Script differences and masked translation priming ...

1

Running Head: Script differences and masked translation priming

Script differences and masked translation priming: Evidence from Hindi-English bilinguals Namrata Dubey, Naoko Witzel, & Jeffrey Witzel Department of Linguistics & TESOL, University of Texas at Arlington

Mailing address: Department of Linguistics & TESOL University of Texas at Arlington Arlington, TX 76019 USA

Email addresses: Namrata Dubey: [email protected] Naoko Witzel: [email protected] Jeffrey Witzel: [email protected] (corresponding author)

[to appear in the ​Quarterly Journal of Experimental Psychology​]

2

ABSTRACT This study reports on two experiments investigating the effects of script differences on masked translation priming in highly-proficient early Hindi-English bilinguals. In Experiment 1 (the cross-script experiment), L1 Hindi was presented in the standard Devanagari script, while L2 English was presented in the Roman alphabet. In Experiment 2 (the same-script experiment), both L1 Hindi and L2 English were presented in the Roman alphabet. Both experiments revealed translation priming in the L1-L2 direction. However, L2-L1 priming was obtained in the same-script experiment, but not in the cross-script experiment. These findings are discussed in relation to the orthographic cue hypothesis as well as hypotheses that hold that script differences influence the distance between the L1 and L2 in lexical space and/or cross-language lateral inhibition. We also provide alternative accounts for these results in terms of how orthographic cues provided by L1 targets might lead to the discontinuation or disruption of processing for L2 primes.

3

Script differences and masked translation priming: Evidence from Hindi-English bilinguals A major question in bilingual language processing is how first- and second-language (L1 and L2, respectively) words are stored in and accessed from memory. One method that has been used to investigate models of the bilingual lexico-semantic system is masked translation priming. A number of studies using this method in lexical decision have shown a translation priming asymmetry, with strong priming for noncognate translation equivalents in the L1-L2 direction, but weak and inconsistent priming in the L2-L1 direction (Chen, Liang, Cui, & Dunlap, 2014; Dimitropoulou, Duñabeitia, & Carreiras, 2011a; Ferré, Sánchez-Casas, Comesaña, & Demestre, in press; Finkbeiner, Forster, Nicol, & Nakayama, 2004; Gollan, Forster, & Frost, 1997; Grainger & Frenck-Mestre, 1998; Jiang, 1999; Nakayama, Sears, Hino, & Lupker, 2013; Wang, 2013; Wang & Forster, 2015; Wen & van Heuven, 2017; Witzel & Forster, 2012). However, more recent studies have found that it is also possible for L2 words to prime their L1 translations in this task (see e.g., Dimitropoulou, Duñabeitia, & Carreiras, 2011b; Duyck & Warlop, 2009; Schoonbaert, Duyck, Brysbaert, & Hartsuiker, 2009). This is particularly the case for bilinguals who have developed sufficient proficiency in the L2 such that it behaves more or less like an L1 (Basnight-Brown & Altarriba, 2007; Duñabeitia, Perea, & Carreiras, 2010; Nakayama, Ida, & Lupker, 2016; Nakayama, Lupker, & Itaguchi, in press; Sabourin, Brien, Burkholder, 2014; Wang, 2013). The present study explores another factor that might modulate masked translation priming -- namely, script properties of the L1 and L2. Indeed, despite findings from a recent meta-analysis indicating no clear moderating role for script differences in either L1-L2 or L2-L1 translation priming (Wen & van Heuven, 2017), as detailed below, these differences are central

4

to a number of models of bilingual access. This study therefore examines this issue further by investigating whether activation between L1 and L2 words is influenced by the script in which these words are presented. This was done by comparing translation priming patterns in bilinguals from the same population -- specifically, early Hindi-English bilinguals -- when the script properties of their L1 either matched or mismatched with that of the L2. As mentioned above, the findings from masked translation priming studies have been taken to indicate important aspects of the bilingual lexico-semantic system. For instance, a number of models, and perhaps most notably the various Bilingual Interactive Activation models (henceforth BIA and BIA+, see e.g., Dijkstra & van Heuven, 2002; Grainger & Dijkstra, 1992; van Heuven, Dijkstra, & Grainger, 1998), hold that translation priming in either direction demonstrates that bilinguals activate both languages in parallel. Under these models, the priming asymmetry noted above is attributed to differences in activation strength for L1 and L2 words (see e.g., Dimitropoulou et al., 2011a; Voga & Grainger, 2007). This is based on the assumption that L1 words have generally higher levels of resting activation. Thus, even when these words are presented as masked primes, they can be activated strongly enough to subsequently activate semantically-related words, including their L2 translations. The lower levels of resting activation for L2 words, on the other hand, mean that when these words are presented as masked primes, they cannot be activated strongly or quickly enough to also yield activation of semantically-similar words. Dijkstra and van Heuven (2002) have further suggested that under these models, script properties of the L1 and L2 might also influence cross-language lexical activation. In particular, they suggest that different-script bilinguals might have more language-specific activation patterns. For example, when Chinese-English bilinguals are

5

presented with Chinese logographs, it is unlikely that English word representations in the Roman alphabet will also be activated (and vice versa). This is because words in these two languages do not share orthographic neighbors (Coltheart, Davelaar, Jonasson, & Besner, 1977), or words that are orthographically similar to one another (e.g., ​gato ​in Spanish and ​gate ​in English, which differ by a single letter). Consistent with this idea, several studies have shown that bilinguals selectively activate their languages when they are presented with words/nonwords that have orthographically-marked letters (van Kesteren, Dijkstra, & de Smedt, 2012), orthotactically-marked letter combinations (Casaponsa, Carreiras, & Duñabeitia, 2014; Casaponsa, Carreiras, & Duñabeitia, 2015; Grainger & Beauvillain, 1987; Vaid & Frenck-Mestre, 2002), or different bigram frequencies and orthographic neighborhood sizes in each language (Oganian, Conrad, Aryani, Heekeren, & Spalek, 2016). (For a more recent view that different-script bilinguals activate both languages together, see van Heuven & Coderre, 2015.) Other models of the bilingual lexicon maintain that L1 and L2 words are stored and accessed separately as a matter of course (see e.g., Finkbeiner et al., 2004; Wang & Forster, 2010, for the Sense Model; Jiang & Forster, 2001; Witzel & Forster, 2012, for the episodic L2 hypothesis). Interestingly, some of the original evidence in support of such models came from findings indicating that script properties might influence masked translation priming. Specifically, a number of early studies found that L1-L2 priming with noncognates could be obtained in different-script bilinguals (see e.g., Gollan et al., 1997, for Hebrew-English bilinguals; Jiang, 1999, for Chinese-English bilinguals), but not in same-script bilinguals (see e.g., Sánchez-Casas, Davis, & García-Albea, 1992, for Spanish-English bilinguals; see also

6

Davis, Sánchez-Casas, García-Albea, Guasch, Molero, & Ferré, 2009). Gollan et al. (1997) accounted for this pattern of results with the ​orthographic cue hypothesis​. Under this hypothesis, the L1 and L2 are stored separately, and orthographic cues are used to indicate which language to search in for visually-presented words, and for masked primes in particular (for further discussion of this hypothesis, see Kim & Davis, 2003; Voga & Grainger, 2007; for a similar account based on ERP evidence, see Hoshino, Midgley, Holcomb, & Grainger, 2010). If the prime and target have the same script, the orthography of the prime does not provide a clear indication of which language to search, resulting either in failure to access the prime or in delayed access such that the prime cannot influence the processing of the target. However, if the prime and the target are presented in different scripts, the orthographic information provided by the prime is able to cue a search in the appropriate lexicon, thus facilitating access to the prime and allowing for translation priming. The lack of L2-L1 priming was explained under this hypothesis in terms of differences in processing speed for L1 and L2 words, with the idea that L2 primes are not processed quickly enough to affect the recognition times of more rapidly accessed L1 targets. In line with this idea, it has since been shown that it is possible to obtain L2-L1 priming in different-script bilinguals, but only when they have very high levels of L2 proficiency and thus comparable processing speed in the L1 and L2 (see e.g., Nakayama et al., 2016; Nakayama et al., in press, for highly-proficient Japanese-English bilinguals; Wang, 2013, for balanced Chinese-English bilinguals; but see Dimitropoulou et al., 2011b, for indications of L2-L1 priming in Greek-English bilinguals at lower levels of proficiency). This orthographic cue hypothesis, however, has been undercut by more recent studies indicating translation priming in same-script bilinguals as well. What is more, it seems that

