Reading English with Japanese in mind

1

Running head: READING ENGLISH WITH JAPANESE IN MIND

Reading English with Japanese in mind: Effects of frequency, phonology, and meaning indifferent-script bilinguals *
	
 
	
 
	
 
1
2
Koji Miwa , Ton Dijkstra , Patrick Bolger1, R. Harald Baayen1 3
1

Department of Linguistics, University of Alberta, Canada
Donders Institute for Brain, Cognition, and Behaviour
Radboud University Nijmegen, The Netherlands
3
Seminar fuer Sprachwissenschaft, Universität Tübingen, Germany
2* The authors are indebted to Marc Brysbaert, Wouter Duyck, Victor Ferreira, SachikoKinoshita, Judith Kroll, and Sarah White for their constructive feedback on an earlier
version of this manuscript. The authors would also like to thank David Allen and Mariko
Nakayama for discussion. Part of this study was presented at the Seventh International
Conference on the Mental Lexicon (2010, Windsor, Canada). Word property data aredownloadable from the first author’s website(http://www.ualberta.ca/~kmiwa/Publications.html).

Address for the correspondence:
Koji Miwa
4-32 Assiniboia Hall
University of Alberta, Edmonton
Alberta T6G2E7, Canada
E-mail: kmiwa@ualberta.ca
Phone: +1(780)492-3434, FAX: +1(780)492-0806

Keywords:

bilingual processing; language non-selective activation; lexical decision;
eye movements; Japanese-English bilinguals

Reading English with Japanese in mind 2
Abstract
Previous priming studies suggest that, even for bilinguals of languages with different
scripts, non-selective lexical activation arises. This lexical decision eye-tracking study
examined contributions of frequency, phonology, and meaning of L1 Japanese words on
L2 English word lexical decision processes, using mixed-effects regression modeling.
The response times and eye fixation durations of late bilinguals were co-determined by
L1 Japanese word frequency and cross-language phonological and semantic similarities,
but not by a dichotomous factor encoding cognate status. These effects were not observed
for native monolingual readers and were confirmed to be genuine bilingual effects. The
results are discussed based on the Bilingual Interactive Activation model (BIA+, Dijkstra& van Heuven, 2002) under the straightforward assumption that English letter units do
not project onto Japanese word units.

Keywords:

bilingual processing; language non-selective activation; lexical decision;
eye movements; Japanese-English bilinguals

Reading English with Japanese in mind 3
Reading English with Japanese in mind: Effects of frequency, phonology, and meaning indifferent-script bilinguals	 While a business person enjoys a cup of coffee to start a day in New York, auniversity professor in Amsterdam lectures on how koffie stimulates neural activity, and a
student with drowsy eyes in Tokyo sips on another cup of コーヒー to concentrate on his
homework. The words coffee /khɔfi/, koffie /kɔfi/, and コーヒー /koohii/ (with the double
vowels representing a moraic long vowel) are examples of cognates. These are word pairs
with a significant degree of semantic, orthographic and/or phonological form overlaps
across languages, which often reflects a cross-linguistic historical link or lexicalborrowing. Although it is easy to see that cross-language semantic similarity motivates
orthographic and phonological resemblance (e.g., coffee in English, koffie in Dutch, andcaffè in Italian), the orthographic similarities of the words in two languages are notalways guaranteed (e.g., coffee in English alphabet, コーヒー /koohii/ in Japanesekatakana, and 咖啡 /kafei/ in Chinese hanzi, with phonological resemblance maintained
across these languages).
When comparing typologically different languages, lexical borrowings are the main
source of cross-language phonological similarity. In Japanese, lexical borrowing is
ubiquitous and on-going, and the presence of katakana scripts makes it possible for any
foreign word to be absorbed into the Japanese lexicon irrespective of whether it is afrequent word across cultures (e.g., ウォーター for water) or a proper noun (e.g.,	
 サリ
ー for Sally).1
The large number of borrowings in Japanese provides a unique opportunity forReading English with Japanese in mind 4
investigating how the specific characteristics of English and Japanese writing systems
affect bilingual visual word recognition. Resolving this issue is important for thecharacterization of the human language-processing architecture, which at its mostabstract level may arguably be language-independent. We addressed this issue by
investigating to what extent katakana word knowledge is activated when JapaneseEnglish bilinguals perform a visual lexical decision task on English words.

Visual word processing in bilinguals of languages with different scripts
Some initial studies proposed that reading a word in one language might lead to a
restricted activation of words only in that language (the so-called ‘language-selective
access view’, Gerald & Scarborough, 1989; Rodriguez-Fornells, Rotte, Heinze, Nösselt,& Münte, 2002; Scarborough, Gerard, & Cortese, 1984). However, the majority ofexperimental studies indicate that a presented visual word input leads to activation ofword candidates in both languages (the so-called ‘language non-selective access view’,
Dijkstra & van Heuven, 1998, 2002; van Heuven, Dijkstra, & Grainger, 1998).
A direct consequence of such a language non-selective access process is thatcognates, due to representations linked in memory, are processed more quickly. Suchcognate facilitation effects have been reported for a variety of experimental tasks, such as
word association (e.g., van Hell & de Groot, 1998), word naming (de Groot, Borgwaldt,
Bos, & van den Eijnden, 2002), picture naming (Hoshino & Kroll, 2008; Kohnert, 2004),
sentence reading with eye-tracking (Duyck, van Assche, Drieghe, & Hartsuiker, 2007;
van Assche, Duyck, Hartsuiker, & Diependaele, 2009; van Assche, Drieghe, Duyck,
Welvaert, & Hartsuiker, 2011), and vocabulary learning (Otwinowska-Kasztelanic,Reading English with Japanese in mind 52009). Collectively, these studies indicate that the lexical processing architecture ofbilingual readers utilizes lexical distributional properties of two languages in lexical
memory even when processing words in one language.
Perhaps surprisingly, there is a growing amount of data supporting exhaustive
cross-language lexical activation even in bilinguals of languages with different scripts.
Gollan, Forster, and Frost (1997), Nakayama, Hino, Sears, and Lupker (2012), and Kim
and Davis (2003) tested Hebrew-English bilinguals, Japanese-English bilinguals, andKorean-English bilinguals respectively with a masked cross-script priming paradigm andreported that a word in one language still activated its phonologically and semanticallyrelated cognate in another orthographically distinct language.
Note that the observation of language non-selective lexical activation in bilingualsof languages with different scripts does not imply that the underlying lexical processing
architecture is the same as in bilinguals of languages with identical scripts. In fact, given
differences in orthography, there must be some differences in the organization of the firststages of visual word processing for the two languages involved. At more abstract levels,
however, the underlying processing mechanisms are likely to be similar. For instance, therole and interaction of lexical-phonological and semantic information sources within thelexicon might be analogous in Japanese-English and Dutch-English bilinguals. The
present study aims to clarify what mechanisms remain the same and what must be
different to account for lexical processing across languages with different scripts. In thisstudy, we will consider this issue from the theoretical perspective of a localist
connectionist framework.

