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Word repetition, masked orthographicpriming, and language switching:bilingual studies and BIA+ simulationsKevin J.Y. Lam a & Ton Dijkstra aa Donders Centre for Cognition , Radboud University , Nijmegen,The NetherlandsPublished online: 16 Aug 2010.
To cite this article: Kevin J.Y. Lam & Ton Dijkstra (2010) Word repetition, masked orthographicpriming, and language switching: bilingual studies and BIA+ simulations, International Journal ofBilingual Education and Bilingualism, 13:5, 487-503, DOI: 10.1080/13670050.2010.488283
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Word repetition, masked orthographic priming, and language switching:bilingual studies and BIA� simulations
Kevin J.Y. Lam and Ton Dijkstra*
Donders Centre for Cognition, Radboud University, Nijmegen, The Netherlands
(Received 12 September 2009; final version received 29 December 2009)
Daily conversations contain many repetitions of identical and similar word forms.For bilinguals, the words can even come from the same or different languages.How do such repetitions affect the human word recognition system? TheBilingual Interactive Activation Plus (BIA�) model provides a theoretical andcomputational framework for understanding word recognition and word repeti-tion in bilinguals. The model assumes that both phenomena involve a languagenon-selective process that is sensitive to the task context. By means of computersimulations, the model can specify both qualitatively and quantitatively howbilingual lexical processing in one language is affected by the other language. Ourreview discusses how BIA�handles cross-linguistic repetition and maskedorthographic priming data from two key empirical studies. We show thatBIA� can account for repetition priming effects within- and between-languagesthrough the manipulation of resting-level activations of targets and neighbors(words sharing all but one letter with the target). The model also predicts cross-linguistic performance on within- and between-trial orthographic priming with-out appealing to conscious strategies or task schema competition as anexplanation. At the end of the paper, we briefly evaluate the model and indicatefuture developments.
Keywords: bilingual word recognition; language switching; repetition priming;masked orthographic priming; neighbors
1. Introduction
When Polonius asks Hamlet ‘What do you read, my lord?,’ Hamlet replies intriguingly
and ambiguously by saying ‘Words, words, words’ (Hamlet, 2.2.191�2). Researchers
interested in the playful use of language can interpret this statement in their own way,
probably unintended by Shakespeare. It might mean, for instance, that all sentences
consist of words, and that these are repeated time and again. Hamlet’s reply could also
be taken to suggest that words, in the beginning and end, are basically scribbled forms
on paper. In this paper, we combine both notions to investigate the effects of the
repetition of whole and partial word forms on the speed of word recognition. More
generally, our intention is to identify the basic mechanisms that underlie word
recognition in individuals who speak not only one but several languages. Can
accounts of word repetition and form overlap effects be formulated independently of
the language a presented word belongs to?
*Corresponding author. Email: [email protected]
International Journal of Bilingual Education and Bilingualism
Vol. 13, No. 5, September 2010, 487�503
ISSN 1367-0050 print/ISSN 1747-7522 online
# 2010 Taylor & Francis
DOI: 10.1080/13670050.2010.488283
http://www.informaworld.com
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The basic issue of the representation and use of words by language users has been
the focus of monolingual and bilingual research for a long time. This comes as no
surprise when one realizes that words are the basic building blocks of sentences, and
that word recognition times explain most of the temporal variance in language
processing in general (as was already demonstrated by Mitchell and Green 1978).
Understanding how the recognition of isolated words takes place is therefore an
important pre-requisite for understanding how they are understood in sentence
context.Furthermore, it is often thought that the mechanisms underlying word repetition
effects (i.e. faster responses to recently encountered words) are also implied in the
well-known and all-pervasive word frequency effect (i.e. faster responses to
frequently encountered words). Whereas repeating a word would lead to a
temporarily higher activation state of this word, the more frequent usage of a word
would lead to a permanently higher activation state; both result in faster word
recognition times (as we will see later in this paper).
This view of word recognition appears to pre-suppose a kind of ‘magicalmoment’ at which word recognition occurs; thereafter, the state of activation of the
recognized word (and perhaps other similar words) is temporarily changed. To test
this notion, it is fundamentally important to appreciate what happens when we hear
or see words a second time at short or long time lags (from milliseconds to seconds
and longer). Is the word processed differently when it is recognized a second time?
And, how does word recognition ‘work’ when similar rather than identical words
from two languages are presented in succession?