7

translation priming is likely to obtain in both the L1-L2 and L2-L1 directions in these bilinguals. Specifically, for same-script bilinguals, bidirectional translation priming has been found not only in simultaneous bilinguals with relatively balanced L1 and L2 proficiency (Duñabeitia et al., 2010, for Basque-Spanish bilinguals; Sabourin et al., 2014, for English-French bilinguals), but also in unbalanced bilinguals who are less proficient in their L2 (Duyck & Warlop, 2009, for Dutch-French bilinguals; Schoonbaert et al., 2009, for Dutch-English bilinguals). One possible explanation for these findings is that L1 and L2 words might be more integrated in the bilingual lexicon when the languages share the same script, as is the case with Dutch/French and Dutch/English (again, see Dijkstra & van Heuven, 2002; as well as Dimitropoulou et al., 2011a). If the L1 and L2 have different scripts, on the other hand, as in Hebrew/English (Gollan et al., 1997), Chinese/English (Jiang, 1999), Japanese/English (Finkbeiner et al., 2004), and Greek/Spanish (Dimitropoulou et al., 2011a), then the words in these languages might be stored or accessed separately. A recent study by Casaponsa and Duñabeitia (2016) provides intriguing support for the idea that script properties of the L1 and L2 influence the organization of the bilingual lexicon. In this study, simultaneous Basque-Spanish bilinguals, whose languages share a script, were presented with Basque prime-Spanish target pairs in a masked translation priming experiment. The Basque primes were either orthotactically unmarked, in that they comprised possible letter sequences in both Basque and Spanish (e.g., ​ilargi, ‘​moon’), or orthotactically marked, in that they were possible in Basque but not in Spanish (e.g., ​txapel​, ‘beret’). Interestingly, translation priming was obtained in the unmarked condition, but not in the marked condition. Under the assumption that words in bilinguals' two languages are stored together and accessed in parallel,

8

these results were taken to indicate a greater degree of cross-language coactivation for orthotactically unmarked words. One explanation put forward for this finding was that words in the bilingual lexicon might be closer to their translation equivalents if they have similar orthographic properties -- and specifically in the case of Basque and Spanish, if they share orthotactic regularities. Under this account, this closer proximity in lexical space allows for greater coactivation of translation equivalents. Alternatively, or in addition to this possible influence of orthographic similarity, it was proposed that language-specific orthographic/sublexical information might allow for facilitated language detection and thus for faster lateral inhibition of lexical representations in the other language, including translation equivalents (for a similar view, see the BIA+ Extension model in van Kesteren et al., 2012; see also Casaponsa et al., 2014, 2015). Although the current BIA+ model (Dijkstra & van Heuven, 2002) maintains that inhibition occurs only at the lexical level, this lateral inhibition idea is generally consistent with this model in that sublexical cues may lead to more language-selective activation of words. In short, based on these findings, L1 and L2 words with similar orthographic properties were hypothesized to be more integrated in bilingual lexico-semantic memory in the sense that they give rise to more activation and/or less inhibition between the two languages. This is of course quite the opposite of the orthographic cue hypothesis put forth by Gollan et al. (1997). As discussed above, this hypothesis posits that language-specific orthographic information assists in the search for lexical representations, and it is this processing advantage that allows for cross-language priming. However, it is important to emphasize that none of these hypotheses offers an adequate explanation of the masked translation priming

9

findings to date. As previously mentioned, the orthographic cue hypothesis has been undercut by a number of studies demonstrating cross-language priming in same-script bilinguals. Likewise, many studies have shown cross-language priming in different-script bilinguals, especially in the L1-L2 direction, which poses a challenge to the idea that L1 and L2 words with similar orthographic properties give rise to more activation and/or less inhibition between the two languages. Despite their clear empirical shortcomings, however, these hypotheses provide a useful framework for testing the possible influence of script properties on bilingual lexical access. With reference to these hypotheses, therefore, the present study further explores the influence of script on the organization of the bilingual lexicon and on access to L1 and L2 words by examining translation priming in Hindi-English bilinguals. These bilinguals were of particular interest in part because many achieve relatively balanced proficiency in both languages. The country of origin for these bilinguals was India, a linguistically diverse society in which English acts as an official language at the national level (along with Hindi), figures prominently in print and electronic media, and plays an important role in education. Indeed, English is the only language that is included (as a first, second, or third language) in the education systems of all Indian states, and it serves as an important medium of instruction, especially at the secondary and university levels (Annamalai, 2004). In this context, many Indians -- especially the educated elite -- begin learning English early in life, achieve a high level of proficiency in this language, and use it regularly for communication. (For more on the use and status of English in Indian society and education, see Annamalai, 2004.) These characteristics are important in the present set of experiments because many studies have shown that balanced, early bilinguals reveal

10

translation priming in both the L1-L2 and L2-L1 directions (Basnight-Brown & Altarriba, 2007; Duñabeitia et al., 2010; Sabourin et al., 2014; Wang, 2013). Hindi-English bilinguals also provide an especially interesting test case of the influence of script on bilingual lexical access in light of the complex use of orthography in their L1. Hindi has been traditionally written in Devanagari, an alphasyllabary derived from the Brahmi script (for more on this writing system, see Bright, 1996; Vaid & Gupta, 2002). However, the increasing use of standard keyboards has given rise to the use of Romanagari, or Hindi that is transliterated into the Roman alphabet (Rao, Mathur, & Singh, 2013). Despite the prevalence of this Romanized script, it does not have a standard spelling system, and formal Hindi literacy education is typically provided only in Devanagari (Gella, Bali, & Choudhury, 2014; Rao et al., 2013). In terms of the use of these scripts, while Devanagari is mainly found in books, magazines, and other traditional print media, Romanagari often appears in text messages and emails as well as in social media and other user-generated content on the Internet, including discussion forums and blogs (Barman, Das, Wagner, & Foster, 2014; Gella et. al., 2014; Rao et al., 2013). The use of Romanagari and Devanagari together is rare, but occasionally occurs in advertisements. However, Hindi written in Romanagari is often mixed with English in social media and messaging platforms (Gella et. al., 2014), which is likely due to the ease of typing in a single script. With respect to the processing of these orthographic codes, a recent fMRI study (Rao et al., 2013) indicated that while Hindi-English bilinguals are proficient at reading both Devanagari and Romanagari, processing Hindi words in the Roman alphabet appears to be more effortful. For these bilinguals, Hindi words in Devanagari were responded to faster and more accurately than Hindi words in Romanagari and L2 English words (written in the Roman

11

alphabet). Hindi words written in Romanagari were also associated with greater activation in brain areas associated with language processing and attention. The present study examines whether activation between L1 and L2 words in balanced early Hindi-English bilinguals, as indicated by translation priming, is also modulated by the script in which L1 Hindi words are presented. This question is important because hypotheses related to the influence of script differences on the bilingual lexicon would appear to make different predictions for these priming effects depending on the L1 Hindi script. Under the orthographic cue hypothesis, for instance, when L1 Hindi is presented in Devanagari, Hindi-English bilinguals should show clear L1-L2 priming. As detailed above, this is because the orthography of the prime cues a search in the appropriate lexicon, which allows for more efficient processing and thus for subsequent cross-language activation. However, priming in the L2-L1 direction should not be obtained in this case because the Roman alphabet in L2 English primes does not provide a clear cue to language identity -- Hindi or English. When L1 Hindi is also written in the Roman alphabet, the orthography of the prime does not unambiguously indicate language identity for either Hindi or English. In this case, the orthographic cue hypothesis therefore predicts no priming in either the L1-L2 or L2-L1 direction. Casaponsa and Duñabeitia’s (2016) accounts for the influence of script on bilingual lexical access would seem to predict different patterns of results. The specifics of these predictions depend in large part on the nature of the mechanism(s) responsible for the results of their study -- i.e., that translation priming was observed only for L1 and L2 words with similar orthographic properties. Recall that one explanation for this pattern was that words might be closer in lexical space to their translation equivalents if they have similar orthographic

12

properties, allowing for greater cross-language coactivation. For the purposes of the present study, this will be called the ​lexical space hypothesis​. This explanation leads to intriguing predictions for Hindi-English bilinguals. For these bilinguals, each Hindi lexical representation has essentially two orthographic codes -- one in Devanagari, which is of course dissimilar to its English translation equivalent, and another in Romanagari, which is relatively similar to this translation. With regard to the influence of these separate codes on lexical representations, it is important to note that a number of studies on languages with multiple scripts have shown cross-script priming (see Bowers & Michita, 1998; Hino, Lupker, Ogawa, & Sears, 2003; Nakamura, Dehaene, Jobert, Le Bihan, & Kouider, 2005; Okano, Grainger, & Holcomb, 2013, for Japanese; Dimitropoulou, Duñabeitia, and Carreiras, 2011c, for Greek; Bowers, Vigliocco, & Haan, 1998; Kinoshita & Kaplan, 2008, for English lower and upper case letters). For instance, Bowers and Michita (1998) showed similar repetition priming effects for same-script (​やま-や

ま​ ‘mountain’) and cross-script (​山-やま​) prime-target pairs in Japanese. However, this same study showed no cross-modal repetition priming when the prime was a spoken word and the target was a written word (see also Kouider & Dupoux, 2001, for indications that cross-modal priming does not obtain under masked priming procedures). That both the same-script and cross-script conditions yielded comparable priming suggests that even if a word is written in different scripts, it nevertheless points to the same abstract lexical representation (see also Dimitropoulou et al., 2011c; Hino et al., 2003; Nakamura et al., 2005). Further, the absence of cross-modal priming was taken to indicate that words written in different scripts cannot just be related at the phonological level and/or semantic level. Such findings would seem to lead to the conclusion that regardless of whether a Hindi word is presented in Devanagari or Romanagari, it

