in the reference lexicon, following the
same procedure that was used for words. For example, fid was
compared with every other word in the lexicon, (e.g., did|fid,
boat|fid, face|fid, NNN|fid), and these values were summed.
Although nonwords have no grammatical class of their own,
we treated the nonwords in noun-constraining context as
nouns and the nonwords in the verb-constraining context as
verbs for the purpose of calculating grammatical density.
Therefore, in the sentence “He thought about the fid,” we
weighted the phi-square density of each competitor by the
frequency with which it occurs as a noun. In the example
above, the phi-square value of did would not be included in
the grammatical density of fid because it cannot occur as a
noun, but the phi-square values of fad and face would be.
Different lists of nonwords were used in the constrained and
unconstrained contexts, and given that it is not possible to
calculate grammatical density of nonwords in the absence of
constraining context, the 120 nonwords in the unconstrained
context have no values for grammatical density. The analysis
described below therefore reflects only the nonwords that
appeared in grammatically constraining contexts.
To examine the influence of grammatical density separately
from that of neighborhood density, we calculated a linear regression to generate residuals for the standardized grammatical
Mem Cogn
density of the nonwords with the influence of phi-square density
removed, following the procedure used above for words. This
analysis was done separately for nonwords in verb-constraining
contexts and noun-constraining contexts. This standardized
grammatical density was used as the criterion variable in subsequent analyses (following the procedure for word analyses).
Nonwords in noun-constraining contexts and verb-constraining
contexts were identified equally quickly, t(118) = 0.19, p = .85,
Cohen’s d = 0.03. Unlike the word analysis, each nonword was
identified only in one context (constrained or unconstrained).
Therefore, a correlation was conducted to assess the relationship
between standardized grammatical density and RT, rather than
using the mixed effects model. In the grammatically constrained
context, standardized grammatical density and RT were significantly correlated: Words with low standardized grammatical
density were identified more quickly than words with high
standardized grammatical density, r = .29, p = .002 (see
Fig. 3). The correlation between standardized grammatical density and RT was significant for both nonwords in nounconstraining contexts, r = .27, p = .04, and for nonwords in
verb-constraining contexts, r = .31, p = .02. This suggests that,
in grammatically constraining contexts, nonwords that are perceptually similar to many words of the appropriate grammatical
class take longer to eliminate from consideration than nonwords
that do not sound like grammatically appropriate words.
General discussion
Thus far, models of word recognition have not incorporated
the effects of grammatical context on lexical competition
Fig. 3 The influence of standardized grammatical density on lexical
decision latency for nonwords in grammatically constraining contexts
during spoken word recognition (but see Mirman et al.,
2010). However, our results suggest that grammatical context
may limit the lexical activation to grammatically appropriate
competitors of the stimulus word. As a sentence unfolds, it
provides expectations about grammatical context and reduces
or eliminates activation for competitors that do not satisfy the
grammatical requirements for a particular target word (e.g., a
verb competitor for a noun target). Although removing competitors will benefit all words to some extent, it preferentially
benefits grammatically sparse words, because a larger proportion of their competitors are eliminated by the context.
The word analyses from Experiments 1 and 2 demonstrate
that the influence of standardized grammatical density depends on whether grammatical context is present. Although
there is a moderate benefit for standardized grammatical density in unconstrained contexts, that benefit disappears in grammatically constrained contexts, at which point standardized
grammatical density impairs recognition. Therefore, responses to grammatically sparse words were facilitated to a
greater degree than to grammatically dense words. The nonword analysis from Experiment 2 provides further evidence
that context limits the set of words that compete. Lexical
decisions to nonwords are assumed to occur when the lexical
search space has been exhausted and no matching candidate is
found (Forster & Bednall, 1976). If grammatical context eliminates incompatible candidates, it reduces the necessary search
space. Thus, when presented with a nonword serving as a
noun that is perceptually similar to only a few other nouns,
listeners search the lexical candidates and more rapidly reject
the stimulus as a word. These results provide further support
for previous findings (Dahan et al., 2000; Magnuson et al.,
2008) that grammatical context affects lexical activation by
reducing activity for competitors that are contextually
inappropriate.