Reading English with Japanese in mind 6
Extending the BIA+ model to languages with different scripts
The Bilingual Interactive Activation (BIA) model (Dijkstra & van Heuven, 1998,
2002; Dijkstra, van Heuven, & Grainger, 1998) is a localist connectionist model that
extends the monolingual IA model (McClelland & Rumelhart, 1981) and allows us toconceptualize monolingual and bilingual lexical processes within one theoreticalframework. While the original BIA model was limited to orthographic aspects (Dijkstra& van Heuven, 1998; Dijkstra, van Heuven, & Grainger, 1998), it has been extended toaccount for experimental evidence on cross-language phonological and semantic
activation (the BIA+ model, Dijkstra & van Heuven, 2002). In addition, the BIA+ model
attempts to account for cross-task variations by incorporating a task/decision system
explicitly in the model architecture. Currently, this extra-linguistic system is not expected
to immediately affect lexical activation in the word identification system, based on theexperimental evidence that bilinguals automatically activate two languages or word
representations even when this language non-selective activation is not necessary in thetask and, in fact, can even be detrimental (Dijkstra, de Bruijn, Schriefers, & ten Brinke,
2000; Dijkstra & van Hell, 2003; Dijkstra, van Jaarsveld, & ten Brinke, 1998; van Assche
et al., 2011; van Hell & Dijkstra, 2002).
For languages sharing the same script (e.g., Dutch-English and French-English
bilinguals with Latin alphabets), the BIA and BIA+ models predict that orthographic
features of the input word immediately activate orthographic lexical representations in
the two languages simultaneously. In contrast, for languages with different scripts,
identification of script-specific orthographic features is not expected to facilitate
activation of words in both languages. When the BIA+ model is generalized to languages

Reading English with Japanese in mind 7with different scripts, the model may provide different predictions. In the example shownin Figure 1, the input word interview activates the corresponding letter nodes I, N, T, E, R,
V, W, and these letter features then activate the word node interview. For JapaneseEnglish bilinguals, however, it is expected that the feature set coding visuoperceptualfeatures of Latin alphabets does not directly encode Japanese katakana script (the dottedline (a) in Figure 1). Consequently, the orthography-driven non-selective lexical accessacross languages is not expected for bilinguals of languages with different scripts at theearliest processing stages. The section below summarizes our hypotheses for JapaneseEnglish bilinguals with respect to the BIA+ architecture, together with diagnostic
variables used to test the predictions in the following lexical decision with eye-tracking
experiments (Table 1). Appendix A provides detailed descriptions about the lexical
predictors, individual differences, and task effects considered in the present study. The
lexical predictors are classified into those specific to Japanese-English bilinguals andthose of English target words. All predictors were centered for the regression analyses. In
order to study independent contributions of lexical distributional properties, we opted fora residulization procedure to orthogonalize correlated variables, as in Kuperman et al.
(2009) and Miwa et al. (2013). Residualized variables are indicated by the suffix _resid
in Table 1 (see Appendix B for the correlational structure among Japanese and English
frequencies, and Appendix C for the details on the residualization procedure).

< Insert Figure 1 and Table 1 around here >Cross-language phonological similarity. Phonological effects in visual wordReading English with Japanese in mind 8
recognition have been studied predominantly in monolingual word recognition research(Carreiras, Ferrand, Grainger, & Perea, 2005; Ferrand & Grainger, 1992, 1994; Perfetti etal., 1992), but there is growing evidence for cross-language phonological activation inbilingual visual word recognition as well (Brysbaert, 2003; Brysbaert, van Dyck, & van
de Poel, 1999; Dijkstra et al., 1999; Duyck, 2005; Duyck, Diependaele, Drieghe, &Brysbaert, 2004; Schwartz, Kroll, & Diaz, 2007).
For Japanese-English bilinguals, if activation of orthographic lexical
representations of L1 words is mediated only by the conceptual route, then a crosslanguage phonological similarity effect should appear late in time. However, phonologydriven sublexical language non-selective access is theoretically still possible (the route to
the box (d) in Figure 1). In the latter case, an effect of cross-language phonologicalsimilarity may appear early. For Japanese-English bilinguals, it is expected that theactivation of English phonemes leads to activation of the corresponding Japanesephonemes and syllabic and/or moraic phonological nodes (e.g., the activation ofphonemes /i/, /n/, /t/, /a/, /b/, /j/, and /u/ facilitates the activation of syllabic
representations or moraic representations of /i/, /nn/, /ta/, /bju/, and /u/). The phonologicalsimilarity between English and Japanese may lead to a larger global activation in thelexicon, just like a greater degree of orthographic similarity between L1 and L2 words
matters for languages with the same script. As a diagnostic measure of phonologicalsimilarity, we used rated phonological similarity (PhonologicalSimilarityJPN).
Relative word frequencies in two languages. Like the monolingual IA model,
the BIA+ model maintains inhibitory connections among words in the target language
and also assumes inhibitory connections between orthographic lexical representations inReading English with Japanese in mind 9
two languages (Figure 1, line c). One consequence of such inhibitory connections is thatthe magnitude of the expected facilitatory effect of target English word frequency will besmaller when the Japanese translation equivalent has a high frequency of occurrence,
because an English lexical orthographic representation and the Japanese lexicalorthographic representation become co-active at some point in the course of word
identification. It has been reported that word frequencies in two languages interact forinterlingual homographs, words that share orthography across languages but not meaning(Dijkstra et al., 2000; Dijkstra, Moscoso del Prado Martin, Schulpen, Baayen, &Schreuder, 2005; Dijkstra, van Jaarsveld, & ten Brinke, 1998; Kerkhofs, Dijkstra,
Chwilla, & de Bruijn, 2006).
The BIA+ model predicts that, for Japanese-English bilinguals, any orthographic
cross-language inhibition can only occur later in the recognition process, because theJapanese orthographic representation becomes activated only by virtue of cross-language
phonological or conceptual mediation. We used log-transformed English word frequency(FreqHAL, Balota et al., 2007) and log-transformed Japanese word frequency (FreqJPN,
Amano & Kondo, 2003) as diagnostic measures of strength of the activation of lexicalorthographic representations to test this prediction.
Semantic similarity. Translation equivalents in two languages occasionally havedifferent shades of meaning. For example, unlike the English word interview which isused unrestrictedly in various contexts, the use of the Japanese translation equivalent イ
ンタビュー is restricted to ‘mass media interviews’ and not typically used for ‘job
interviews.’ A question relevant to bilingual word processing is whether such crosslanguage semantic similarity contributes to recognition of L2 words. In an unprimed

Reading English with Japanese in mind 10
English progressive demasking task with Dutch-English bilinguals, Dijkstra et al. (2010)
reported a processing advantage for English words with higher semantic similarity. As
shown in Figure 1 box (e), we predicted that semantic similarity contributes relatively
late in the course of word recognition. We used rated semantic similarity as thediagnostic measure to test this prediction (SemanticSimilarity).
Cognate status. While the BIA and BIA+ models account for cognate facilitation
effects by means of cross-language orthographic, phonological, and semantic overlaps
alone, it has been suggested that a shared morpheme representation for cognate mayunderlie the effects (Davis et al., 2010; Lalor & Kirsner, 2000; Sánchez-Casas & GarcíaAlbea, 2005). The present study considers cognate effects as orchestration of gradientlexical effects but also considers a dichotomized factor coding cognate status to test the
special representation view. If cognates have shared morpheme representations, then afactor encoding cognate status should emerge significant on top of relevant numericalpredictors encoding orthography, phonology, and semantics. We straightforwardly tested
this theoretical prediction with a factor Cognate (levels: Cognate and NotCognate) in aregression model.
Extra-linguistic task/decision processes. Although the BIA and BIA+ models
have been frequently discussed together in relation to language non-selective lexicalaccess, the latter is distinguished from the former with respect to its explicit considerationof a non-linguistic system co-determining responses in a given task. The BIA+ model
currently assumes that the non-linguistic system does not modulate lexical processes atthe earliest processing stages. This assumption will be falsified if response-based data
and data sampled from the earliest time frame both reveal the same interactions between

Reading English with Japanese in mind 11
lexical and task-related variables. To the best of our knowledge, however, no study has
identified such interactions. In order to fully test the BIA+ model, we tracked
participants’ global and local response criteria by studying extra-linguistic variables:
Trial, the number of preceding trials, and PreviousResponseCorrect, whether the
responses in the preceding two trials were correct.

Control variables
We considered objective phonological similarity based on Levenshtein distance(PhonologicalDistance, Levenshtein, 1966; Gooskens & Heeringa, 2004; Schepens,
Dijkstra, & Grootjen, 2011; Schepens, Dijkstra, Grootjen, & van Heuven, 2013) to checkthe validity of rated PhonologicalSimilarity. A log-transformed Google document
frequency measure (GoogleFreqJPN) was considered to check the validity of FreqJPN
because the latter newspaper-based measure contains zero frequencies for some words.
As lexical control predictors, word length (Length), orthographic Levenshtein
distance (OLD20, Yarkoni et al., 2008), log-transformed context diversity (SUBTLCD,
Brysbaert & New, 2009a, 2009b), and rated Imageability were considered.
As task-related variables, we considered PreviousRT, inversely transformed RT in
the previous trial, and PreviousFixationDuration for second fixation duration analyses toaccount for potential spillover effects from the previous fixation.
To safe-guard against potential individual differences (Kroll & Stewart, 1994;
Potter, So, von Eckhardt, & Feldman, 1984), we considered log-transformed participants’months of stay away from Japan (LengthOfStayCanada) as a measure of L2 English
proficiency.