13

points to the same location in lexical space relative to its English translation. One might therefore predict comparable levels of cross-language coactivation, as indicated by translation priming, regardless of the script properties of L1 Hindi words. The other mechanism put forward by Casaponsa and Duñabeitia (2016) to explain why translation priming was found only for L1 and L2 words with similar orthographic properties was that language-specific script information might allow for facilitated language detection and thus for more efficient inhibition of related lexical representations in the other language. For the purposes of this study, this will be called the ​inhibition hypothesis​. This account suggests different predictions for masked translation priming in Hindi-English bilinguals. Specifically, it would seem to predict less cross-language inhibition, and thus clearer indications of translation priming, when the script of the prime does not unambiguously indicate language identity -- that is, in cases when the masked prime is written in the Roman alphabet. (Note that comparable predictions might be made under a version of the lexical space hypothesis in which Devanagari and Romanagari Hindi words have different lexical form representations -- an issue that is addressed in detail in the ​General Discussion​.) The present study tested these predictions by examining masked translation priming with Hindi-English bilinguals in two lexical decision experiments. In Experiment 1, L1 Hindi words were presented in standard Devanagari script (the ​cross-script experiment​), whereas in Experiment 2, these words were presented in the Roman alphabet (the ​same-script experiment​). Translation priming was tested in both the L1-L2 and L2-L1 directions. Because L2-L1 translation priming appears to be obtained most often in early and relatively balanced bilinguals (see above), the participants in the present study were highly-proficient Hindi-English bilinguals

14

who started learning their L2 English before the age of six. Finally, in both experiments, within-language repetition priming was included as a comparison condition -- and specifically as an additional test of the influence of L1 and L2 primes during lexical decision. In order to facilitate data collection with these early Hindi-English bilinguals, both experiments were conducted using the web-deliverable implementation of the DMDX software package, or webDMDX (Forster & Forster, 2003; Witzel, Cornelius, Witzel, Forster, & Forster, 2013). This allowed participants to access the experiment online by downloading it temporarily onto their own computer. The experiment was then run locally -- that is, on the participant's computer itself -- with the data transmitted over the web only at its conclusion. This made it possible for participants to take part in the experiments either in the presence of the first author, using the same laptop computer, or remotely, using their own hardware. Previous research has indicated that webDMDX provides an effective platform for a range tasks in psycholinguistics and experimental psychology more generally (see Gor, Chrabaszcz, & Cook, 2017; Romanova & Gor, 2017, for lexical decision with unmasked priming; Lee, 2016, for plausibility judgment; Pauszek, Sztybel, & Gibson, in press, for spatial cueing tasks; Price & Witzel, 2017, for acceptability judgment and self-paced moving window reading; and Price, Witzel, & Witzel, 2015, for a cloze task). This is the case even for experimental methods that require precise control over stimulus display timing, including the masked priming paradigm used in the present study (Witzel et al., 2013; Woods, Velasco, Levitan, Wan, & Spence, 2015). The experiments reported below conformed to the best practices for running such experiments on webDMDX, as detailed in Witzel et al. (2013). Specifically, millisecond keywords were used to code display durations so that the stimuli -- and, most crucially, the masked primes -- would be presented for

15

the same amount of time even on testing units with different screen refresh rates. The function was also used in these keywords, which provided the number of refresh intervals and the length of these intervals for each display duration. This made it possible to determine whether the masked primes had in fact been presented for the desired display duration on each testing unit and thus to eliminate data sets obtained from units with refresh rates that were incompatible with the experimental design. Finally, strict inclusion criteria related to response accuracy on words and nonwords in both Hindi and English were established in order to ensure (a) that participants were proficient in both of these languages and (b) that they were attending to the task regardless of whether they took part remotely or in the presence of the experimenter. (See the ​Method​ and ​Results​ sections of each experiment for details related to these design characteristics and procedures.) Experiment 1: Cross-script experiment Method Participants: ​Fifty-nine participants took part in the experiment: 15 in the presence of the first author, using the same laptop computer (Intel Pentium 2.16 GHz processor; 60 Hz refresh rate); 44 remotely, using their own hardware. The participants were acquaintances (family members, friends, and university colleagues) and online connections/contacts of the first author. They had been born and raised in India and were highly proficient in both Hindi and English. Prior to the experiment, participants confirmed (a) that they had begun learning Hindi from birth, (b) that they were proficient readers of Hindi, (c) that they were proficient in English, and (d) that they had begun learning English before the age of six (see the ​Procedures​ section below). There was no independent test of language abilities in Hindi or English. Rather, in order to

16

ensure that the analyses included only the data from participants with high levels of proficiency and strong literacy skills in both Hindi and English, a set of strict inclusion criteria was established. Under these criteria, only the data from participants with error rates (ERs) of less than 20% on each of the four target types in the experiment -- Hindi words, Hindi nonwords, English words, English nonwords -- were included in the analyses. Participants were also removed from the analyses if they self-identified as a native-speaker of a language other than Hindi or if they took the experiment remotely, but on incompatible hardware. (See the ​Results section below for more on the participants who were excluded from the analyses.) Altogether, the data from 32 participants were included in the analyses. Materials and Design: ​For both Hindi and English, 64 words were selected as targets. Half of these words (32) were used to assess translation priming and were chosen based on the norming procedures discussed below. The other half (32) were used to assess within-language repetition priming. Sixty-four nonword targets were also used for each language. Hindi nonwords were created by replacing one or two letters in Hindi words, while English nonwords were selected from the ARC nonword database (Rastle, Harrington, & Coltheart, 2002). All nonwords were ortho/phonotactically legal in their respective languages, and none constituted a word in the other language (i.e., none of the Hindi nonwords was a word in English, and vice versa). For Hindi, word targets were 2-6 letters long, with a mean length of 4.02 letters (​SD​ = 0.97), while nonword targets were 2-7 letters long, with a mean length of 4.25 letters (​SD​ = 0.99). Log frequencies for Hindi word targets were based on the number of occurrences per million in the EMILLE Corpus (2004) of spoken and written Hindi. The mean log frequency for Hindi word targets was 2.54 (​SD​ = 1.94). For English, word targets were 3-9 letters long, with a

17

mean length of 5.53 letters (​SD​ = 1.53), while nonword targets were 4-8 letters long, with a mean length of 6.05 letters (​SD​ = 1.08). Log frequencies for English word targets were based on the number of occurrences per million in the CELEX database (Baayen, Piepenbrock, & van Rijn, 1995; accessed from N-Watch, Davis, 2005). (One target that did not occur in the database -ERASER​ -- was assigned a frequency of .01 occurrences per million.) The mean log frequency for English word targets was 3.62 (​SD​ = 1.78). For translation priming, the related prime was the translation equivalent of the target word (L1-L2: ​म खन-BUTTER​; L2-L1: ​spoon-च ​ मच), while the unrelated prime was a word with no form or semantic connection to the target (L1-L2: च ​ ूड़ी-BUTTER​; L2-L1: ​wind-​च मच). The translation equivalents were noncognates, in that they did not share phonological properties that would be indicative of a common etymological origin (e.g., ​म खन /məkkʰən/-​BUTTER /bʌtəɹ/ and ​spoon​ /spun/-​च मच /tʃəmmətʃ/). For within-language repetition priming, the related prime was the same as the target word (L1-L1: द ​ ु मन-द ​ ु मन; L2-L2: ​eraser-ERASER​), while the unrelated prime was an all-letter-different word with no phonological or semantic connection to the target (L1-L1:​ र​ े त-द ​ ु मन; L2-L2: ​square-ERASER​). (For a complete list of word targets, along with their related and unrelated primes, see Supplemental Material A.) Half of the nonword targets (32) were preceded by nonword primes from the other language (e.g., L1-L2:

शां तक-VOADS​; L2-L1: ​maped-​ ब गल). The other half (32) were preceded by nonword primes from the same language. The within-language repetition priming manipulation was applied to these items. The related prime was the same as the target nonword (L1-L1: ​ मताब-​ मताब; L2-L2: ​yourts-YOURTS​), while the unrelated prime was an all-letter-different nonword (L1-L1:

18

लमेट-​ मताब ; L2-L2: ​finced-YOURTS​). Two counterbalanced lists of items were created such that targets appeared under both the related and unrelated conditions across lists. Translation equivalents were selected based on a norming study. In this study, 10 Hindi-English bilinguals were asked to provide translations for a set of Hindi and English words. The items consisted of counterbalanced lists of 76 Hindi words and 76 English words. Words were considered translation equivalents only if they were translated in the same way in both directions by all of the participants (e.g., ​butter​ was always translated into Hindi as म ​ खन, and likewise म ​ खन was always translated into English as ​butter​). Procedure: ​The experiment was run using the web-based implementation of the DMDX software package, version 4.2.0.1 (Forster & Forster, 2003; Witzel et al., 2013). This was done as follows: A link to the informed consent document for the experiment was placed on the website for the Psycholinguistics Lab at the University of Texas at Arlington. The participant clicked on a link at the bottom of this document to agree to continue with the study. The participant then answered four language background questions: Did you learn Hindi from birth? [YES / NO]; Are you a proficient reader of Hindi? [YES / NO]; Are you proficient in English? [YES / NO]; When did you start learning English? [Before age 6 / After age 6]. Only participants who answered 'YES' to the first three questions and 'Before age 6' on the last question were routed to the link for the experiment. The list referenced by this link was changed regularly so that participants were randomly assigned to the counterbalanced lists. At the beginning of the experiment, participants were asked to type the name of their “mother tongue”. This was done in order to screen out participants who had learned Hindi from birth, but who nevertheless