2. Computational models of word recognition
The problem of word recognition is complicated by the numerous variables
contributing to it (Cutler 1981). For instance, word frequency, repetition, language
switching, and the number and frequency of words similar to the target all affect
bilingual word recognition. So how do we make any predictions about the
recognition speed of any particular word, let alone across categories of similar
words? Surely, a verbal account is too general and incapable of disentangling theintricacies of the word recognition process in quantitative detail.
We are convinced that this major shortcoming can be overcome by building a
computational model that incorporates our basic assumptions about word recogni-
tion, and is able to qualify and quantify the interactions between the variables
involved in the word recognition process. In this paper, we will demonstrate how
verbally based principles about the word recognition process and system can be
implemented and tested. We will show that simulations using a computational model
(the Bilingual Interactive Activation Plus (BIA�) model) can make use of exactly thesame stimulus materials that are included in experiments and that these simulations
can offer quantitative predictions with respect to recognition times (for other
advantages of computational models, see Dijkstra and De Smedt 1996, 6�8).
In the following sections, we will discuss our current knowledge of the word
recognition system in bilinguals (Section 3), formulate a number of principles
underlying word recognition and repetition (Section 4), and show how the partially
implemented BIA�model incorporates these principles (Section 5). In the second
part of the paper, we then discuss how BIA�has been able to account for empiricaldata on repetition, language switching, and masked orthographic priming (Section 6
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onwards). Ultimately, we strive to demonstrate the utility and importance of
modeling as a means to better understand the precise mechanisms employed by
bilinguals in processing and coordinating their two languages. The BIA�model
continues to be developed in order to capture those mechanisms as explicitly as
possible.
3. The bilingual word recognition system
Over the last decade, we have learned a lot about the properties of bilinguals’ visual
word recognition system. We now know, for example, on the basis of a large number
of studies, that the word identification system is ‘profoundly language non-selective’
(Brysbaert, Van Dijck, and Van de Poel 1999; De Groot, Delmaar, and Lupker 2000;
Dijkstra and Van Heuven 2002; Dijkstra, Van Jaarsveld, and Ten Brinke 1998; Jared
and Kroll 2001; Kim and Davis 2003). This non-selectivity implies that lexicalcandidates from different languages are activated in numerous reading conditions.
Language membership does not appear to serve as an early criterion in lexical
candidate selection in bilingual word recognition. In fact, in alphabetic languages the
initial activation of word candidates on the basis of the input letter string proceeds
analogously for items of different languages. This activation is based on the
similarity of the input letter string to stored lexical representations in an integrated
bilingual mental lexicon. For instance, upon presentation of the English word
WORK to a Dutch�English bilingual, not only are English word candidates such asWORK, WORD, and CORK initially activated, but so are Dutch words such as
WERK, WOLK, and WORP. Differences in the activation of lexical representations
from the different languages appear to be quantitative (e.g. dependent on subjective
frequency of usage or orthotactics) rather than qualitative (e.g. due to separate or
different word recognition procedures). Thus, the time to recognize the word WORK
depends on its lexical characteristics, such as its frequency and age-of-acquisition,
and the same applies also to form-similar words, like CORK and WORP (Dutch for
‘throw’), irrespective of the language to which the word belongs (Beauvillain 1992;De Groot et al. 2002; Lemhofer et al. 2008).
4. A bilingual account of masked priming and repetition effects
Grainger and Jacobs (1999) have formulated a number of general principles that areassumed to underlie monolingual word recognition. These principles are so general
that they could also apply to auditory word recognition and perhaps even to
speaking. The principles can also be directly applied to bilingual word recognition,
under the assumptions (discussed above) that the bilingual lexicon is integrated (not
separate for languages) and accessed in a language non-selective way.
The principles start from the general assumption that word recognition can be
seen as a process unfolding over time in which abstract representations of letters and
orthographic word forms are activated and, in the end, selected and identified. Whena letter string is presented as input to the reader, letter representations are activated
on the basis of graphical features (such as horizontal and vertical line elements,
curves, etc.) of the input. Activation of letter representations then accumulates over
time and is fed forward continuously to orthographic word representations (Principle
1). Thus, there is a certain degree of letter-to-word activation. The activation strength
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of word units is a function of the degree of orthographic overlap with the stimulus,
thereby exciting compatible words and inhibiting incompatible ones (Principle 2).
Said differently, a particular orthographic similarity metric comparing the input to
stored representations codetermines lexical activation. Positively activated words
send feedback to their letter representations (Principle 3). This important mechanism
is called top-down feedback. Activated words also inhibit all other activated words
(Principle 4). This is the mechanism of lateral inhibition. The resting-level activations
of word representations are proportional to their frequency of occurrence (Principle5). In line with this principle, high-frequency words have a higher resting-level
activation than low-frequency words. Principles 1�5 apply to the recognition of
words in isolation, irrespective of the language to which the word belongs.