Reading English with Japanese in mind 12

Methodological concerns and goals of this study
Previous priming studies for bilinguals with different scripts have provided
evidence supporting automatic language non-selective lexical activation within an
integrated lexicon, as implemented in the BIA model. However, when Japanese-English
readers encounter an English word, Japanese lexical orthographic representation is not
yet activated due to the orthographic dissimilarity (Figure 1 line a). It is important to note
that, in the context of cross-script priming, a lexical orthographic representation of one
language (Figure 1 box b) is artificially pre-activated. This raises a concern as to what
extent language non-selective activation holds in a task without priming (as exceptions,see Thierry & Wu, 2004, 2007 and Wu & Thierry, 2010 for implicit priming to study
bilinguals of languages with different scripts). While masked priming is one of the mostpopular techniques to test subconscious lexical processes, researchers have not reached aconsensus on an interpretation of a masked priming effect (e.g., see Forster, 1998 for alexical pre-activation account, Kinoshita & Norris, 2010 for a non-lexical account, andMarsolek, 2008 for an antipriming account). At the moment, studies without priming forbilinguals with different scripts are scarce, and we investigate this issue using eyetracking.
In the present study, combining English lexical decision tasks with eye-tracking, we
tested the above predictions of the extended BIA+ model with Japanese-English
bilinguals (Experiment 1) and with native monolingual readers (Experiment 2). Lexicaldecision was chosen, as this has been the most widely used experimental task (Libben &Jarema, 2002). Eye-tracking was used because previous studies employing lexical

Reading English with Japanese in mind 13
decision with eye-tracking (Kuperman, Schreuder, Bertram, & Baayen, 2009; Miwa,
Libben, Dijkstra, & Baayen, 2013) reported that early and late lexical processes
systematically co-determine the initial and late eye-fixation measures respectively.
Although the vast majority of psycholinguistic studies on bilingual processing haveanalyzed bilingual-specific effects solely in bilingual readers, we also compared latebilinguals and native monolinguals to (1) make sure that effects of interest are genuine
bilingual effects, rather than artifacts arising from statistical correlation across languages
and general processing mechanisms in reading English words, (2) to confirm that there isa reasonable amount of functional overlap between bilinguals and monolinguals becausepart of the BIA/BIA+ model architecture, in theory, accounts for monolingual readers’lexical processing, and (3) to explore the ‘expert’ ability in reading, as native proficiency
also provides a benchmark of an expert reader, and the acquisition of such ‘expert’ ability
is often viewed as a goal of late bilinguals.
In order to test the above predictions of the BIA+ model, we opted for a mixedeffects analysis (Baayen, Davidson, & Bates, 2008) without dichotomization ofnumerical predictors for more power and precision (Baayen & Milin, 2010; Cohen, 1983;
MacCallum, Zhang, Preacher, & Rucker, 2002). Mixed-effects modeling allows us to test
lexical distributional properties, participants’ characteristics, and task-related variables ina single statistical model.

Experiment 1: English lexical decision with Japanese-English bilingual readersReading English with Japanese in mind 14
MethodParticipants. Nineteen Japanese-English late-bilingual readers (three males, mean
age = 25.1, SD = 5.8) were recruited at the University of Alberta. The participants had
stayed in Canada for 33 months on average (SD = 45.6) and acquired English as theirsecond language.
Materials. We sampled 250 words from English Lexicon Project database (Balota
et al., 2007) based on the following criteria: (1) the word length was between 6 and 9
letters; (2) the word frequency was greater than 2,000 in the FreqHAL frequency
distribution; (3) the morphological status was simplex; (4) the part of speech was noun;and (5) the mean accuracy rates in lexical decision and naming were at least 0.9 (seeAppendix D for the list of words used in the present study). We also sampled 200
nonwords from the ARC nonword database (Rastle, Harrington, & Coltheart, 2002) tomake a total of 450 letter strings. These nonwords were similar to the words: 6 to 9 letterslong, with existing onsets, existing bodies, and legal bigrams.
Apparatus. The experiment was designed and controlled by SR ResearchExperiment Builder software. Words were presented on a 20-inch display. Eyemovements were tracked by an EyeLink II head-mounted eye-tracker (SR Research,
Canada) in the pupil-only mode with a sampling rate of 250 Hz. Calibrations wereconducted with horizontal three points.
Procedure. In this lexical decision with eye-tracking experiment, participants wereasked to decide as quickly and accurately as possible whether the letter strings presented
on the computer display were legitimate existing English words or non-existent words(nonwords) by pressing the right and left buttons of a Microsoft SideWinder game-pad

Reading English with Japanese in mind 15
respectively. The words were presented following a fixation circle, on which participants
were asked to fixate their eyes. This fixation circle served as a drift correct point allowing
the researcher to correct for head drifts between trials.
Given our understanding of the optimal viewing position in sentence reading and inisolated word reading (Brysbaert & Nazir, 2005; Farid & Grainger, 1996; O’Regan &Jacobs, 1992; Vitu, O’Regan, & Mittau, 1990), the location of the fixation mark wasslightly shifted horizontally so that the first fixations were positioned slightly (1.5 letters)
to the left of the word centre. The target words were presented in Courier New 44-point
white font on a black background. At a viewing distance of 70 cm from the screen, thevisual angle was estimated to be 1.1° for each letter (i.e., for six-letter words and nineletter words, such as camera and interview, the visual angles were approximately 6.7º and
10.0º respectively). A testing session contained two breaks, one after every 150 words.
Participants saw a summary of their performance regarding accuracy and response speed
after the practice trials and at each break point. The experiment took roughly 90 to 120
minutes. The right eye was tracked throughout the experiment.

Results
Response latency. R version 2.13.2 (R Development Core Team, 2011) was used
for statistical analyses. We opted for a mixed-effects regression analysis with subjects
and items as crossed random effects (Baayen et al., 2008; Bates, Maechler, & Dai, 2007).
Response accuracy across subjects ranged from 0.86 to 0.98 (M = 0.92, SD = 0.04).
Therefore, no participant was excluded from the analyses. Of all 4,750 trials, those withresponse latency either shorter than 300 ms or longer than 3,000 ms were excluded (14Reading English with Japanese in mind 16
trials). Twenty-one words with more than 30% erroneous responses and one word
(heroin) with a coding error were excluded from the analysis (414 trials, 8.7 % of theremaining data). The following analyses are based on the remaining 228 words,
amounting to 4,099 trials after excluding the 5.1% of trials that contained incorrect
responses.
A reciprocal transformation (-1000/RT) was applied to the RTs to attenuate
skewness in their distribution, based on the appropriate exponent suggested by the BoxCox power transformation technique (Box & Cox, 1964; Venables & Ripley, 2002). The
Box-Cox transformation technique was applied to all dependent measures in the rest of
this paper.
We first fitted a simple main effects model with all predictors and also considered
all pairwise interactions. We then tested interactions between task-related predictors andlexical predictors. Potentially influential outliers were removed with standardized
residuals exceeding 2.5 standard deviation units (1.8% of the data points). Table 2
summarizes the fixed-effects in our final model, and Figure 2 visualizes significant
interactions. The reference level for the factor PreviousResponseCorrect was Correct
throughout this study. The random-effect structure of this model consists of random
intercepts for item (SD = 0.09) and subject (SD = 0.16), by-subject random slopes for
Trial (SD = 0.02), and by-subject random contrasts for PreviousResponseCorrect (SD =0.06). The standard deviation of the residual error was 0.24. Other random slopes for
subjects did not reach significance.