19

considered another language to be their native language. Only participants who reported Hindi as their mother tongue were included in the analyses. The experiment was then divided into two sections -- one with Hindi targets, the other with English targets. The ordering of these sections was randomly determined by the software. Out of the 32 participants whose data were included in the analyses, 20 (62.50%) took the section with English targets first. Each section began with task directions in English, followed by 16 practice trials. The experimental items were then presented in a different random order for each participant, in four blocks of 32 trials, with a short break after each block. Items were displayed in the center of the screen as black letters on a white background. Each trial consisted of a forward mask (@#$%&@#$%&) for 500 ms, immediately followed by a prime for 50 ms and a target for 500 ms. The display durations for these stimuli were coded in DMDX using keywords that make reference to milliseconds -- for the prime and for the forward mask and target. For each host computer, DMDX calculated the number of refresh intervals that gave the closest approximation to these millisecond durations (for more on this coding, see Witzel et al., 2013). English stimuli were presented in 12-point Courier New font, with primes in lower case, and targets in upper case. Hindi primes were presented in 10.5-point Mangal font, while targets were presented in 12-point Mangal font. Participants were asked to decide whether each target letter string was a real word or not as quickly and accurately as possible. They pressed the right Ctrl key if the target string was a word, and the left Ctrl key if it was not. After each trial, participants were given feedback on the speed and accuracy of their response. Results

20

The datasets from seven participants were eliminated because they indicated a native language other than Hindi (specifically, Kannada, Konkani, Marathi, or Telugu) at the beginning of the experiment. The datasets from 14 participants with ERs of greater than 20% on at least one of the four target types -- Hindi words, Hindi nonwords, English words, English nonwords -were also excluded from the analyses. Finally, datasets from participants who took part in the experiment remotely on hardware that was not suited to the design of the experiment were also removed. Specifically, the datasets from two remote participants were eliminated because their output files indicated display errors on nearly every trial. These errors meant that display frames during the experiment -- including those for the primes -- were regularly presented for longer than intended, by as much as 33 ms. The datasets from four more remote participants were excluded because their testing units had refresh rates of 50 Hz. For these participants, the closest approximation to the coding for the prime duration was 3 refresh intervals of 20 ms. This meant that the primes were displayed for 60 ms, instead of the intended 50 ms. For the 32 participants whose data were included in the analyses reported below -- six of whom took part in the experiment in the presence of the first author on the same laptop computer, while 26 participated remotely on their own hardware -- the mean overall ER was 5.06% (​SD​ = 2.84), with a mean ER of 5.59% (​SD​ = 3.09) for Hindi word/nonword targets and a mean ER of 4.47% (​SD​ = 3.42) for English word/nonword targets. Thirty participants (all six of those who used the same laptop computer as well as 24 of the remote participants) took the experiment on units with refresh rates of 60 Hz. For the remaining two participants, one used a computer with a 40 Hz monitor, while another used a unit with a 59 Hz monitor. For the 60 Hz units, primes were presented for 3 refresh intervals of 16.67 ms, or 50 ms. For the 40 Hz unit,

21

primes were presented for 2 refresh intervals of 25 ms, or 50 ms. For the 59 Hz unit, primes were presented for 3 refresh intervals of 16.95 ms, or 50.85 ms. Reaction times (RTs) for items on which the participant made an error were not included in the analyses. Items with RTs shorter than 300 ms or longer than 2000 ms were also discarded (0.53% of the data), and outlier data points were adjusted to two SD units above and below the participant’s mean for each condition (4.65% of the data). The RT and ER data for translation priming (L1-L2 and L2-L1) and within-language repetition priming (L1-L1 and L2-L2) were analyzed separately for word targets. The mean RTs and ERs for each of the priming conditions are shown in Table 1. Within-language repetition priming for nonwords was also analyzed. The analyses consisted of 2x2x2 ANOVAs for both subjects (​F​1) and items (​F​2) with target language (L1: Hindi, L2: English) and prime type (related, unrelated) as predictor variables and list/item group as a grouping factor (Pollatsek & Well, 1995), as well as tests of the simple effect of prime type for each priming condition. Furthermore, because ​F​1.​F​2 analyses have been argued to be anti-conservative (see e.g., Barr, Levy, Scheepers, & Tily, 2013), the simple effect of prime type in each priming condition was also analyzed using linear mixed effect models. These models were applied to raw RTs, log-transformed RTs, and inverse-transformed RTs and included random slopes for the fixed effect for both subjects and items.



For translation priming (L1-L2 and L2-L1), the RT analyses revealed a significant main effect of target language (​F​1(1, 30) = 26.29, ​p ​< .001; ​F​2(1, 60) = 75.02, ​p​ < .001), indicating

22

that L2 English words were generally responded to faster than L1 Hindi words. Although the main effect of prime type was not significant (​F​1(1, 30) = 2.34, ​p​ = .137; ​F​2(1, 60) = 2.54, ​p​ = .116), there was a marginal interaction of target language and prime type (​F​1(1, 30) = 3.50, ​p​ = .071; ​F​2(1, 60) = 4.10, ​p​ = .047). This interaction reflects the fact that there was a reliable 21 ms translation priming effect in the L1-L2 direction (​F​1(1, 30) = 10.82, ​p​ = .003; ​F​2(1, 30) = 12.66, p​ = .001), but no hint of a priming effect in the L2-L1 direction (both ​F​'s < 1). The priming effect in the L1-L2 direction was also reliable under linear mixed effects models over raw RTs (​t​ = 2.80, ​p​ = .005), log-transformed RTs (​t​ = 2.89, ​p​ = .004), and inverse-transformed RTs (​t​ = 2.89, p​ = .004). The priming effect in the L2-L1 direction, however, was not statistically reliable under any of these analyses (raw RTs: ​t​ = 0.23, ​p​ = .815; log-transformed RTs: ​t​ = 0.02, ​p​ = .984; inverse-transformed RTs: ​t​ = 0.30, ​p​ = .766). The ER analyses did not reveal any reliable effects (target language: ​F​1(1, 30) = 1.43; ​F​2(1, 60) = 1.14; all other ​F​'s < 1). A different pattern of results was found for within-language repetition priming (L1-L1 and L2-L2). The RT analyses revealed a significant main effect of target language (​F​1(1, 30) = 23.25, ​p​ < .001; ​F​2(1, 60) = 61.53, ​p​ < .001), again indicating that L2 English words were generally responded to faster than L1 Hindi words. However, unlike for translation priming, there was a robust main effect of prime type (​F​1(1, 30) = 24.39, ​p​ < .001; ​F​2(1, 60) = 33.13, ​p​ < .001), but no suggestion of a target language x prime type interaction (​F​1(1, 30) = 1.08; ​F​2(1, 60) = 1.05). This pattern of results reflects the fact that there was reliable within-language repetition priming for both the L1-L1 (​F​1(1, 30) = 14.83, ​p​ < .001; ​F​2(1, 30) = 16.97, ​p​ < .001) and L2-L2 (​F​1(1, 30) = 12.43, ​p​ = .001; ​F​2(1, 30) = 17.36, ​p​ < .001) priming conditions. Linear mixed effects models also revealed reliable repetition priming effects in both the L1-L1