Although current models of isolated spoken word recognition (i.e., PARSYN, NAM, Trace) do not currently include
a mechanism to account for the influence of grammatical
class in lexical competition, small modifications to the architectures of the existing models could explain the present
findings. The TRACE model of word recognition
(McClelland & Elman, 1986; Strauss et al., 2007) is a connectionist model that includes three levels of processing: a
feature layer, a phoneme layer, and a word layer.3 As the
speech stimulus unfolds, activation spreads up from the feature layer through the phoneme layer to the word layer.
Additionally, there are feedback connections from the word
layer to the phoneme layer and lateral inhibition within
layers. The lateral inhibition accounts for lexical competition
effects (lexical nodes within the word layer inhibit one another), and the feedback connections between word and
3
The TRACE is not unique in its ability to be modified to account for these
findings; it is presented here because of its established place in the field.
Mem Cogn
phoneme layers account for top-down lexical processes, such
as the phoneme restoration effect (Warren, 1970).
To account for the present findings, we propose adding a
grammatical class level to the architecture of the model, at a
higher level than the word level. A syntactic or grammatical
class level of processing in spoken word recognition would
parallel the syntactic frame of word production models (Dell,
1986; Levelt, 1999) as well as sentence-processing models
supported by ERP data (Hagoort, 2003). In the production
models, specification of grammatical class occurs separately
from lexical selection. These syntactic frames account for the
findings that word substitution errors usually occur within
grammatical class (Fay & Cutler, 1977); these errors represent
successful syntactic frames but failures in phonological or
lexical selection. The proposed addition of a grammatical
class level closely parallels the principles of a computational
model of parsing, the unification space model (Vosse &
Kempen, 2000). In this model, when a word in the mental
lexicon is retrieved, it activates a lexical frame , which is a
basic syntactic structure that specifies the appropriate grammatical contexts for an activated word. As words continue to
be processed, the lexical frames are bound together to form a
sentence (see Hagoort, 2003, for ERP support for this model).
Given the emphasis of the present research on the processes of
lexical competition, we frame our discussion in light of
models that explicitly include mechanisms to account for
lexical competition but acknowledge that there are many
parallels between this class of models and others (e.g.,
Friederici, 2002; Hagoort, 2005).
The proposed grammatical class level contains bidirectional excitatory connections with the word level. When a listener
hears an utterance, the preceding grammatical context increases activation for the contextually appropriate grammatical class node. Activation from the grammatical class node
spreads down to grammatically appropriate word nodes, raising the activation level for those nodes. As acoustic-phonetic
input feeds up the feature and phoneme levels and begins to
activate the word nodes as well, activation is highest for word
nodes that are both grammatically and phonologically appropriate. Because words that are grammatically incongruent are
less activated than those that are grammatically congruent,
they inhibit the target word less. Therefore, when presented
in grammatical context, grammatically incongruent competitors provide less competition for the stimulus word. Thus,
adding context selectively benefits words with less withinclass competition. The present research is not able to assess
the time course at which the grammatical activation gathers
(relative to the lexical nodes), but future work should seek to
address this.
The grammatical class level of analysis also accounts for
the nonword analysis in Experiment 2. Because nonwords
lack entries in the lexicon, a nonword is correctly rejected
when the system realizes that no lexical entry is consistent
with the phonological input. Given that grammatically
constraining context reduces the number of active lexical
candidates, those words can be examined and rejected as
matching the input more quickly than when many candidate
competitors are present.
The grammatical class level of analysis can also account
for the finding that standardized grammatical density provides
small benefits for RT in the grammatically unconstrained
context. In a grammatically unconstrained context, no topdown information will initially be present, since the sentence
is not preferentially activating any specific grammatical class
node. As bottom-up speech input unfolds, activation spreads
from the feature level to the phoneme level to the word level.