A sixth principle is important to understand what happens in the word
recognition system if a word is recognized and then presented again at a later
time (after a relatively long lag). Repeating an item leads to a repetition priming
effect, which can be defined as a decrease in reaction time (RT) to repeated items
relative to non-repeated items. In this account by Grainger and Jacobs, thefacilitatory repetition effect arises because identification of a word results in an
elevated resting-level activation of the corresponding whole-word representation. In
addition, it is also likely that under certain conditions an inhibitory reset of active
lexical alternatives to the target takes place at this time. According to Principle 6, the
degree of inhibition imposed on non-target candidates depends on the activation
level of the non-target representation in question at the time of target word
recognition. The more active the alternative representation, the more the resting
level of this alternative is temporarily decreased (i.e. it would be recognized moreslowly if presented). Recognizing a target leads to a subsequent increased
suppression of neighbor primes, thus generating smaller competition effects due to
those neighbors when the target is presented again later. In sum, both an elevation of
the target word’s resting-level activation and a lowering of the resting-level activation
of neighbors are assumed to take place when target word recognition occurs.
5. The Bilingual Interactive Activation Plus (BIA�) model
Although initially formulated for monolingual word recognition, the account by
Grainger and Jacobs (1999) can be directly extended to the bilingual domain. In fact,
there already is a model that is consistent with it: the BIA�model proposed in 2002
(Dijkstra and Van Heuven 2002). The model is a bilingual extension of McClelland
and Rumelhart’s (1981) well-known Interactive Activation (IA) model for mono-
lingual word recognition. BIA� is a language non-selective, integrated-lexicon
model that makes a distinction between a word identification system and a task/
decision system. To a large extent, the model incorporates the earlier BIA modeldiscussed elsewhere (Dijkstra and Van Heuven 1998; Van Heuven, Dijkstra, and
Grainger 1998). Simply said, the BIA�model includes the BIA model in its simplest
form (i.e. with non-operational language nodes). In the following, we will describe
the characteristics of the BIA�model with emphasis on the empirical phenomena
that can be accounted for.
The word identification system of BIA� incorporates orthographic, phonologi-
cal, and semantic representations. Both sub-lexical and lexical representations are
incorporated. For example, an orthographic network of nodes consists of variousrepresentational units such as features, letters, words, and language membership (for
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a graphical representation of the model, see Figure 1). The model assumes that
written words are recognized in a series of steps. Suppose one reads the target word
WORK. The curved and straight lines on paper first activate particular letter
representations in memory, such as W, O, and K. These letters then activate possible
words, such as CORK, FORK, WORN, and WORK. Such form-similar words,
called ‘neighbors,’ differ by only one-letter entry from the target word. Next, the
target and its neighbors feed activation back to the letters of which they consist and
suppress other irrelevant letters. Through a gradual process of activation and
elimination via competitive inhibition (activation reduction), the neighbors are
excluded as potential targets until only the presented word, WORK, remains solely
active. Complex interactions between letters and words make the activation process
very difficult to predict without actual simulation.
Although only the orthographic part of the lexicon has so far been implemented,
the phonological and semantic parts are assumed to work more or less analogously.
They, too, consist of representational units that are linked within a symbolic, localist
connectionist network. The same holds for the language nodes, which in the
BIA� model indicate the language to which each particular word belongs (e.g.
English or Dutch).
Figure 1. Graphical illustration of the BIA�model. Both a word identification system anda task-decision system are included. Language nodes represent the language membership ofwords only. Sub-lexical units include features and letter representations. (Reproduced fromDijkstra and Van Heuven 2002.)
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The task/decision system of BIA�specifies the different cognitive steps to be
taken and the decisions that must be made to accomplish the goal of the reader (see
Dijkstra and Van Heuven 2002; Green 1998). This can, for instance, be the naming of
a presented word (word naming), pressing one button or another depending on
whether the presented letter string is a word or not (lexical decision), or determining
whether the object that the word represents is bigger or smaller than a pre-
determined object of comparison (semantic categorization).