23

condition (raw RTs: ​t​ = 3.82, ​p​ < .001; log-transformed RTs: ​t​ = 4.65, ​p​ < .001; inverse-transformed RTs: ​t​ = 5.25, ​p​ < .001) and the L2-L2 condition (raw RTs: ​t​ = 3.49, ​p​ < .001; log-transformed RTs: ​t​ = 4.50, ​p​ < .001; inverse-transformed RTs: ​t​ = 4.95, ​p​ < .001). The ER analyses again did not reveal any reliable effects (target language: ​F​1(1, 30) = 2.07, ​p​ = .161; F​2(1, 60) < 1; prime type: ​F​1(1, 30) = 1.55, ​p​ = .223; ​F​2(1, 60) = 1.96, ​p​ = .167; target language x prime type: both ​F​'s < 1). Nonword targets did not show comparable within-language repetition priming, indicating that the robust repetition priming observed for L1 and L2 word targets was not simply a low-level form priming effect. The mean RTs for the relevant conditions were as follows: L1-L1/related - 784 ms (standard error of the mean​ (SEM) ​for repeated measures: 15); L1-L1/unrelated - 806 ms (​SEM​: 18); L2-L2/related - 680 ms (​SEM​: 17); L2-L2/unrelated - 685 ms (​SEM​: 15). Analyses of these RT data revealed a significant main effect of target language (​F​1(1, 30) = 12.44, ​p​ = .001; ​F​2(1, 60) = 133.23, ​p​ < .001), indicating that L2 English nonwords were generally responded to faster than L1 Hindi nonwords. The effect of prime type, however, only approached significance (​F​1(1, 30) = 3.10, ​p​ = .088; ​F​2(1, 60) = 3.03, ​p​ = .087). Although the interaction of target language and prime type was not reliable (​F​1(1, 30) = 1.41; ​F​2(1, 60) < 1), tests of the simple effect of prime type revealed a suggestion of a priming effect only for L1 Hindi nonword targets (​F​1(1, 30) = 3.22, ​p​ = .083; ​F​2(1, 30) = 1.88, ​p​ = .181; L2 English nonword targets: ​F​1(1, 30) < 1; ​F​2(1, 30) = 1.21). The mean ERs for these conditions were as follows: L1-L1/related - 6.4% (​SEM​: 0.9); L1-L1/unrelated - 5.1% (​SEM​: 1.0); L2-L2/related 8.0% (​SEM​: 1.1); L2-L2/unrelated - 4.9% (​SEM​: 0.7). In the analyses of these ER data, there was a marginally significant effect of prime type (​F​1(1, 30) = 3.45, ​p​ = .073; ​F​2(1, 60) = 5.73, ​p​ =

24

.020; all other ​F​'s < 1). In this case, however, this effect suggested that there were higher ERs for related prime-target pairs. Tests of the simple effect of prime type indicated that this effect was reliable only for L2 English nonword targets (​F​1(1, 30) = 4.62, ​p​ = .040; ​F​2(1, 30) = 5.60, ​p​ = .025; L1 Hindi nonword targets: ​F​1(1, 30) < 1; ​F​2(1, 30) = 1.05). Discussion This cross-script experiment, in which L1 Hindi words were presented in Devanagari and L2 English words were presented in the Roman alphabet, revealed clear L1-L2 translation priming, but no indication of priming in the L2-L1 direction. These results are thus consistent with many studies that have found a comparable asymmetry in masked translation priming. This asymmetry is particularly interesting in light of the fact that early Hindi-English bilinguals were tested. As discussed above, several studies have shown masked translation priming in both directions for early and simultaneous bilinguals (Duñabeitia et al., 2010; Sabourin et al., 2014; Wang, 2013). Interestingly, the overall pattern of results suggests that this asymmetry cannot be explained in terms of generally slower processing for L2 words. In fact, the Hindi-English bilinguals in this experiment responded to L2 English targets faster than to L1 Hindi targets (e.g., 588 ms vs. 684 ms for word targets in the within-language repetition priming condition). It is also important to note that this asymmetry resists interpretation in terms of a general inability to process masked primes in the L2. Indeed, there were clear indications of within-language repetition priming for both L1 Hindi and L2 English. Most importantly, these findings provide a basis for comparison with those of Experiment 2, in which both L1 Hindi and L2 English were presented in the Roman alphabet, and thus for the examination of the three hypotheses outlined above for the role of orthography

25

in bilingual lexical access. Under the orthographic cue hypothesis, the L1-L2 translation priming observed in the present experiment should be reduced or eliminated when both L1 Hindi and L2 English are presented in the same script. This is because the script of the L1 prime no longer provides a clear cue to language identity. A different set of predictions would apply under the lexical space hypothesis -- an account whereby orthographic properties influence the location of words with respect to their translation equivalents. Assuming that a Hindi word points to the same location in lexical space whether it is presented in Devanagari or Romanagari, this hypothesis would seem to predict comparable levels of cross-language coactivation regardless of the script in which L1 Hindi words are presented. If this is the case, the translation priming patterns in the same-script experiment should be similar to those observed in the cross-script experiment reported above. Finally, recall that under the inhibition hypothesis, language-specific orthographic information allows for facilitated language detection and thus for more efficient inhibition of words in the other language. The findings from this cross-script experiment seem to pose several challenges for such an account. Again, translation priming was obtained in the L1-L2 direction, but not in the L2-L1 direction. In other words, this priming was obtained from orthographically distinct Devanagari primes (i.e., L1 Hindi primes), but not from Roman alphabet primes (i.e., L2 English primes) that could correspond to either Hindi or English. Both of these findings would seem to run contrary to the predictions of the inhibition hypothesis. The same-script experiment allows for an examination into whether this pattern of results also holds when both L1 Hindi and L2 English primes are presented in the Roman alphabet. Experiment 2: Same-script experiment Method

26

Participants: ​Forty-three participants took part in the experiment: 20 in the presence of the first author, using the same laptop computer (Intel Pentium 2.16 GHz processor; 60 Hz refresh rate); 23 remotely, using their own hardware. As in the previous experiment, the participants were acquaintances (family members, friends, and university colleagues) and online connections/contacts of the first author. They had been born and raised in India and were highly proficient in both Hindi and English. Prior to the experiment, participants confirmed (a) that they had begun learning Hindi from birth, (b) that they were proficient readers of Hindi, (c) that they were proficient in English, and (d) that they had begun learning English before the age of six. As in Experiment 1, a strict set of inclusion criteria was used in order to ensure that only the data from participants with high levels of proficiency and strong literacy skills in both Hindi and English were included in the analyses. Under these criteria, only the data from participants with ERs of less than 20% on each of the four target types in the experiment -- Hindi words, Hindi nonwords, English words, English nonwords -- were included. As in the previous experiment, participants were also removed from the analyses if they self-identified as a native-speaker of a language other than Hindi or if they took the experiment remotely, but on incompatible hardware. (See the ​Results​ section below for more on the participants who were excluded from the analyses.) Altogether, the data from 32 participants were included in the analyses. Materials and Design: ​As in Experiment 1, 64 words were selected as targets for both Hindi and English. Half of these words (32) were used to assess translation priming, while the other half (32) were used to assess within-language repetition priming. Most of these translation and repetition pairs -- 45/64 of the translation pairs and 45/64 of the repetition pairs, or 70.31% in both cases -- were carried over from Experiment 1. However, 38 of these pairs (19 translation

27

pairs and 19 repetition pairs, or 29.69% in both cases) were replaced in order that all of the Hindi words (primes and targets) had consistent Romanagari spellings. (See below for more on the spelling consistency norming for these Hindi words.) Translation pairs were randomly reassigned to L1-L2 and L2-L1 priming conditions. The nonwords for each language were the same as in Experiment 1, except that in this case, the Hindi nonwords appeared in Romanagari. For Hindi, word targets were 3-7 letters long, with a mean length of 5.08 letters (​SD​ = 1.09), while nonword targets were 3-8 letters long, with a mean length of 5.25 letters (​SD​ = 1.04). Log frequencies for Hindi word targets were based on the number of occurrences per million in the EMILLE Corpus (2004) of spoken and written Hindi. The mean log frequency for Hindi word targets was 2.40 (​SD​ = 2.06). For English, word targets were 3-9 letters long, with a mean length of 5.30 letters (​SD​ = 1.63), while nonword targets were 4-8 letters long, with a mean length of 6.05 letters (​SD = 1.08). Log frequencies for English word targets were based on the number of occurrences per million in the CELEX database (Baayen, Piepenbrock, & van Rijn, 1995; accessed from N-Watch, Davis, 2005). (Two targets that did not occur in the database -- ​ERASER​ and ​CHILI​ -were assigned a frequency of .01 occurrences per million.) The mean log frequency for English word targets was 3.25 (​SD​ = 1.96). Independent samples ​t​-tests indicated no reliable differences between the log frequencies of the target types in Experiments 1 and 2 (Hindi repetition targets: p​ = .525; Hindi translation targets: ​p​ = .864; English repetition targets: ​p​ = .942). There was only a non-significant trend suggesting that English translation targets had generally higher frequencies in Experiment 1 (​M​ = 4.15, ​SD​ = 1.35) than in Experiment 2 (​M​ = 3.43, ​SD​ = 1.97; t​(62) = 1.71, ​p​ = .092).