As multiple words of the same grammatical class are activated, that activation accumulates in the appropriate grammatical
class node. Therefore, perceiving a grammatically dense stimulus word will cause heightened activation in the appropriate
grammatical class node, whereas the grammatical class level
activation of a grammatically sparse word will be more dispersed across class nodes. Assuming excitatory feedback connections from grammatical class level to word level, higher
levels of activity in the appropriate grammatical class node
will provide additional activation for the stimulus word. This
additional activation could be sufficient to raise the activation
level of the stimulus word enough to push it over the recognition threshold. For grammatically sparse words, activity in
the grammatical class level will be more distributed across
class nodes, and therefore the stimulus word will receive less
activation from the grammatical class level.
In real-world speech processing, listeners undoubtedly
make use of many top-down, contextual cues to process
spoken language, in addition to the bottom-up acousticphonetic information. Given that, incorporating the influence
of top-down cues into models of word recognition may improve their predictive power. This proposed change to models
of word recognition is consistent with research that suggests
early influences of top-down grammatical information occurring simultaneously with bottom-up acoustic-phonetic activation (Dahan et al., 2000; Magnuson et al., 2008). A grammatical class level allows the model to represent the effects of
grammatical context on the processes of lexical competition
and word recognition by creating a set of candidates that are
both grammatically and phonologically appropriate. Future
research should address how other types of cues, including
semantic or contextual, may also constrain which lexical
candidates are activated for competition.
Acknowledgments This work was supported in part by a grant from
Howard Hughes Medical Institute to Carleton College Interdisciplinary
Science and Math Initiative (Grant 52006286). We are grateful to
Sarah Meerts, Julie Neiworth, and three anonymous reviewers for
helpful comments on an earlier draft. Portions of this research were
presented at the Association for Psychological Science Annual Meeting, May 2013.
Mem Cogn
Table 3 (continued)
Appendix
Table 3 Stimuli used in Experiments 1 and 2
Nouns and verbs used in Experiments 1 and 2
Nouns
Verbs
badge food
luck
song bake
fill
bag
foot
lull
soup beg
fit
ban
fuss
lung
suit
bet
fought
batch game
mace sun
bide
gain
beach germ
mice
surf
boil
gave
beef
goal
mob
tag
bought give
bell
gong
mood thing budge
got
bike
goose moss tour
burn
guess
boss
gown
muck town call
had
cake
guide
mud
toys
catch
has
calf
guys
mug
van
caught heed
make
met
mock
move
need
paid
pave
pull
push
put
ran
shows
shut
sing
sit
soak
take
talk
taught
teach
tell
tied
cash