Simulations with the orthographic implementation of BIA� have been quitesuccessful thus far (for a review, see Dijkstra and Van Heuven 1998, 2002). For
instance, BIA�has been able to account for L1 and L2 word frequency effects, L1
and L2 neighborhood density (the number of words similar to the target word)
effects, relative L1 and L2 proficiency effects, previous item effects, sub-lexical
orthographic effects, and some interlingual homograph effects. In addition, the
BIA�model is able to generate a verbal account of several additional findings, such
as cross-linguistic semantic and phonological effects for cognates (Dijkstra et al.
2010) and false friends (for a review, see Dijkstra 2005b), the recognition of words inbilingual sentence context (Dijkstra, Van Hell, and Brenders, forthcoming), and the
recognition of words in the bilingual brain (Van Heuven et al. 2008). In this paper, we
will present simulations involving the orthographic component of BIA� (henceforth
referred to as the BIA� framework) that attempt to account for masked
orthographic priming effects, repetition priming effects, and word-priming effects
within- and between-languages at long and short time lags. (Apart from some
negligible differences, the presented simulations and analyses are the same as those
reported by Dijkstra, Hilberink-Schulpen, and Van Heuven 2010.)
6. Orthographic and repetition priming within and across languages
Two priming experiments by Dijkstra, Hilberink-Schulpen, and Van Heuven (2010)
demonstrate that the processing principles provided by Grainger and Jacobs (1999)
for monolingual repetition priming can be readily applied to the bilingual domain.
The second experiment of that study was concerned with cross-linguistic priming. In
that experiment, 41 Dutch�English bilinguals performed a Dutch lexical-decisiontask on target words that were repeated four times and were preceded by primes. The
target words were always Dutch words or non-words, whereas the primes were always
English words or non-words. At each presentation, target words were preceded by a
masked prime of one of four different types. The primes were masked and presented
for 60 ms and were rotated across targets following a Latin square design. More
specifically, the targets consisted of 20 Dutch five-letter words, whereas primes
consisted (in total) of 20 related English words (highest-frequency neighbors),
20 unrelated English matched controls, 20 related non-words, and 20 unrelated non-words. Additionally, there were 20 non-word targets preceded by the four different
prime types across blocks. In total, 160 prime-target pairs (e.g. large-LARVE) were
administered in four blocks (in this study, target words were presented in uppercase
letters).
The results are shown in Figure 2. They indicate that unrelated word primes and
unrelated non-word primes yielded similar outcomes across the four blocks. Thus, RTs
to item pairs like death � LEPEL (where LEPEL is Dutch for ‘spoon’) and grail �BOURE were very similar. Related non-word primes, however, such as in lepal �LEPEL, led to facilitation effects across all four blocks. This is in contrast to related
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word primes, e.g. level � LEPEL, which demonstrated slight inhibition effects in
Block 1 that subsequently turned into facilitation effects for subsequent target word
presentations. The authors argued that the resulting RT pattern for this English�Dutch priming study was in line with the repetition priming account from Grainger
and Jacobs (1999). Thus, they attributed the facilitation effect for non-words to sub-
lexical overlap, while inhibition was attributed to cross-linguistic lexical competition.
The general RT effects across blocks were accounted for by target repetition.
Importantly, with respect to non-word targets, a facilitation effect was observed if the
prime was a related non-word rather than an unrelated non-word. Note that this
suggests that both sub-lexical overlap and shared lexical status (i.e. both prime and
target being non-words) may have had effects on the RTs in this study.
Simulations were conducted within the BIA� framework to see to what extent
the empirical results could be mimicked. The model revealed strong inhibition effects
in Block 1 for related compared to unrelated conditions. By merely varying the
resting-level activation of the target and its neighbors, the authors simulated the
relatedness facilitation effects for word targets in Blocks 2 and later. In later blocks,
the resting-level activation for targets was increased and for neighbors decreased. In
the simulations, the processing times (in cycles) for unrelated word and non-word
prime conditions overlay each other (see Figure 3). Simulation points (in time cycles)
and empirical data points (in ms) were highly correlated, indicating that the extended
model was able to capture the most important aspects of the data.To examine if the cross-linguistic results in this priming study would differ from
within-language results, another experiment was conducted in which both prime and
target words belonged to the L1 (Dutch) of the participants. In this within-language
Figure 2. Mean RTs for word targets in the related and unrelated word and non-word primeconditions across blocks for English (L2) primes and Dutch (L1) targets. (Adapted fromDijkstra, Hilberink-Schulpen, and Van Heuven 2010.)