< Insert Table 2 and Figure 2 around here >Reading English with Japanese in mind 17

Interestingly, all bilingual-specific predictors, except the factor Cognate, codetermined the lexical decision response latencies (Figure 2, Panels a, b, and c). Although
the effect of PhonologicalSimilarityJPN reached significance in a simple main-effects
model, a model allowing interactions into the model specification further clarified that
PhonologicalSimilarityJPN facilitated responses later in the experiment (Panel a).
Replacing PhonologicalSimilarityJPN with PhonologicalDistance successfully replicatedthis interaction.
The effect of FreqJPN_resid emerged in an interaction with target word frequency
FreqHAL (Panel b). For words with large FreqJPN_resid (the solid line in Panel b), themagnitude of the FreqHAL effect was attenuated. When FreqJPN_resid was replacedwith GoogleFreqJPN_resid, a virtually identical interaction was obtained.
SemanticSimilarity_resid had a small yet significant facilitatory effect in a simple
main-effects model. Upon close inspection, its interaction with the task-related variable,
PreviousRT (Panel c) indicated that when the response latency in the previous trial was
long (the solid line in Panel c), cross-language SemanticSimilarity_resid facilitated the
response more strongly. This is in line with the finding that the cross-language semantic
similarity effect is more likely to be observed when the task induces longer response
times (Dijkstra et al., 2010, using a progressive demasking word identification task).
Cognate was not significant in this model, indicating that the variance explained by
Cognate was absorbed by the numerical predictors.
Several other lexical distributional properties of the English target words codetermined response latencies. The magnitude of the FreqHAL facilitatory effect was theReading English with Japanese in mind 18
greatest for words with large OLD20_resid, which are words situated in a sparse
orthographic space (the solid line in Figure 2 Panel d). As summarized in Table 2, thecontext diversity of words gauged by SUBTLCD_resid contributed beyond FreqHAL, andLength inhibited responses.
The effect of SemanticSimilarity_resid was not modulated by LengthOfStayCanada,suggesting that, for Japanese-English bilinguals, the recruitment of cross-language
semantic activation did not vary across readers with different L2 proficiency.
It is likely that the above effects reflected in response times also guide eyemovements. We investigated the time-course of the above effects by studying whether
they load onto the early or late fixations, or a combination of both. Japanese-English
bilinguals read words with a single fixation only 1% of the time (36% showed two
fixations, 39% three fixations, and 17% four fixations, with a mode at three fixations). In
order to include as many data points as possible, for all trials with at least two fixations,
we analyzed (1) first fixation durations, (2) first subgazes (in this study, sum of all nonfinal fixations, which were ended by a saccade to a next location) and (3) last fixations asmeasures of very early processing, relatively early processing, and late processing
respectively. The first fixation and subgaze durations were measured from the onset oftarget word presentation, excluding the fixation duration on the fixation point. It is
assumed that the former two measures are less contaminated by conscious lexicaldecision response strategies than the last fixation, which was ended with a button press.	
 First fixation duration. For the analysis of the first fixation durations, data points
with a first fixation shorter than 100 ms and longer than 850 ms, those before a blink, andthose with an incorrect response were excluded from the analysis. A log-transformation

Reading English with Japanese in mind 19
was applied to attenuate skewness in the distribution of the fixation durations (M = 5.7,
SD = 0.3, raw median = 280 ms). As in the response time analysis, we tested simple main
effects and pairwise interactions, as well as interactions between lexical and task-related
predictors. Potentially influential outliers were removed with standardized residuals
exceeding 2.5 standard deviation units (2.0% of the data points). The final model issummarized in Table 3. The random-effect structure of this model was comprised ofrandom intercepts for item (SD = 0.05) and subject (SD = 0.11), and by-subject random
slopes for Trial (SD = 0.01). The standard deviation of the residual error was 0.21.
As expected, the first fixation duration was co-determined by the signature of earlybottom-up orthographic processing. The inhibitory effect of OLD20_resid indicates thatwords in sparse orthographic neighbourhood receive a longer first fixation. A word
frequency effect (FreqHAL) was observed already as well, replicating the early word
frequency effects in previous studies (Kuperman et al., 2009; Miwa et al., 2013).
Interestingly, the bilingual-specific predictor PhonologicalSimilarityJPN alreadycontributed at this earliest time frame, indicating that a sublexical cross-language
phonological decoding route is used (the route to the box d in Figure 1). Unlike its effectin the RT analysis, however, its effect was inhibitory. It should also be noted that theobjective PhonologicalDistance measure, when replaced with
PhonologicalSimilarityJPN, did not reach significance.

< Insert Table 3 around here >	 First subgaze duration. For the analysis of the first subgaze durations, data points

Reading English with Japanese in mind 20
with a first fixation shorter than 100 ms, those before a blink, and those with an incorrect
response were excluded from the analysis. A log-transformation was applied to attenuate
skewness in the distribution of the subgaze durations (M = 6.2, SD = 0.4, raw median =500 ms).
After testing simple main effects, pairwise interactions, and interactions between
lexical and task-related predictors, potentially influential outliers were removed withstandardized residuals exceeding 2.5 standard deviation units (1.2% of the data points).
The final model is summarized in Table 3. The random-effect structure of this model wascomprised of random intercepts for item (SD = 0.08) and subject (SD = 0.16), and bysubject random slopes for Trial (SD = 0.04). The standard deviation of the residual error
was 0.31.
At the first subgaze, some bilingual-specific effects were observed. Whereas at thefirst fixation, PhonologicalSimilarityJPN was inhibitory, at the second fixation,
PhonologicalSimilarityJPN became facilitatory. It should be noted, however, that
PhonologicalDistance did not reach significance when replaced with
PhonologicalSimilarityJPN. Interestingly, the interaction between FreqHAL and
FreqJPN_resid observed in the response time analysis was also observed at the firstsubgaze in a virtually identical form (Figure 3, Panel a). This interaction was successfully
replicated when FreqJPN_resid was replaced with GoogleFreqJPN_resid.
Cognate and SemanticSimilarity_resid were not significant predictors.
LengthOfStayCanada was not a significant predictor and did not modulate any lexical
effects either.

Reading English with Japanese in mind 21< Insert Figure 3 around here >Last fixation duration. For the analysis of the last fixation duration, the words
excluded in the analysis of response latencies were excluded here as well. Trials with last
fixation durations longer than 900 ms, those before or after a blink, and those with anincorrect response were also excluded from the analysis. A square-root transformation
was applied to attenuate the skewness in the distribution of the fixation durations (M =13.5, SD = 4.3, raw median = 188 ms). Unlike the first fixation and subgaze durations,which were terminated by the eye moving to another location in the word, the last
fixation duration was terminated by the readers’ button-press. Consequently, we expectedthat the last fixation durations would reflect, in addition to lexical predictors, variables
associated with response planning and execution.
We tested simple main effects, pairwise interactions, and tested interactions amongtask-related predictors and lexical predictors. As in the response time analysis, with all
relevant numerical predictors considered in a model, Cognate did not contributesignificantly to the model fit. The final model for Japanese-English bilinguals’ last
fixation durations is summarized in Table 3. Figure 4 presents the significant lexicalinteractions in the model. Potentially influential outliers were removed with standardized
residuals exceeding 2.5 standard deviation units (1.6% of the data points). The randomeffect structure of the final model was comprised of random intercepts for item (SD =0.93) and subject (SD = 1.08). The standard deviation of the residual error was 2.97.
Several bilingual-specific effects co-determined the last fixation durations.
FreqJPN_resid interacted with PhonologicalSimilarityJPN. Larger