cause
chain
chase
chum
coach
cub
dash
date
dice
dot
duck
fad
faith
fang
fees
feet
fig
haze
hearse
hedge
hoof
hoop
house
hub
jade
jaws
jib
joys
juice
keys
knife
latch
leaf
lid
lodge
myth
name
neck
nick
pawn
peg
pep
pod
pub
robe
room
rug
sauce
shade
shape
sheen
shin
shirt
took
tuck
turn
use
veer
walk
wash
watch
weave
wed
win
wish
woke
wrote
folk
loop
shoes
vase
vine
wage
wife
wig
wine
year
yen
cease
check
cheer
chose
cook
cope
dare
deem
dig
dine
dug
fade
fail
fall
fed
feed
feel
fell
hid
hide
hope
jog
join
keep
knock
laugh
lead
learn
leave
led
let
lied
lit
look
lose
lurk
rang
reach
read
reek
rid
rowed
run
rung
sang
sat
save
seek
seep
serve
shed
shine
shook
shoot
fight
made
shout
dʒɛr
dʒɝz
dʒig
dʒɪs
dʒoɪg
gɛk
gɛn
gɝn
gid
gɪŋ
kɔs
kʊp
ledʒ
lɛk
lɝd
bɛm
bɛs
biv
bok
bɔn
bug
bul
dʒoɪt
fæl
fæt∫
fæθ
faɪd
fet∫
fɛg
gɔm
gut
guv
hæb
haʊt
hɑdʒ
hem
lit
liθ
lɪ∫
lɪb
lɪg
lɪn
lɪθ
fɛn
zil
fɪk
fod
fɔd
fɔz
fuf
fʊs
fʌb
fʌl
fʌm
fʌt
fʌθ
gæd
gaʊd
ged
geθ
gɛd
hɛz
him
hos
hun
hʌd
hʌp
hʌz
kaɪb
kaɪd
kaɪn
kaʊf
ke∫
keb
kɛp
kɛz
kim
kit
kob
lot
luf
lʌd
lʌs
lʌt
mɑt∫
meb
mɝf
mɝt
miv
mok
mos
mɔk
mɔt
mʌb
mʌn
mʌθ
næf
paɪt
paʊd
paʊn
pɑv
pɛd
pɛdʒ
pɛl
pɝn
pɪd
pʊd
pʊn
pʌŋ
ræf
raʊd
raʊs
rɛm
rɛt
rib
sɛp
sɝd
sɝk
sɝm
sig
sɪf
sos
suf
∫æn
∫et
∫ɛn
∫ɛt
∫ɝn
∫ɪd
∫op
∫oθ
∫un
∫uv
ted
tes
tɝt
tɝt∫
tɝz
tɪd
tɪg
tɪv
toɪk
tɔz
tup
tʊb
tʊt
tʌp
tʌz
θek
θit
θɪg
wɛg
wɛk
wɝn
wɝt∫
wim
wo∫
won
wɔg
yer
yoz
zaɪd
zaɪz
zaʊt∫
zed
zɪr
zol
zɔt
zʌŋ
Table 4 GLMM model output
Experiment 1
Fixed Effects
β
(Intercept)
−1.25 0.35
SE(β) z-value p
−3.61
<0.001
<0.001
0.20
1.63
0.11
Standardized grammatical density
0.13
1.61
0.11
−2.88
0.004
0.21
0.05
12.20
Grammatical context (constrained vs. 0.63
unconstrained)
Grammatical category (noun vs. verb) 0.33
Interaction: Standardized grammatical −0.14 0.05
density × context
Random Effects
s2
Stimulus items (Intercept)
2.13
Participants (Intercept)
0.34
Experiment 2
Nonwords (Transcribed in IPA) Used in Experiment 2
bæf
baɪs
baɪθ
bɑf
bɛdʒ
bʌp
dæk
dɑd
dɑr
des
det∫
dik
dit∫
dɪt
dob
dok
duf
dʌdʒ
dʒaɪd
dʒaɪt
dʒem
dʒev
dʒɛk
næt∫
naɪd
naʊk
nɑf
rin
rit
riz
rɪn
∫ʊm
t∫aɪr
t∫aɪz
t∫ɑt
θud
θuz
θʊŋ
vaɪt
neθ
nɛg
nik
nɪ∫
nɪd
nɔs
nʌk
paɪs
rof
rok
roθ
rʌl
sæb
sædʒ
saɪv
sɛŋ
t∫ek
t∫ɪs
t∫on
t∫ɔl
t∫un
tæs
tæt∫
tɑdʒ
vek
vɪn
vɔl
vɔs
vuz
waɪk
waɪt∫
wes
Fixed Effects
β
(Intercept)
19.66 0.77
25.43
<0.001
Grammatical context (constrained vs. −0.85 0.15
unconstrained)
Grammatical category (noun vs. verb) −0.53 0.33
−5.72
<0.001
−1.61
0.11
−1.13 0.28
−4.04
<0.001
3.78
<0.001
Standardized grammatical density
Interaction: Standardized grammatical 0.57
density × context
Random Effects
s2
Stimulus items (Intercept)
4.68
Participants (Intercept)
6.94
SE(β) t value
0.15
p
Experiment 1: Note. Conditional R 2 = 44%; Number of observations =
8,846; Stimulus items = 224; Participants = 40.
Experiment 2: Note. Conditional R 2 = 35%; Number of observations =
4,550; Stimulus items = 224; Participants = 25.
Mem Cogn
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