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priming experiment (Experiment 1 of this study), 39 participants performed a Dutch
lexical-decision task, in which they were presented with repeated targets preceded by
masked Dutch primes using exactly the same design as before. As an example of a
prime-target pair, the prime word ‘kwart’ (Dutch for ‘quarter’) would precede the
target word KWARK (Dutch for ‘cream cheese’). The within-language repetition
results were similar to the cross-language repetition results of the earlier reported
experiment, but there were some notable differences. In Block 1, conditions with
unrelated non-word primes were somewhat faster than expected, but responses were
slower with unrelated word primes. This led to similar RTs for related and unrelated
prime-target words, both for word and non-word primes. Said differently, the
expected relatedness effect arose neither for words nor for non-words (see Figure 4).
We will return to this puzzling result later, when we discuss the similarity of our
studies to a study by Bijeljac-Babic, Biardeau, and Grainger (1997). In Blocks 2�4,
unrelated word primes and non-word primes yielded outcomes similar to those of the
cross-linguistic priming experiment. Related non-word primes resulted in facilitation
effects at the outset, whereas the effects for related word primes turned into
facilitation only subsequently. As in the cross-linguistic priming experiment,
facilitation was attributed to sub-lexical overlap while inhibition was attributed to
lexical competition. RT effects across blocks (i.e. faster responses in later blocks)
were ascribed to target repetition.
To a large extent, the combined results of the two experiments give no reason to
assume any differences between within- and between-language repetition priming.
The verbal repetition account proposed by Grainger and Jacobs (1999) appears to
provide a reasonable account of the data. In addition, we have shown that the
BIA�model is able to account for the results of both experiments by increasing the
Figure 3. Simulation results of BIA� framework. Mean number of time cycles for wordtargets in the related and unrelated word and non-word prime conditions across blocks.(Adapted from Dijkstra, Hilberink-Schulpen, and Van Heuven 2010.)
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resting-level activation for targets, and lowering the activation for neighboring
items. However, while the BIA� account works generally well, the empirical data do
display some puzzling aspects. For instance, the remarkable absence of relatedness
effects in Block 1 of the cross-linguistic experiment needs to be addressed. Note that
the related condition was slower than the unrelated condition for English�Dutch
prime-target pairs (Experiment 2, Figure 2), but hardly differed for Dutch�Dutch
prime-target pairs (Experiment 1, Figure 4).
7. Orthographic priming within and across trials and languages
In a separate study, Mansfield, Dijkstra, and Schiller (forthcoming) were interested
in determining whether the BIA�framework is better suited to simulating only the
effects of within-trial priming (where the presentation lag between prime and target
word is in the order of milliseconds) or of between-trial priming (where the
presentation lag between previous and subsequent word is in the order of seconds).
To appreciate the need for this distinction, we must realize that the BIA�model was
originally proposed as a model for bilingual word recognition (rather than priming).
On one hand, we would naturally expect it to simulate the effects of recognizing a
target word preceded by a recent prime word that was not consciously recognized.
On the other hand, however, the BIA�model is not necessarily equipped to account
for the effects of a previous item more distantly encountered in a list on the present
target item, because both prime and target would be recognized as distinct events.
Nevertheless, the mechanism underlying priming (i.e. pre-activation of a later
arriving target word by the prime) has been (perhaps unjustifiably) used to explain
between-trial effects in earlier studies. As yet, this latter observation has not been
Figure 4. Mean RTs for word targets in the related and unrelated word and non-word primeconditions across blocks for Dutch primes and Dutch targets. (Adapted from Dijkstra,Hilberink-Schulpen, and Van Heuven 2010.)
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formally tested. Whether the two types of priming differ in their empirical results and
are both under the jurisdiction of the BIA�model was the goal of the study to be
discussed in this section.
To assess this issue, Mansfield, Dijkstra, and Schiller conducted two masked
priming experiments with Dutch�English bilinguals to establish what the empirical
differences between within- and between-trial priming are in the first place. In their
first experiment, within-trial switching effects were examined. Participants made
generalized lexical decisions on targets that were preceded by related or unrelated
masked primes of the same or different language. Participants were asked to press a
‘yes’ button to both English and Dutch target words, and give a ‘no’ response to non-
words. The target stimuli consisted of 18 Dutch and 18 English four-letter words, and
18 four-letter non-words. The prime words consisted of 18 related English words and
18 unrelated English matched controls, and 18 related Dutch primes and 18
unrelated Dutch primes. Again, the primes all consisted of four letters. Every target
item therefore appeared in four conditions: related non-switch (i.e. same language),
related switch (different language), unrelated non-switch, and unrelated switch.