Reading English with Japanese in mind 22
PhonologialSimilarityJPN shortened the fixation duration for words with highFreqJPN_resid (the solid line in Figure 3, Panel b), suggesting that words with higher
cross-language PhonologicalSimilarityJPN and FreqJPN were perceived as more wordlike. In a model in which PhonologicalDistance was used instead of
PhonologicalSimilarityJPN, a virtually identical interaction was obtained.	
 SemanticSimilarity_resid facilitated processing. The magnitude of facilitation was
greater for words with low cross-language phonological similarity (Panel c). This
interaction may indicate an L1-based response strategy to rely on either phonologicalsimilarity or semantic similarity to make a lexical decision response.
SemanticSimilarity_resid also interacted with PreviousRT_resid (Figure 3, Panel d).
Recall that the facilitatory effect of SemanticSimilarity for words preceded by long
PreviousRT_resid was also observed in the RT analysis. Since SemanticSimilarity_resid
was absent both at the first fixation and subgaze, we can conclude that this semantic
effect emerges late.
The last fixation seems to be qualitatively different from the first fixation andsubgaze, as indicated by the atypical inhibitory effects of FreqHAL (20 ms) and
SUBTLCD_resid (27 ms). The inhibitions from L2 word properties may be due to a
response strategy to rely on L1 word properties. Involvement of a conscious response
strategy is evident from the significant effect of PreviousResponseCorrect. Whenparticipants make an error, they usually become aware of it immediately after a buttonpress and try to be cautious in the following trials. LengthOfStayCanada did not reachsignificance and did not modulate lexical effects either.	
 Reading English with Japanese in mind 23
Discussion
Lexical decision measures emerged, from our analyses, as a composite measureamalgamating processing costs that arise at different stages of information uptake. Firstfixations reflected early bottom-up processing as witnessed by orthographic
neighbourhood density and target word frequency. First subgazes reflected lexical effects
in the word identification system not affected by conscious response strategies, followed
by last fixations, which were more dedicated to response planning and execution in thetask/decision system.
Importantly, using a regression technique, we observed all the expected bilingualspecific effects in the reaction times (i.e., phonological similarity effect, interaction
between L1 and L2 frequencies, and semantic similarity effect) simultaneously in a task
without priming. Interestingly, these effects also co-determined eye-movements but atdifferent points in time. The early contribution of cross-language phonological similarity
indicates that, even for languages with different scripts and in a task without priming,
sublexical phonological decoding immediately takes place. It should be noted, however,that its effect was inhibitory in the earliest time frame and facilitatory in later time frames.
It was also notable that the interaction between L1 and L2 frequencies was foundrelatively early at the first subgaze. Under the assumption that response planning andexecution takes approximately 200 ms (Schmidt, 1982), it is likely that the crosslanguage competition was not due to readers’ conscious response strategy but rather partof central lexical processing mechanism, as assumed in the BIA+ model.
The time-course of lexical activation characterized by the relatively early
contribution of cross-language phonological similarity, followed by the competition

Reading English with Japanese in mind 24
between L1 and L2 words, and then by a cross-language semantic similarity effect is inline with the predictions of the BIA+ model.
In Experiment 2, we tested monolingual readers of English to ensure that thebilingual-specific effects observed in Experiment 1 arose from a bilingual-specificprocessing mechanism, as well as to explore whether the within-language English lexical
distributional properties are utilized similarly by the two groups of readers.	
 	 Experiment 2: English lexical decision with English monolingual readers

Method	 Participants. Nineteen monolingual English readers (7 males, mean age = 21.6, SD= 7.1) were recruited at the University of Alberta. There was no significant difference
between the late bilinguals in Experiment 1 and the monolingual readers with respect toage. Monolingual readers were defined here as native readers of English with more than
80% daily exposure to English relative to the amount of exposure to their second
languages at the time of the experiment, as reported by the participants. None of theparticipants had Japanese as their second or third language.
Materials, Apparatus, and Procedure. The same as in Experiment 1.

Results
Response latency. In the analyses of Experiment 2, we excluded the words which
elicited higher error rates in Experiment 1 to ensure that the comparisons of Experiment 1
and 2 are based on the same set of words. Response accuracy rate ranged from 0.96 toReading English with Japanese in mind 25
1.00 (mean = 0.99, SD = 0.01) for English monolingual readers. Therefore, no subject
was excluded from the analyses. Of all trials (4,750 data points), data points withresponse latency shorter than 300 ms or longer than 2,000 ms were excluded (11 data
points). The following analyses are based on the same 228 words analyzed in Experiment
1 with correct responses (4,275 data points, after excluding 1.1% of trials with incorrect
responses). A reciprocal transformation (-1000/RT) was applied to RTs to attenuate theskew in its distribution.

< Insert Table 4 and Figure 4 around here >We tested simple main effects, all pairwise interactions, and finally interactions
between task effects and lexical effects. Potentially influential outliers were removed
with standardized residuals exceeding 2.5 standard deviation units (1.9% of the data
points). Table 4 summarizes the coefficients of the final model. The random-effectstructure of this model was comprised of random intercepts for item (SD = 0.07) andsubject (SD = 0.14), and by-subject random slopes for Trial (SD = 0.03) andPreviousRT_resid (SD = 0.07). The standard deviation of the residual error was 0.25.
There were qualitative differences between bilinguals and monolinguals. A threeway interaction of FreqHAL by OLD20_resid by Trial was observed. As illustrated inPanels a and b of Figure 4, FreqHAL provided facilitation without interacting with
OLD20_resid early in the experiment (Trial was dichotomized only for the purpose ofvisualization in Figure 4). However, later in the experiment, the interaction between
FreqHAL and OLD20_resid emerged (Panel b), as observed for Japanese-English

Reading English with Japanese in mind 26
bilinguals in Experiment 1. Unlike Experiment 1, Imageability_resid co-determined
response times as well, such that the responses were delayed for words with low
imageability (Balota et al., 2004).
As reported by previous bilingual processing studies (e.g., Duyck et al., 2008;
Gollan et al., 2008), the magnitude of the English word frequency effect was larger forJapanese-English late bilinguals than for English monolingual readers. This larger
frequency effect for non-native readers can be interpreted by the negative decelerating
functional form of the word frequency effect, indicating that each additional log unit of
frequency provides smaller and smaller processing benefits (Baayen, Feldman, &Schreuder, 2006; Baayen & Milin, 2010; Duyck et al., 2008). Overall, monolingualEnglish readers’ response latencies (M = 546 ms, SD = 138) in Experiment 2 were fasterthan those of Japanese-English bilingual readers (M = 733 ms, SD = 248) in Experiment
1 (p < 0.0001, mixed-effects model not shown).
A direct quantitative comparison between monolinguals and bilinguals was also
conducted by including a factor FirstLanguage (levels: Japanese, English) in a regression
model for all data. The results indicate that supposedly bilingual effects (Freq_HAL *FreqJPN_resid, PhonologicalSimilarityJPN, and SemanticSimilarity_resid) did not reachsignificance for the English monolinguals (See Appendix E for the analysis).
First fixation duration. Like bilingual speakers in Experiment 1, native English
speakers read words with multiple fixations most of the time (8% showed a single
fixation, 66% two fixations, 23% three fixations, and 2% four fixations). We therefore
analyzed first fixation durations, first subgaze durations, and last fixation durations, as inExperiment 1.

Reading English with Japanese in mind 27
For the analysis of the first fixation durations, data points with a first fixation
shorter than 100 ms and longer than 850 ms, those before a blink, and those with anincorrect response were excluded from the analysis. A log-transformation was applied toattenuate skewness in the distribution of the fixation durations (M = 5.6, SD = 0.2, raw
median = 264 ms).
The final model is summarized in Table 5. Potentially influential outliers were
removed with as standardized residuals exceeding 2.5 standard deviation units (2.7% of
the data points). The random-effect structure of this model was comprised of random
intercepts for item (SD = 0.03) and subject (SD = 0.11), and by-subject random slopes for
Trial (SD = 0.01) and Length (SD = 0.03). The standard deviation of the residual error
was 0.16. For the expert readers, Length was the only significant lexical predictor. An
item-wise correlation between the first fixation durations of Japanese-English bilingual
readers (Experiment 1) and those of English monolingual readers was significant but
weak (r = 0.19, p < 0.01). The first fixations of the monolinguals were not significantly
faster than those of the bilinguals.

< Insert Table 5 around here >First subgaze duration. Data points before a blink and trials with incorrect
responses were excluded from the analysis. Trials with a first subgaze duration shorter
than 100 ms were excluded, and an inverse transformation was applied to the remaining
fixation durations to attenuate skewness in the distribution (M = -3.2, SD = 0.9, raw
median = 308).