28

For translation priming, the related prime was the noncognate translation equivalent of the target word (L1-L2: ​kahani-STORY​; L2-L1: ​blue-NEELA​), while the unrelated prime was an all-letter-different word with no phonological or semantic connection to the target (L1-L2: grah-STORY​; L2-L1: ​photo-NEELA​). Translation equivalents were selected based on the norming procedures discussed in Experiment 1. That is, only words that were translated consistently in both directions were used as translation equivalents. For within-language repetition priming, the related prime was the same as the target word (L1-L1: ​sapna-SAPNA​; L2-L2: ​orange-ORANGE​), while the unrelated prime was again an all-letter-different word with no phonological or semantic connection to the target (L1-L1: ​chaar-SAPNA​; L2-L2: clown-ORANGE​). (For a complete list of word targets, along with their related and unrelated primes, see Supplemental Material B.) As in the previous experiment, half of the nonword targets (32) were preceded by nonword primes from the other language (e.g., L1-L2: shaantik-VOADS​; L2-L1: ​maped-BIGIL​). The other half (32) were preceded by nonword primes from the same language. The within-language repetition priming manipulation was again applied to these items. The related prime was the same as the target nonword (L1-L1: ​buvar-BUVAR​; L2-L2: ​flinns-FLINNS​), while the unrelated prime was an all-letter-different nonword (L1-L1: dohal-BUVAR​; L2-L2: ​gront-FLINNS​). Two counterbalanced lists of items were created such that targets appeared under both the related and unrelated conditions across lists. The Hindi words in the experiment were also normed for spelling consistency. This was done by asking 10 Hindi-English bilinguals to provide the Roman alphabet spellings for 196 Hindi words written in Devanagari. Hindi words were selected as primes or targets only if they were given the same spelling by at least seven of the informants. For the Hindi words that were

29

selected for the experiment, there was 93.28% agreement on their Romanagari spellings. These informants also provided possible Roman alphabet spellings for the 64 Hindi nonword targets. There was 77.66% agreement on the Romanagari spellings for these nonwords. The most commonly provided spelling for each nonword was used in the experiment. Procedure: ​The procedures were the same as in Experiment 1. However, in this case, both Hindi and English stimuli were presented in the Roman alphabet. Specifically, they were presented in 12-point Courier New font, with primes in lower case, and targets in upper case. The experiment was again divided into two sections -- one with Hindi targets, the other with English targets -- with the ordering of these sections randomly determined by the software. Out of the 32 participants whose data were included in the analyses, 17 (53.13%) took the section with English targets first. Results The dataset from one participant was eliminated because he/she indicated a native language other than Hindi (specifically, Marwari) at the beginning of the experiment. The datasets from five participants with ERs of greater than 20% on at least one of the four target types -- Hindi words, Hindi nonwords, English words, English nonwords -- were also excluded from the analyses. The datasets from another five participants were removed because their output files indicated display errors on nearly every trial. These errors meant that display frames during the experiment -- including those for the primes -- were regularly presented for longer than anticipated, by as much as 33 ms. Of the 32 participants whose data were included in the analyses, 18 took part in the experiment in the presence of the first author on the same laptop computer, while 14 participated remotely on their own hardware. All of these participants took

30

the experiment on units with refresh rates of 60 Hz. The primes were therefore presented for 3 refresh intervals of 16.67 ms, or 50 ms. The mean overall ER was 6.19% (​SD​ = 2.18), with a mean ER of 7.53% (​SD​ = 3.01) for Hindi word/nonword targets and a mean ER of 4.66% (​SD​ = 2.98) for English word/nonword targets. A comparison with Experiment 1 suggested that participants in the present experiment had higher overall ERs (​t​(62) = 1.78, ​p​ = .080). However, this difference was not reliable for both Hindi and English. Rather, participants in the present experiment had higher ERs only for Hindi words/nonwords (​t​(62) = 2.54, ​p​ = .013). This difference was fully expected, however, in light of the fact that participants in Experiment 1 responded to Hindi words/nonwords written in the canonical Devanagari script, while participants in this experiment responded to Hindi words/nonwords written in the as-yet-unstandardized Romanagari script. For English words/nonwords -- which were presented in the same way in both experiments -- there was no reliable difference between participants' ERs in the two experiments (​t​(62) = .23, ​p​ = .816). RTs for items on which the participant made an error were not included in the analyses. Items with RTs shorter than 300 ms or longer than 2000 ms were also discarded (0.39% of the data), and outlier data points were adjusted to two SD units above and below the participant’s mean for each condition (4.36% of the data). The analysis procedures followed those of the previous experiment. The mean RTs and ERs for each of the priming conditions are shown in Table 2.



31

For translation priming (L1-L2 and L2-L1), the RT analyses revealed a significant main effect of target language (​F​1(1, 30) = 94.36, ​p​ < .001; ​F​2(1, 60) = 86.08, ​p​ < .001), indicating that L2 English words were generally responded to faster than L1 Hindi words. There was also a robust main effect of prime type (​F​1(1, 30) = 23.99, ​p​ < .001; ​F​2(1, 60) = 21.65, ​p​ < .001), but no suggestion of a target language x prime type interaction (both ​F​'s < 1). This reflects the fact that unlike in Experiment 1, there was reliable translation priming in both the L1-L2 direction (​F​1(1, 30) = 12.92, ​p​ = .001; ​F​2(1, 30) = 35.69, ​p​ < .001) and L2-L1 direction (​F​1(1, 30) = 5.95, p​ = .021; ​F​2(1, 30) = 5.35, ​p​ = .028). Linear mixed effects models also revealed reliable translation priming effects in both the L1-L2 condition (raw RTs: ​t​ = 3.68, ​p​ < .001; log-transformed RTs: ​t​ = 4.09, ​p​ < .001; inverse-transformed RTs: ​t​ = 4.20, ​p​ < .001) and the L2-L1 condition (raw RTs: ​t​ = 2.08, ​p​ = .038; log-transformed RTs: ​t​ = 2.22, ​p​ = .026; inverse-transformed RTs: ​t​ = 2.27, ​p​ = .023). The ER analysis also revealed a pattern of results consistent with translation priming in the L2-L1 direction. Under this analysis, there was a marginal main effect of target language (​F​1(1, 30) = 8.78, ​p​ = .006; ​F​2(1, 60) = 3.59, ​p​ = .063), suggesting that L2 English words were generally responded to more accurately than L1 Hindi words. While the main effect of prime type was not significant (​F​1(1, 30) < 1; ​F​2(1, 60) = 1.10), there was a reliable target language x prime type interaction (​F​1(1, 30) = 6.43, ​p​ = .017; ​F​2(1, 60) = 4.40, ​p​ = .040). This interaction reflects the fact that there was a marginally significant translation priming effect only in the L2-L1 direction (​F​1(1, 30) = 5.00, ​p​ = .033; ​F​2(1, 30) = 3.34, ​p​ = .078; L1-L2: ​F​1(1, 30) < 1; F​2(1, 30) = 1.06).

32

A similar pattern of results was found for within-language repetition priming (L1-L1 and L2-L2). The RT analyses revealed a significant main effect of target language (​F​1(1, 30) = 123.68, ​p​ < .001; ​F​2(1, 60) = 100.62, ​p​ < .001), again indicating that L2 English words were generally responded to faster than L1 Hindi words. As was the case for translation priming, there was also a robust main effect of prime type (​F​1(1, 30) = 56.08, ​p​ < .001; ​F​2(1, 60) = 90.16, ​p​ < .001), but no target language x prime type interaction (​F​1(1, 30) = 1.46; ​F​2(1, 60) = 1.69, ​p​ = .199). This reflects the fact that there was reliable within-language repetition priming for both the L1-L1 (​F​1(1, 30) = 14.04, ​p​ < .001; ​F​2(1, 30) = 24.85, ​p​ < .001) and L2-L2 (​F​1(1, 30) = 44.59, ​p < .001; ​F​2(1, 30) = 89.89, ​p​ < .001) priming conditions. Linear mixed effects models also revealed reliable repetition priming effects in both the L1-L1 condition (raw RTs: ​t​ = 4.06, ​p​ < .001; log-transformed RTs: ​t​ = 5.13, ​p​ < .001; inverse-transformed RTs: ​t​ = 5.50, ​p​ < .001) and the L2-L2 condition (raw RTs: ​t​ = 6.49, ​p​ < .001; log-transformed RTs: ​t​ = 8.34, ​p​ < .001; inverse-transformed RTs: ​t​ = 9.51, ​p​ < .001). The ER analysis also revealed a pattern of results consistent with repetition priming, particularly for L2-L2 prime-target pairs. Specifically, although the main effect of target language was not significant (​F​1(1, 30) = 1.79, ​p​ = .191; ​F​2(1, 60) < 1), there was a reliable effect of prime type (​F​1(1, 30) = 9.84, ​p​ = .004; ​F​2(1, 60) = 10.40, ​p​ = .002), indicating that related prime-target pairs were generally responded to more accurately. Although the target language x prime type interaction only approached significance (​F​1(1, 30) = 2.84, ​p​ = .102; F​2(1, 60) = 2.20, ​p ​= .144), this repetition priming effect was found to be reliable only for the L2-L2 priming condition (​F​1(1, 30) = 12.85, ​p​ = .001; ​F​2(1, 30) = 10.73, ​p​ = .003; L1-L1: ​F​1(1, 30) = 1.57, ​p​ = .220; ​F​2(1, 30) = 1.57, ​p​ = .220).