Examples of the conditions, in order, were male-mall, maal-mall, vote-mall, hoek-
mall for English targets, and klap-krap, warp-krap, hout-krap, and calm-krap for
Dutch targets. Participants saw all target items twice; once in the related condition
and once in the unrelated condition.
As can be seen in Figure 5, Dutch (L1) target words led to somewhat faster RTs
than English (L2) target words. For both languages, there were clear inhibitory
effects of language switching and relatedness, and an interaction between relatedness
and language switch. Related conditions showed larger language switching effects.
Furthermore, it is interesting to note that for English�English prime-target
Figure 5. Mean RTs for word targets in Dutch (L1) and English (L2) on same- or different-language trials in the related and unrelated word conditions. (Adapted from Mansfield,Dijkstra, and Schiller, forthcoming.)
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combinations, the RTs in the related condition were slightly faster than in the
unrelated condition (by 11 ms).
Because relatedness can be assumed to exert its effects within the lexicon (i.e.
neighbors of the target are activated and compete), the interaction between
relatedness and language switch suggests that the effect of language switch must
also be at least partially lexical in nature. Thus, it appears that not only task schema
level effects underlie the slower RTs in language switching trials (Green 1998), but
also lexical effects. To clarify, a task schema is a mental higher-order structure
assumed to mediate between lexical processing and a specific behavioral response. It
incorporates a series of cognitive operations or actions that lead to a pre-specified
goal, e.g. correctly performing a lexical-decision task. The selection and use of a
particular task schema is accompanied by the inhibition of other, non-relevant task
schemas. At the moment when performing a task necessitates a switch in task
schema, a switch cost is manifested, for instance, as an increase in RT. The present
study indicates that switch costs may indeed arise, though not only at the level of the
task schemas but also in the lexicon, because the task schema remained constant.In the second experiment conducted by Mansfield, Dijkstra, and Schiller,
between-trial switching was investigated. Participants again made generalized lexical
decisions on the same set of target words, but these were now preceded by related or
unrelated items of the same or different language, not as primes on the present trial,
but as targets on the previous trial. All other aspects of the experiment were
analogous to the first experiment.
As in the within-trial priming situation, faster RTs were obtained for Dutch than
for English target words. Similarly, there were again inhibitory effects of relatedness
and language switching, and an interaction between the two factors. Also, a small
facilitatory RT difference (14 ms) was found between the related and unrelated non-
switch conditions for English target words.
Remarkably, the results of this between-trial priming experiment were somewhat
faster but otherwise very similar to the previous one involving within-trial priming
(see Figure 6). As noted, the results suggest that priming across languages takes placeat least partially within the lexicon, given that there was an interaction of
orthographic overlap (relatedness) and language switching. Crucially, the similarity
in results of the within-trial priming and between-trial switching experiments appears
to rule out the involvement of conscious strategies in the second experiment. (As
discussed by Mansfield, Dijkstra, and Schiller, this may have some consequences for
the assumption of the reset mechanism mentioned by Grainger and Jacobs 1999.)
The results of the two experiments were simulated in Mansfield, Dijkstra, and
Schiller by means of the BIA� framework. As can be seen in Figure 7, the
simulation study showed that for these stimuli both the language-switch effect and
relatedness effects observed in the two experiments can be replicated. Because the
result patterns of the two experiments are very similar, the common pattern can
be mimicked by one set of BIA� simulations. In this respect, BIA� would be
capable of accounting for both within- and between-trial priming. Importantly, the
interaction between the two effects did not surface when the English word
frequencies used in the simulation were those experienced by a monolingual English
speaker (taken from the CELEX database; Baayen, Piepenbrock, and Van Rijn
1993). However, the interaction did appear when the English frequencies weredivided by four, in order to create the more realistic situation of a relatively lower
subjective frequency of English usage in Dutch�English bilinguals.