Reading English with Japanese in mind 28
Table 5 summarizes the final model, after testing all main effects and pairwiseinteractions. Potentially influential outliers were removed with standardized residuals
exceeding 2.5 standard deviation units (1.3% of the data points). The random-effectstructure of this model was comprised of random intercepts for item (SD = 0.14) andsubject (SD = 0.4), and by-subject random slopes for Trial (SD = 0.07). The standard
deviation of the residual error was 0.73. The fixed-effect structure of this model wascomprised of Length, FreqHAL, and SUBTLCD_reisd, all of which co-determinedJapanese-English bilinguals’ first subgaze in Experiment 1. A significant item-wisecorrelation between the first subgaze durations of Japanese-English bilingual readers(Experiment 1) and those of English monolingual readers (r = 0.46, p < 0.01) also
indicates strong commonality in information uptake during the relatively early stage ofword recognition. The first subgaze durations of the monolingual readers weresignificantly faster than those of the bilingual readers (p < 0.0001, effect size = 163 ms,
mixed effects model not shown).
Last fixation duration. In the same subset of words analyzed above, we excluded
last fixation durations longer than 900 ms and applied a square root transformation toattenuate a skew in the distribution (M = 14.2, SD = 3.7, raw median = 216). Fixations
before or after a blink and trials with incorrect responses were excluded from the analysis.
The final model is summarized in Table 5. Potentially influential outliers were
removed with standardized residuals exceeding 2.5 standard deviation units (1.9% of thedata points). The random-effect structure of this model was comprised of random
intercepts for item (SD = 0.81) and subject (SD = 0.75), and by-subject random slopes forFirstSubgazeDuration_resid (SD = 0.61). The standard deviation of the residual error

Reading English with Japanese in mind 29
was 2.35.
The Imageability_resid was the only lexical predictor co-determining monolingualEnglish readers’ last fixation durations. Recall that Imageability_resid also co-determined
the same readers’ response times to a comparable magnitude. Interestingly, as inExperiment 1, PreviousResponseCorrect was significant here as well. When participants
make an error response (which they are usually aware of), their immediately following
responses become slower to be more cautious. Such strategic effects do not seem to codetermine early measures and only inflated last fixation durations.
An item-wise correlation between the last fixation durations of Japanese-English
bilingual readers (Experiment 1) and those of English monolingual readers was
significant but weak (r = 0.29, p < 0.01). Furthermore, the last fixations of themonolingual English readers were not significantly faster than those of the late bilinguals.
	 Discussion	 Experiment 2 confirmed that there was commonality in processing between
monolingual readers and bilingual readers: The lexical processes proceed from visuoperceptual and sublexical orthographic effects (Length and OLD20_resid) to orthographic
lexical effects (FreqHAL and SUBTLCD_resid) and then to semantic processes(Imageability_resid and SemanticSimilarity_resid). Importantly, however, the bilingualspecific effects obtained in Experiment 1 did not reach significance in Experiment 2. This
indicates that the bilingual-specific effects of our interest were genuine bilingual effects
arising from the theoretical bilingual-specific architecture rather than artifacts arisingfrom processes of target words per se. The functional overlap across different groups ofReading English with Japanese in mind 30
readers and the significance of bilingual-specific effects only for bilingual readers are inline with the BIA and BIA+ models.
However, it should be noted that Experiment 2 also identified differences between
monolingual readers and late bilinguals. Length was a significant predictor at the first
fixation only for the monolinguals. Imageability_resid was similarly a significant
predictor at the last fixation durations only for the monolinguals.
Finally, the significant effects of PreviousResponseCorrect on last fixation
durations and response times, but not first fixations and subgaze durations, of bothmonolingual readers and bilingual readers suggest that this is a language-general
phenomenon and that readers’ conscious strategy to respond more cautiously affects only
the late processes. This, in turn, indicates that the first fixations and subgazes are
relatively strategy-free measures for automatic lexical processing. The late involvementof a response strategy and the word identification system’s insensitivity to a strategic
factor is in line with the prediction of the BIA+ model. 	
 General discussion

This study addressed the question of the time-course of lexical activation in
Japanese-English bilinguals. To this end, we combined lexical decision with eye-tracking
because eye-tracking, unlike button press responses, affords insight into the time-courseof lexical activation. Importantly, because the task did not involve cross-script priming,the lexical orthographic representation (Figure 1, box b) was not artificially activatedwhen the L2 target word was presented, allowing a more natural interpretation of theReading English with Japanese in mind 31
results. As predicted by the BIA+ model, we observed clear effects of all the bilingualspecific lexical predictors (cross-language phonological similarity, L1 word frequency
interacting with L2 word frequency, and cross-language semantic similarity), but not thefactor coding cognate status.
First, cross-language phonological similarity facilitated the lexical decision
responses of Japanese-English bilinguals. The eye-tracking record clarified that thephonological similarity effect emerged already at the first fixation. Within the frameworkof BIA+ model, we interpret this effect as sketched in Figure 1 box (d). Once thealphabetic letter representations I, N, T, E, R, V, and W are activated, based on thewritten input interview, the corresponding phonemes (e.g, /ɪ/, /n/, /t/, /ɚ/, /v/, /j/, and /u/)are activated to derive the appropriate lexical phonological representation /ɪntɚvju:/. It isconceivable that, at the same time, the activation of English phonemes can lead to (atleast partial) activation of the corresponding Japanese phonemes (e.g., /i/, /n/, /t/, /a/, /b/,/j/, and /u/), eventually leading to the activation of /inntabjuu/.
In this lexical decision study without priming, however, its effect was inhibitory at
the first fixation, facilitatory at the first subgaze, and facilitatory also at the moment of
the response. Previous studies reported mixed results: Dijkstra et al. (1999) reported an
inhibitory effect of phonological similarity, and Lemhöfer and Dijkstra (2004) and Haighand Jared (2007) reported facilitatory effects. Our results indicate that this is not aneither-or problem but that inhibition and facilitation manifest themselves at different
points in time. Given that this inhibitory effect was not replicated with the objective
PhonologicalDistance measure, this may be due to early noise induced by co-activationof English and Japanese sublexical phonology (e.g., vowel-consonant distinction andReading English with Japanese in mind 32
stress pattern), which are not accounted for by the edit distance coding.
Second, L1 Japanese word frequency co-determined lexical decision responses in
an interaction with L2 English word frequency. The English word frequency effect was
progressively attenuated as Japanese frequency increased. Importantly, this interaction
was also observed at the first subgaze, but not at the first and last fixations. This finding
can be understood within the BIA+ model as follows. The BIA+ model posits, as does
any interactive activation model, inhibitory links between non-identicalorthographic/phonological lexical representations (see Figure 1, line c, and previous
studies by Dijkstra et al., 1998; Kerkhofs et al., 2006). Because there are no links
projecting from the English letter units to the Japanese katakana word representation (i.e.,the dashed line a in Figure 1 is not active), the input word interview cannot activate theJapanese lexical katakana representation インタビュー at an early stage. However, theEnglish input word interview activates the alphabetic letter representations I, N, T, E, R,
V, and W, and consequently the English orthographic lexical representation
INTERVIEW relatively early. The activation of the L1 Japanese lexical orthographic
representation of a cognate can and does occur but only via indirect activation mediatedby sublexical and lexical phonological representations or via top-down activation fromthe conceptual representation (see, for instance, the significant cross-language masked
priming effect for non-cognate translation equivalents reported by Grainger & FrenckMestre, 1998; see also Duñabeitia, Perea, & Carreiras, 2010; Pecher, 2001; Perea,
Duñabeitia, & Carreiras, 2008).
Third, a small significant facilitatory contribution of semantic similarity wasobserved. Its effect arose late, at the last fixation and in the lexical decision responses. In