33

Nonword targets did not show comparable within-language repetition priming. As in Experiment 1, this result indicates that the robust repetition priming effects observed for L1 and L2 word targets was lexical in nature. The mean RTs for the relevant conditions were as follows: L1-L1/related - 711 ms (​SEM​: 12); L1-L1/unrelated - 724 ms (​SEM​: 10); L2-L2/related - 560 ms (​SEM​: 11); L2-L2/unrelated - 564 ms (​SEM​: 9). Analyses of these RT data revealed only a significant main effect of target language (​F​1(1, 30) = 71.04, ​p​ < .001; ​F​2(1, 60) = 179.38, ​p​ < .001; prime type: ​F​1(1, 30) = 2.29, ​p​ = .141; ​F​2(1, 60) = 1.39; target language x prime type: both F​'s < 1). As was the case for word targets, this effect indicated that L2 English nonwords were generally responded to faster than L1 Hindi nonwords. The mean ERs for these conditions were as follows: L1-L1/related - 9.0% (​SEM​: 1.0); L1-L1/unrelated - 6.4% (​SEM​: 1.0); L2-L2/related - 4.5% (​SEM​: 0.9); L2-L2/unrelated - 5.9% (​SEM​: 1.0). In the analyses of these ER data, the main effect of target language approached significance (​F​1(1, 30) = 4.20, ​p​ = .049; ​F​2(1, 60) = 1.81, ​p​ = .183), suggesting that L2 English nonwords were generally responded to more accurately than L1 Hindi nonwords. While the main effect of prime type was not significant (both ​F​'s < 1), the language x prime type interaction also approached significance (​F​1(1, 30) = 2.79, ​p​ = .105; ​F​2(1, 60) = 4.51, ​p​ = .038). Tests of the simple effect of prime type indicated that this effect only approached significance for the L1-L1 priming condition (​F​1(1, 30) = 2.57, ​p​ = .119; ​F​2(1, 30) = 3.51, ​p​ = .071; L2-L2: ​F​1(1, 30) < 1; ​F​2(1, 30) = 1.21). In this case, this effect suggested that related prime-target pairs were associated with higher ERs under this condition. Discussion The results of this same-script experiment, in which both L1 Hindi and L2 English words were presented in the Roman alphabet, revealed translation priming in both the L1-L2 and L2-L1

34

directions. This pattern of results contrasts with those of the previous cross-script experiment (Experiment 1), in which there was translation priming in the L1-L2 direction, but no hint of this effect in the L2-L1 direction. Taken together, these findings thus indicate a clear influence of script properties on masked translation priming. The implications of these results for hypotheses related to the role of orthography in bilingual lexical access are addressed in detail in the ​General Discussion​ section. In other ways, the results of this experiment were similar to those of Experiment 1. As in the previous experiment, L1 targets were again responded to slower than L2 targets (e.g., 616 ms vs. 511 ms respectively for word targets in the within-language repetition priming condition). This pattern of results is not particularly surprising in the present experiment, however, in light of previous evidence indicating processing difficulty for L1 Hindi words written in Romanagari compared to L2 English words (Rao et al., 2013). It is important to note that despite any processing costs that might have been incurred by this script, L1 Hindi words written in Romanagari were nevertheless able to be processed automatically. This is evidenced by the fact that as in Experiment 1, there was within-language repetition priming for both L2 English and L1 Hindi. More importantly, as discussed above, these Hindi words in Romanagari also acted as effective translation primes in the L1-L2 priming condition. General Discussion This study reports on two experiments investigating the effects of script differences on masked translation priming in highly-proficient early Hindi-English bilinguals. In the cross-script experiment (Experiment 1), L1 Hindi was presented in Devanagari, while L2 English was presented in the Roman alphabet. In the same-script experiment (Experiment 2), both L1

35

Hindi and L2 English were presented in the Roman alphabet. Although the experiments revealed similar translation priming effects in the L1-L2 direction, only the same-script experiment yielded priming in the L2-L1 direction. More specifically, in an analysis of these translation priming conditions that included experiment (cross-script, same-script), target language (L1: Hindi, L2: English), and prime type (related, unrelated) as predictor variables, there was a reliable experiment x prime type interaction (​F​(1,60) = 5.28,​ p​ = .025; prime type: ​F​(1,60) = 20.24, ​p​ < .001). This interaction indicates that there was stronger translation priming in the same-script experiment. Further analyses suggest that this interaction is driven primarily by differences between the L2-L1 priming effects in the two experiments. For the L1-L2 direction, in a follow-up analysis that included both experiment and prime type as predictors, only the main effect of prime type was significant (​F​(1,60) = 22.87, ​p​ < .001; experiment x prime type: ​F​(1,60) = 1.83, ​p ​= .181). As detailed above, this reflects the fact that reliable L1-L2 translation priming was found in both the cross-script experiment (21 ms; ​p​ < .01) and the same-script experiment (37 ms; ​p​ < .01). For the L2-L1 direction, however, this follow-up analysis revealed a marginal experiment x prime type interaction (​F​(1,60) = 3.00, ​p​ = .088; prime type: ​F​(1,60) = 2.59, ​p​ = .113). As discussed above, this reflects the fact that although there was absolutely no suggestion of L2-L1 translation priming in the cross-script experiment (-1 ms; ​n.s.​), there was a reliable priming effect in this direction in the same-script experiment (24 ms; ​p​ < .05). A different pattern of results was found for within-language repetition priming. In this case, there were robust L1-L1 and L2-L2 priming effects in both experiments (all ​p​'s < .01). The translation priming effects in this study can also be represented in terms of standardized effect sizes. This approach was adopted in Wen and van Heuven's (2017)

36

meta-analysis of masked translation priming effects in both the L1-L2 and L2-L1 directions. The standardized effect sizes in the present study match well with those reported in this meta-analysis. With respect to L1-L2 priming, the standardized effect sizes in the meta-analysis ranged from 0.29 to 2.50. In the present study, the effect sizes for the L1-L2 priming conditions fell squarely within this range and were similar in the two experiments -- 0.58 (95% confidence interval (​CI​): 0.19, 0.97) in the cross-script experiment and 0.64 (95% ​CI​: 0.24, 1.03) in the same-script experiment. For L2-L1 priming, the standardized effect sizes in the meta-analysis ranged from -0.23 to 1.09. In the present study, despite clear differences between the effect sizes for L2-L1 priming in the two experiments, these values nevertheless again fell within this range -- -0.01 (95% ​CI​: -0.37, 0.34) in the cross-script experiment and 0.43 (95% ​CI​: 0.06, 0.81) in the same-script experiment. In addition to these priming patterns, the Hindi-English bilinguals in this study were found to respond faster to L2 English targets than to L1 Hindi targets, regardless of whether the L1 targets were presented in Devanagari or Romanagari (all ​p​'s < .001). There are several possible explanations for this finding. As mentioned above, in the same-script experiment, some portion of this overall RT disparity might be attributed to processing difficulty for Hindi words/nonwords written in the Romanagari script (Rao et al., 2013). The same explanation, however, cannot be applied to the cross-script experiment, in which Hindi words/nonwords were presented in Devanagari. Another explanation might appeal to frequency differences between the Hindi and English words in this study. In both experiments, Hindi words had significantly lower log frequencies than their English counterparts (cross-script experiment: ​t​(126) = 3.28, ​p​ = .001; same-script experiment: ​t​(126) = 2.37, ​p​ = .019). However, because these frequency counts were

37

based on very different corpora, these comparisons should be treated with caution. This frequency disparity also cannot account for the faster RTs to Hindi nonwords. To the extent that the difference between Hindi and English nonwords relates to stimulus characteristics, it would have to be attributed to some uncontrolled-for disparity related to their form properties (e.g., bigram frequency, neighborhood density; for more on how such factors influence rejection times for L1- and L2-based nonwords, see Lemhöfer & Dijkstra, 2004; Lemhöfer & Radach, 2009). Although it is difficult to identify the precise nature of the generally faster responses for L2 English words/nonwords, this finding provides another indication that the Hindi-English bilinguals in this study were highly proficient in English and had strong literacy skills in this language. Indeed, this pattern of results suggests that these bilinguals were as proficient in L2 English as in L1 Hindi -- and possibly more proficient in their L2, at least in terms of the reading skills involved in visual lexical decision. With regard to the interpretation of the priming effects observed in this study -- and of the translation priming effects in particular -- it is first important to consider these findings in relation to the orthographic cue hypothesis (Gollan et al., 1997), the lexical space hypothesis, and the inhibition hypothesis (Casaponsa & Duñabeitia, 2016). As detailed above, these hypotheses posit that orthographic information influences bilingual lexical access in different ways, and each appears to make distinct predictions for L1-L2 and L2-L1 translation priming in this study. Interestingly (although somewhat as anticipated), none of these hypotheses provides a straightforward account for the observed pattern of results. First, consider the orthographic cue hypothesis. This hypothesis holds that when bilinguals' two languages have different scripts, orthographic information can cue a search in the appropriate lexicon. In masked translation