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8. Comparison of the two studies
Although the two studies by Dijkstra, Hilberink-Schulpen, and Van Heuven (2010)
and Mansfield, Dijkstra, and Schiller address somewhat different issues, they are
comparable with respect to their manipulations in the first block. Both studies
involved orthographically related and unrelated masked prime-target combinations
that either were or were not accompanied by a language switch. Examples of item
pairs used were, respectively, level-LEPEL and kwart-KWARK, wrap-krap, and
klap-krap. Even though the first study involved a language-specific lexical-decision
task and the second a generalized lexical-decision task, the results in the first block
were similar: longer RTs for related than for unrelated English�Dutch prime-target
conditions, and no clear differences between related and unrelated Dutch�Dutch
prime-target conditions. Thus, there is a consistent trend of longer RTs for related
items vs. unrelated items in the English�Dutch condition, but no difference between
items in the Dutch�Dutch condition.How can the absence of relatedness effects for the Dutch�Dutch conditions in
both studies be explained? One possibility is that the trade-off between lexical
inhibition effects and sub-lexical facilitation effects is different for English�Dutch
and Dutch�Dutch prime-target pairs. Null-effects in Dutch�Dutch prime-target
pairs would arise if the lexical competition of prime and target would be
compensated for by the sub-lexical facilitation due to letter overlap. However, this
account would predict facilitation effects for English�Dutch prime-target pairs,
because English words are less strongly represented and therefore less competitive at
the lexical level. Instead, a trend toward inhibition for English�Dutch pairs was
observed. It would therefore be necessary to make an additional assumption, i.e. that
Figure 6. Mean RTs for word targets in Dutch and English on non-switch or switch trials inthe related and unrelated word conditions. (Adapted from Mansfield, Dijkstra, and Schiller,forthcoming.)
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the stronger lexical representation of the Dutch primes (relative to English primes)
would deliver stronger feedback to the letter level, making the activation at the letter
level much stronger in the Dutch�Dutch case than in the English�Dutch case. (Yet
another explanation might be sought in different criteria settings; see De Moor,
Verguts, and Brysbaert 2005.)
9. Comparison to other studies
Issues similar to ours were addressed by Bijeljac-Babic, Biardeau, and Grainger
(1997). These authors conducted two language-specific lexical-decision experiments
with English (L2, Experiment 1) and French (L1, Experiment 2) four-letter target
items, preceded by masked primes presented for 57 ms. In both experiments, the
prime was either of the same language as the target or not, and was either
orthographically related to the target or not.For high-proficiency participants, the L1 targets (Experiment 2) led to similar
results as in Mansfield, Dijkstra, and Schiller (forthcoming). An inhibitory
relatedness effect, an inhibitory language switch effect, and an interaction between
the two were obtained (see Figure 8, left panel). However, the L2 targets (Experiment
1) led to different results than in Mansfield, Dijkstra, and Schiller (Figure 8, right
panel). Although the effect of relatedness was still inhibitory, language-switch
conditions were faster here than in non-switch conditions. (Bijeljac-Babic, Biardeau,
and Grainger presented the results separately for within- and between-language
conditions.) The results of this first experiment were replicated by the original BIA
Figure 7. Simulation results of BIA� framework. Mean number of time cycles for wordtargets in the unrelated and related conditions involving a language switch or not. Englishword frequencies used for the simulation were divided by 4. (Adapted from Mansfield,Dijkstra, and Schiller, forthcoming.)
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model, in a simulation that was similar to the one we presented above (for the first
presentation block).
It seems likely that differences in experimental designs and/or stimulus materials
of the various studies underlie at least some of the differences in results. The Dutch�English participants of Mansfield, Dijkstra, and Schiller performed a generalized
lexical-decision task, whereas the French�English participants of Bijeljac-Babic,
Biardeau, and Grainger made language-specific lexical decisions. Furthermore,
Bijeljac-Babic, Biardeau, and Grainger indicate that their ‘prime-target relatedness
was rotated across two groups of subjects so that each target word was preceded by a
related and an unrelated prime in different subjects.’ Overall RT differences in related
and unrelated prime conditions may have resulted from this situation in which a
given (un)related prime-target pair was presented once to participants. In contrast,
the participants in Mansfield, Dijkstra, and Schiller encountered each target twice, as
mentioned earlier in Section 7: once preceded by two switch primes, another time by
two non-switch primes. The repetition of a target word in Mansfield, Dijkstra,
and Schiller may also have had its consequences, as the study by Dijkstra, Hilberink-
Schulpen, and Van Heuven (Section 6) discussed above so clearly shows.
It could be argued that differences in stimulus materials must also play a role,
because the BIA�model is able to simulate the different response patterns on the
basis of the word materials specific to each experimental study. Future empirical
studies should attempt to disentangle the empirical contribution of these various
factors.
A recent masked priming study using event-related potentials (ERPs) by
Chauncey, Grainger, and Holcomb (2008) provides other evidence in favor of lexical
processes as the locus of switch costs in the absence of conscious strategies (cf.
Figure 8. Mean RTs for word targets in French (L1) and English (L2) on non-switch orswitch trials in the related and unrelated word conditions. (Adapted from Bijeljac-Babic,Biardeau, and Grainger 1997.)