Reading English with Japanese in mind 33
the present study, the semantic similarity effect did not depend on readers’ L2 proficiency.
This finding is consistent with models assuming a strong form-to-meaning mapping forboth L1 and L2, allowing rapid semantic activation in L2 word processing (Duyck &Brysbaert, 2004).
Fourth, regression modeling allowed us to straightforwardly test the view thatcognate facilitation arises from special morphological representations for cognates (Daviset al., 2010; Lalor & Kirsner, 2000; Sánchez-Casas & García-Albea, 2005). Althoughmodels with the factor Cognate as a sole lexical predictor replicated standard cognate
facilitation effects (mixed-effect models not shown), it was no longer a significant
predictor once the relevant numerical predictors were included in the regression
equations. This is in harmony with Voga and Grainger’s (2007) conclusion; a cognate
facilitation effect is not due to special morphological representations but due to sharedform overlaps.
Finally, we observed similarities and differences in how monolingual readers andbilingual readers make lexicality judgments. On one hand, the lexical decision processesof the two groups were comparable, as indicated by the lexical distributional properties
that similarly co-determined the lexical decision responses of the two groups of readers(e.g., the interaction between FreqHAL and OLD20_resid). Such a functional overlap
between groups is consistent with the general architecture of the BIA+ model, which is a
generalization of the monolingual interactive activation model.
On the other hand, the lexical decision process of the bilingual readers diverged
from that of the monolingual readers, beyond the bilingual-specific lexical effects
mentioned above. At the first fixation, Length was significant only for monolinguals, andReading English with Japanese in mind 34
OLD_resid was significant only for bilinguals. This may reflect the fact that theperceptual span of proficient readers is wider than that of less proficient readers (Rayner,
Slattery & Belanger, 2010). At the last fixation, Imageability_resid was significant only
for monolinguals.
Japanese-English bilinguals and English monolinguals also fine-tuned response
criteria differently throughout the experiment. The former group apparently adjusted the
response threshold for lexical decision with respect to a Japanese word property (i.e.,cross-language phonological similarity). Because the co-activation of an L1 Japaneseword via its phonological overlap with the L2 English word is a reasonable criterion for a‘yes’ response in lexical decision, participants may have fine-tuned their response criteria
so that phonology received progressively more weight to optimize responses.
Monolinguals, on the other hand, fine-tuned response criteria with respect to word
frequency and orthographic density (FreqHAL * OLD20_resid).
The above qualitative differences between bilinguals and monolinguals reconfirm
that bilinguals are not two monolinguals in one mind (Grosjean, 1989). Although the
BIA+ is, in terms of processing architecture, an extension of the monolingual IA model,such an architectural extension leads to qualitative and quantitative differences inprocessing (see Appendix E for a direct quantitative comparison between the two groups
with the factor FirstLanguage in a mixed-effects analysis). The processing differences
observed in this study do not provide evidence against the BIA+ model but point out
specific areas that are yet to be clarified, namely the visuo-perceptual level and thedecision/response level. The observed processing differences between bilinguals andmonolinguals also imply that it requires caution to use monolinguals as experimental

Reading English with Japanese in mind 35
controls when studying bilingual processing.
Future research should further investigate potential consequences of quality oflexical predictors, experimental manipulations, and individual differences. As for qualityof lexical predictors, human-rated phonological and semantic similarity measures wereused in this study, as in many previous studies. The objective edit distance measure
replicated the late effect, but not early effects, of the rated phonological similarity
successfully. It is important to clarify what constitute rated measures and how to codethem objectively (see Appendix F for a comparison of various rated measures). As forexperimental manipulations, the font size chosen in this study was relatively larger thanthat in normal reading. While readers made multiple fixations most of the time in thisstudy, an analysis of eight native English speakers’ reading with smaller font (visual
angle per letter = 0.4˚) revealed that multiple eye-movements were still used 73% of thetime (median = 2, range = 1:5), indicating that isolated word reading itself triggers a taskspecific pattern of eye-movements. As for individual differences, we only considered apotential effect of readers’ length of stay in Canada, and we leave it to future research todisentangle contributions of various related measures. In addition, although monolinguals
and bilinguals tested in this study were comparable in terms of age, more rigorous
assessment should be conducted in the future (e.g., matched intelligence and reading
speed). It is likely that the by-subject random intercepts, which capture between subject
variability that cannot be traced back to the predictors included in the present study,
comprise variation that can be explained by more refined measures characterizing theindividual subjects, including measures for their reading skills.
In conclusion, without using a priming technique, the present study tapped into theReading English with Japanese in mind 36
time-course of lexical activation by observing eye movements to test various predictionsof the BIA+ model. The bilingual-specific lexical processes that are characterized byearly cross-language phonological similarity to an interaction between L1 and L2
frequency, and then to late cross-language semantic similarity is in line with the BIA+
model. The absence of a significant contribution of a factor coding cognate statusindicates that a cognate facilitation effect can be sufficiently captured by numericalpredictors coding form and meaning in two languages. The localist connectionist
framework of the BIA+ model, which thus far has been guided by research on bilingualswith the same script, can be modified to account for lexical processes of JapaneseEnglish bilinguals, under the straightforward assumption that English letter units do not
project onto Japanese word units.

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Reading English with Japanese in mind 49

Footnotes

1

Nakayama, Kiryu, and Yamaguchi (2007) studied a corpus covering 10 years of anation-wide newspaper from 1994 to 2003 and counted 8,226 katakana words thatappeared at least 100 times. Typical Japanese dictionaries of katakana words list many
more katakana words (e.g., the Concise Dictionary of Katakana Words lists as many as
56,300 words, Sanseido Henshujo, 1994).

Reading English with Japanese in mind 50
Table 1
Lexical predictors, individual differences, and task effects considered in this study. The
range and mean are presented for their original values before residualization andcentralization procedures. The superscripts represent a transformation method used forthe given predictor. The values for Individual and Task variables are those in Experiment
1 (Japanese-English bilinguals).

Type

Predictor

Range (Mean, SD) / Levels

Japanese-English

PhonologicalSimilarityJPN

2.3: 6.0 (M = 4.5, SD = 0.7)

PhonologicalDistance

0.2 : 1.2 (M = 0.6, SD = 0.2)

FreqJPN_resid (log)

0 : 10.4 (M = 4.2, SD = 2.8)

GoogleFreqJPN_resid (log)

5.7 : 19.2 (M = 14.8, SD = 2.4)

SemanticSimilarity_resid

3.0 : 7.0 (M= 5.9, SD = 0.7)

CognatesLevels: Cognate, NotCognate

Length6 : 9 (M = 6.8, SD = 0.9)

OLD20_resid

1.3 : 4.5 (M = 2.4, SD = 0.5)

FreqHAL (log)

7.8 : 13.0 (M = 9.6, SD = 1.1)

SUBTLCD_resid (log)

0 : 4.4 (M = 1.9, SD = 0.9)

Imageability_resid

1.6 : 7.0 (M = 4.7 , SD = 1.7)

Individual

LengthOfStayCanada (log)

1.1 : 5.3 (M = 2.7, SD = 1.3)

Task

PreviousResponseCorrect

Levels: Correct, Error

Trial (/100)

0.11 : 4.60 (M = 2.4, SD = 1.3)

EnglishPreviousRT_resid(-1000/)-1.1 : 1.1 (M = 0, SD = 0.4)

	 FirstSubgazeDuration_resid (log)	 4.9 : 7.8 (M = 6.2, SD = 0.4)

Reading English with Japanese in mind 51
Table 2Estimate, standard error, t-value, p-value, and effect size of the fixed effects in the modelof Japanese-English bilingual readers’ lexical decision response times. Task = taskrelated predictors, Engl = English target word properties, Jpn-Engl = Japanese-Englishbilingual-specific predictors. The effect sizes refer to the magnitude of an effectcalculated as the difference between the model’s prediction for the minimum and the
maximum back-transformed values of a given predictor

Response timeType

(Intercept)
PreviousResponseCorrect (Error)
Trial
PreviousRT_resid
Length
OLD20_resid
FreqHALSUBTLCD_resid
FreqJPN_resid
PhonologicalSimilarityJPN
SemanticSimilarity_resid
PreviousRT_resid* TrialPhonologicalSimilarityJPN* Trial
FreqHAL* FreqJPN_resid
SemanticSimilarity_resid* PreviousRT_resid
FreqHAL* OLD20_resid

Task
Task
Task
Engl
Engl
Engl
Engl
Jpn-Engl
Jpn-Engl
Jpn-Engl
Jpn-Engl* Task
Jpn-Engl* TaskEngl* Jpn-Engl
Jpn-Engl* TaskEngl* EnglEstimate

Std.Error

t-valuep-value

Effect size

-1.4770.064-0.085
0.135
0.046
0.033
-0.064-0.061-0.003-0.027-0.037

0.037
0.017
0.006
0.012
0.008
0.021
0.007
0.010
0.003
0.011
0.011

-40.43
3.69-13.52
10.81
5.79
1.55
-9.61
-5.89
-1.04
-2.38
-3.27

< 0.0001
0.0002< 0.0001< 0.0001< 0.0001
0.1209< 0.0001< 0.0001
0.29660.01720.0011

28
-168
124
65
26
-129
-111
-14
-44
-70

-0.028

0.009

-2.98

0.0029No plotted-0.011

0.005

-2.28

0.0227

Figure 2 (a)

0.006

0.003

2.53

0.0116

Figure 2 (b)-0.046

0.016

-2.92

0.0035Figure 2 (c)-0.050

0.018

-2.74

0.0061Figure 2 (d)Reading English with Japanese in mind 52
Table 3
Estimate, standard error, t-value, p-value, and effect size (ms) of the fixed effects in themodel of Japanese-English bilingual readers’ first fixation durations, first subgaze
durations, and last fixation durations.

