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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: t.dijkstra@donders.ru.nl

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

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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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