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Section 7). The authors advocate that more importance should be attached to the
lexical system in accounting for switch costs when overt language switching is absent,
as opposed to an account that bestows exclusivity to executive control processes.
Chauncey, Grainger, and Holcomb reasoned that switch costs should not arise under
conditions that obviate the recruitment of executive control processes, in particular,
when masked priming is coupled with very brief prime duration (50 ms). In two
experiments, differing in prime duration (50 vs. 100 ms), French�English bilinguals
read silently for meaning and responded by key-presses to target words that wereanimal names. ERPs were recorded for critical (non-animal) words that did not
require any key-press responses. Switch cost was inferred from the difference in
magnitude of a pre-determined ERP component for a language switch trial vs. a
language non-switch trial. Switch costs were reportedly evident at very early intervals
of the brain waves, centered on the N250 component, making any involvement of
(late) executive control (and effectively of the task/decision system) unlikely. In other
words, Chauncey, Grainger, and Holcomb reported an early automatic modulation
of language-specific representations by language node information, thereby preclud-ing any influence from the task schema level that is assumed to occur later in
processing. Their study favors a return to the original BIA model in which language
nodes impose top-down influence directly on lexical processing.
However, as we have previously noted, the BIA�model can replicate many other
findings occurring in the absence of conscious strategies; it does this, crucially,
without postulating top-down modulation by language nodes. The data by
Chauncey, Grainger, and Holcomb beckon simulations with BIA� itself to uncover
the actual underlying mechanisms contributing to this fast-acting involvement oflanguage information under covert switching.
10. The BIA�framework and its future
Three of the four studies that we have discussed in this paper were accompanied by
simulations with the BIA� framework. The simulations showed quite clearly how
different processing mechanisms were necessary in accounting for RT differences due
to target repetition, orthographic relatedness, and language switching within- andbetween-trials. On the basis of just a small number of additional assumptions, the
BIA� framework could be applied to these various factors and their interactions.
In all, these simple extensions of the localist connectionist model to new empirical
studies suggest that the model is viable and very much alive. The facilitatory and
inhibitory effects observed in human participants could be simulated computation-
ally on the basis of the set of principles introduced by Grainger and Jacobs (1999).
As such, their account provides insight with respect to the internal ‘engine’ of word
recognition; i.e. the precise mechanisms underlying word retrieval. In a more generalcontext, the simulations clarify how two languages affect (by helping or hindering)
each other at the word level.
In order to make qualitative progress in the near future, there is an obvious need
to integrate semantic and phonological representations into the BIA� framework.
This has been done at a verbal level in the BIA�model, but actual simulations would
be more useful than verbal predictions. Preliminary work has been conducted, for
instance, with respect to grapheme-to-phoneme mapping within the framework of
the SOPHIA-model (Van Heuven and Dijkstra, in progress). This work shows that itis indeed possible to implement the sub-lexical mapping between letters and sounds
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(grapheme�phoneme conversion) for one language in a localist connectionist
network, and that it crucially agrees with the empirical data. However, getting the
mapping to resemble bilinguals’ lexicon is no child’s play. Particularly cumbersome is
the ‘position-specific letter coding’ inherent to the IA framework. This coding
effectively prohibits the spread of activation to any other letter position beyond the
position in question. For instance, the letter L in CLAM cannot and does not
activate a letter representation in the third position, precluding the model from
mistakenly recognizing CLAM as CALM. Nevertheless, human word recognition
does show letter transposition and letter migration errors (see Dijkstra 2005a for a
review of and commentary on characteristics of localist and distributed connectionist
models of word recognition). The immediate implication of this observation is that it
may be necessary to include sub-lexical units in the model that differ from individual
letters (e.g. syllables or onset�nucleus�coda units); and the sub-lexical units in
question might even be different across languages. It may turn out to be easier to
simulate semantic priming and orthographic�semantic effects than to simulate
orthographic�phonological mappings (see Kerkhofs et al. 2006). Other options are
to develop completely new localist or connectionist models for bilingual word
retrieval.
Whatever the ultimate shape of these models to come, we hope to have
demonstrated in the present paper that there is a clear benefit to modeling the
bilingual word recognition process in order to complement empirical studies. In the
studies we presented, simulations and experimental studies involved the same word
materials, which made a direct comparison of data patterns feasible. Thus, the
convergence and divergence of differently designed studies, involving different
languages, words, and tasks, may be better understood and captured in implemented
models that encourage us to go beyond underspecified verbal accounts of the
bilingual phenomena in question.
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