First fixation duration(Intercept)
Trial
PreviousRT_resid
OLD20_resid
FreqHAL
PhonologicalSimilarityJPN

TypeFirst subgaze duration(Intercept)
Trial
PreviousRT_resid
Length
OLD_resid
FreqHALSUBTLCD_resid
PhonologicalSimilarityJPN
FreqJPN_resid
FreqHAL* FreqJPN_resid

Type

Last fixation duration(Intercept)
FirstSubgazeDuration_resid
PreviousResponseCorrect (Error)
Trial
PreviousRT_resid
FreqHALSUBTLCD_resid
FreqJPN_resid
PhonologicalSimilarityJPN
SemanticSimilarity_resid
PhonologicalSimilarityJPN* FreqJPN_resid

Type

Task
Task
Engl
Engl
Jpn-Engl

Task
Task
Engl
Engl
Engl
Engl
Jpn-Engl
Jpn-Engl
Engl* Jpn-Engl

Task
Task
Task
Task
Engl
Engl
Jpn-Engl
Jpn-Engl
Jpn-Engl
Jpn-Engl* Jpn-Engl

Estimate Std.Error t-value p-value
5.673
0.025 228.98 < 0.0001
0.011
0.004
2.93
0.0034-0.0250.011-2.23
0.0256
0.040
0.013
2.99
0.0028-0.0090.004-1.99
0.0467
0.017
0.007
2.29
0.0219Effect size

Estimate Std.Error t-value p-value
6.207
0.037 168.46 < 0.0001-0.0870.009-9.41 < 0.0001
0.129
0.017
7.78 < 0.0001
0.084
0.008
10.42 < 0.0001
0.039
0.021
1.83
0.0680-0.0640.007-9.54 < 0.0001-0.0680.010-6.61 < 0.0001-0.0290.012-2.45
0.0142-0.0040.003-1.22
0.2215

Effect size

14
-16
23
-13
18

-181
134
130
34
-143
-132
-49
-18

0.005

0.003

2.04

0.0412 Figure 3 (a)Estimate
13.282-8.0060.667
0.257-1.643
0.142
0.240
0.016
0.006
-0.497

Std.Error
0.261
0.145
0.130
0.038
0.158
0.071
0.112
0.031
0.124
0.121

t-value
50.89-55.23
5.14
6.83-10.41
1.98
2.14
0.52
0.05
-4.10

p-value< 0.0001< 0.0001< 0.0001< 0.0001< 0.0001
0.04740.03230.60420.9609< 0.0001

Effect size

-0.104

0.044

-2.38

0.0175Figure 3 (b)

-507
18
31
-104
20
27
0
5
-49

Reading English with Japanese in mind 53
SemanticSimilarity_resid* PhonologicalSimilarityJPN
SemanticSimilarity_resid* PreviousRT_resid	 Jpn-Engl* Jpn-Engl
Jpn-Engl* Task

0.444

0.199

2.23

0.0259Figure 3 (c)-0.616

0.199

-3.09

0.0020Figure 3 (d)Reading English with Japanese in mind 54
Table 4
Estimate, standard error, t-value, p-value, and effect size (ms) of the fixed effects in themodel of English monolingual readers’ lexical decision response times.

Response timeType

(Intercept)
PreviousResponseCorrect (Error)
Trial
PreviousRT_resid
Length
OLD20_resid
FreqHALSUBTLCD_resid
Imageability_resid
OLD20_resid* Trial
FreqHAL* Trial
FreqHAL* OLD20_resid
FreqHAL* OLD20_resid* Trial

Task
Task
Task
Engl
Engl
Engl
Engl
Engl
Engl
* TaskEngl* TaskEngl* EnglEngl* Engl* TaskEstimate

Std.Error

t-valuep-value

Effect size

-1.9460.085-0.040
0.181
0.029
0.005
-0.030-0.058-0.011

0.034
0.018
0.008
0.020
0.007
0.018
0.006
0.009
0.004

-57.59
4.82
-5.25
9.27
4.33
0.30
-5.22
-6.64
-2.81

< 0.0001< 0.0001< 0.0001< 0.0001< 0.0001
0.7668< 0.0001< 0.0001
0.0049

23
-46
124
23
3
-37
-63
-19

0.019

0.009

2.07

0.0381Figure 4 (a, b)-0.002

0.003

-0.71

0.4749Figure 4 (a, b)-0.027

0.016

-1.70

0.0886

Figure 4 (a, b)-0.016

0.008

-2.08

0.0380Figure 4 (a, b)

Reading English with Japanese in mind 55
Table 5
Estimate, standard error, t-value, p-value, and effect size (ms) of the fixed effects in themodel of English monolingual readers’ first fixation durations, first subgaze durations,and last fixation durations.

First fixation duration(Intercept)
Trial
PreviousRT_resid
LengthTypeFirst subgaze duration(Intercept)
Trial
PreviousRT_resid
LengthFreqHALSUBTLCD_resid
Length* TrialType

Last fixation duration(Intercept)
FirstSubgazeDuration_resid
PreviousResponseCorrect (Error)
Imageability_resid

Type

Task
Task
Engl

Task
Task
Engl
Engl
Engl
Engl
*Task

Task
Task
Engl

Estimate Std.Error
t-values
p-value
5.620
0.024
230.71< 0.0001
0.007
0.004
1.69
0.0911-0.0230.008-2.98
0.0029
0.031
0.007
4.51
< 0.0001

Effect size

Estimate Std.Error
t-valuep-value-3.1570.094-33.5< 0.0001-0.0470.018-2.61
0.0092
0.136
0.036
3.82
0.0001
0.223
0.017
13.48
< 0.0001-0.0430.014-3.12
0.0018-0.0890.021-4.18< 0.0001

Effect size

8
-17
26

-11
32
76
-20
-34

-0.024

0.010

-2.44

0.0147

Not plotted

Estimate
14.164-3.0500.530-0.100

Std.Error
0.184
0.063
0.167
0.042

t-value
76.91-48.113.17-2.36

p-value
0.00000.00000.00150.0181

Effect size
-1080
15
-19

Reading English with Japanese in mind 56

Figure Captions

Figure 1. A bilingual interactive activation (BIA+) architecture applied for JapaneseEnglish bilinguals’ processing of an L2 English word. Arrows represent
facilitatory links and circular connectors represent inhibitory links.
Figure 2. Lexical interactions in the mixed-effects model for Japanese-English
bilinguals’ lexical decision response times. Different lines represent quantiles,
and the rug in the x-axes represents the pattern of distribution.
Figure 3. Lexical interactions in the mixed-effects model for Japanese-English
bilinguals’ first subgaze and last fixation durations in trials with two fixations.
Different lines represent quantiles, and the rug in the x-axes represents thepattern of distribution.
Figure 4. Lexical interactions in the mixed-effects model for monolingual English
readers’ response latencies. Different lines represent quantiles, and the rug inthe x-axes represents the pattern of distribution.

Reading English with Japanese in mind 57

Figure 1	 Reading English with Japanese in mind 58

Figure 2

Reading English with Japanese in mind 59

Figure 3	 Reading English with Japanese in mind 60
Figure 4