38

priming, this information leads to facilitated access to the prime and thus to activation of its translation equivalent. In terms of the present study, this hypothesis predicts translation priming for Hindi-English bilinguals in the L1-L2 direction, but only in the cross-script experiment -- i.e., when the orthography of the L1 Hindi prime provides a clear cue to language identity. This prediction, however, was not borne out. While the cross-script experiment did indeed reveal priming in the L1-L2 direction, there was also priming in both the L1-L2 and L2-L1 directions in the same-script experiment. This latter set of results is problematic for the orthographic cue hypothesis because script did not provide a clear cue to language identity for either Hindi or English primes in the same-script experiment, and yet significant priming effects were observed in both translation directions. The results of these experiments are also inconsistent with the lexical space hypothesis. As discussed above, this hypothesis refers to one of the accounts for the findings reported in Casaponsa and Duñabeitia (2016), in which Basque-Spanish bilinguals showed translation priming only for prime-target pairs that shared orthotactic properties. Under this hypothesis, translation equivalents that have shared sublexical information are stored more closely in lexical space, and this proximity allows for greater coactivation. It is important to note that it is not necessary to assume a specific location for Hindi words relative to English words in the Hindi-English bilingual lexicon in order to apply this hypothesis to the present study. It might be the case that Hindi words are located relatively close to their English counterparts by virtue of their orthographically-similar Romanagari codes. But it could just as easily be the case that they are located relatively far away from English words because they are also associated with dissimilar Devanagari orthographic codes. The crucial point is that any hypothesis that appeals to

39

location in lexical space and that assumes unified lexical form representations for words written in different scripts would predict comparable levels of cross-language coactivation in the Hindi-English bilingual lexicon, regardless of the script in which L1 Hindi is presented. With respect to the present study, therefore, such a hypothesis would predict similar patterns of translation priming when L1 Hindi is written in Devanagari as well as when it is presented in Romanagari. But this was clearly not the case. Only L1-L2 priming was observed when L1 Hindi was presented in Devanagari, whereas priming in both the L1-L2 and L2-L1 directions was found when L1 Hindi was presented in Romanagari. It is important to note that the lexical space hypothesis might be able to explain these translation priming patterns -- and the L2-L1 priming effects in particular -- by positing that L1 Hindi words in Romanagari are stored relatively close to L2 English words (including their translation equivalents), while the same L1 Hindi words in Devanagari are stored farther away from L2 English words. This would require that Hindi words have two lexical form representations -- one associated with Romanagari and another associated with Devanagari -- that occupy different locations in lexical space. However, as previously mentioned, a hypothesis of this type would appear to be undercut by findings from within-language cross-script repetition priming studies (Bowers et al., 1998; Bowers & Michita, 1998; Dimitropoulou et al., 2011c; Hino et al., 2003; Kinoshita & Kaplan, 2008; Nakamura et al., 2005; Okano et al., 2013). Such studies have indicated that even if a word is written in different scripts, it nevertheless points to the same abstract lexical form representation. The inhibition hypothesis also fails to explain the complete pattern of translation priming in this study. As discussed above, this hypothesis refers to another account for the findings reported in Casaponsa and Duñabeitia (2016). Under this hypothesis, the information provided

40

by words with language-specific orthographic properties allows for facilitated language detection and thus for more efficient inhibition of related lexical representations in the other language. The flip side of this is that there should be less cross-language inhibition -- and therefore clear indications of between-language priming -- when orthographic properties do not unambiguously indicate language identity. This hypothesis provides a fairly straightforward account for the results of the same-script experiment (Experiment 2), in which both L1 Hindi and L2 English primes were presented in the Roman alphabet and thus did not provide a reliable cue to language identity. Consistent with this hypothesis, this experiment revealed the clearest indications of translation priming, as evidenced by priming in both the L1-L2 and L2-L1 directions. The results of the cross-script experiment, however, appear to run contrary to the predictions of this hypothesis. First, translation priming was obtained in the L1-L2 direction even though the L1 Hindi primes were presented in Devanagari. Under the inhibition hypothesis, these words should not have been effective translation primes because their language-specific script properties should have allowed for efficient inhibition of related L2 lexical representations. Moreover, translation priming was not obtained in the L2-L1 direction even though the script of the L2 English primes did not clearly indicate language identity. These ambiguous primes, whose script could correspond to either Hindi or English, should have been effective translation primes, but they were not. In light of these issues, one might consider other explanations for the results of the cross-script experiment that maintain the general principles of the inhibition hypothesis. For instance, with regard to priming in the L1-L2 direction, one might appeal to the idea that L1 words can be automatically interpreted at the semantic level regardless of the specific conditions

41

or tasks in the experiment (Wang & Forster, 2015; Witzel & Forster, 2013). In the present study, this would mean that despite the generally faster response times for L2 English targets, words in L1 Hindi were processed more completely, allowing them to act as effective translation primes regardless of their script properties. That is, the processing of L1 Hindi words was automatic enough such that it might have effectively overcome any script-based inhibition of L2 lexical representations. This explanation seems plausible in light of the many studies that have reported strong L1-L2 priming across tasks and across L2 proficiency levels. With regard to the priming patterns in the L2-L1 direction, it could be the case that the orthography of the Hindi and English targets provided cues as to what information to expect from the prime. Again, in the cross-script experiment, all of the L1 Hindi targets were presented in Devanagari, while all of the L2 English targets were presented in the Roman alphabet. This might have given rise to the expectation that Roman alphabet letter strings can only correspond to English words. This expectation might then have allowed for efficient lateral inhibition of Hindi words from L2 English primes. A similar expectation would not have been generated in the same-script experiment since both Hindi and English targets were presented in the Roman alphabet. One way to conceive of these expectations and their influence on the processing of L2 primes is in terms of task schemas. Under the BIA+ model, these schemas adjust the information that is considered by the decision system in order to optimize task performance (Casaponsa et al., 2014; Dijkstra & van Heuven, 2002). However, it is not clear how modulating inhibition from L2 primes would allow for more efficient lexical decisions in the present study. If anything, the tasks should have indicated (albeit below the level of conscious awareness) that the prime was

42

equally useful to the lexical decision process under the L1-L2 and L2-L1 translation priming conditions in both experiments. In sum, the results of this study pose a number of challenges for the orthographic cue hypothesis, the lexical space hypothesis, and the inhibition hypothesis. It is therefore important to consider other possible explanations for these findings and for the influence of orthography on bilingual lexical access more generally. Of course, any plausible alternative should account for the key finding of the present study -- that L2-L1 translation priming was obtained in the same-script experiment, but not in the cross-script experiment. This finding is particularly interesting in light of the fact that the L2 English primes were presented in exactly the same way in both experiments. The only thing that changed was the way in which the L1 Hindi targets were presented -- in Devanagari in the cross-script experiment, and in Romanagari in the same-script experiment. This suggests that script of the L1 Hindi targets affected the processing of the L2 English primes. One explanation for this finding might appeal to strategic cross-language inhibition. Specifically, in the cross-script experiment, the one-to-one correspondence between language and script for both L1 Hindi and L2 English targets (i.e., Hindi-Devanagari, English-Roman alphabet) might have led to the selective inhibition of words in the other language. Under such an account, L1-L2 priming would be obtained because the activation of L1 Hindi primes might overcome this inhibition such that they could act as effective primes for L2 English targets. (See above for a comparable explanation for L1-L2 priming under the inhibition hypothesis.) However, this across-the-board inhibition might have been sufficient to render L2 English primes ineffective when responding to L1 Hindi targets. The same strategic inhibition would not

43

apply in the same-script experiment, in which there was not a one-to-one correspondence between script and language. A more parsimonious account would explain these findings without positing the selective strategic inhibition of an entire set of lexical entries. An explanation along these lines might appeal to the idea that distinctive orthographic properties allow for facilitated language detection (e.g., Casaponsa et al., 2014; Vaid & Frenck-Mestre, 2002; van Kesteren et al., 2012). As discussed above, this idea figures prominently in the orthographic cue hypothesis and the inhibition hypothesis. Despite their clear differences, both of these hypotheses focus on how facilitated language detection influences the processing of the prime. Specifically, under the orthographic cue hypothesis, facilitated language detection makes the processing of the prime easier; whereas under the inhibition hypothesis, it allows for stronger inhibition from the prime to words in the other language. An alternative is that facilitated language detection affects the target by focusing processing on the language of this word. How might this influence translation priming? It is important to note that the processing of masked primes appears to continue even after the target is presented (Forster, 2009, 2013). With this in mind, it could be the case that targets with language-specific orthographic properties direct processing only or primarily to the language of the target. This would have the effect of discontinuing the processing of the prime in the other language, effectively nullifying its influence on access to the target. This type of account provides a relatively straightforward explanation for the L2-L1 translation priming results in the present study. In the cross-script experiment, the distinct orthographic properties of the Devanagari L1 Hindi targets would focus processing on the L1, negating the influence of information provided by the L2 English prime. In the same script experiment, however, the

44

orthographic properties of the Romanagari L1 Hindi targets do not provide a clear cue to language identity. The focus of processing would therefore not be directed only or primarily to L1 Hindi, allowing the processing of the L2 English prime to continue and to influence access to the L1 Hindi target. Another way to account for the influence of the L1 Hindi targets on the processing of the L2 English primes might appeal to language switch costs. A number of studies have observed processing costs when switching between orthographies. For instance, Shafiullah and Monsell (1999) found processing costs when switching between Japanese words written in mora-based Kana scripts and those written in logographic Kanji characters. However, a comparable cost was not observed when switching between the two Kana systems, Hiragana and Katakana. Shafiullah and Monsell (1999) argue that Kana and Kanji require the use of different processing mechanisms, which results in this switch cost. Similarly, for Hindi-English bilinguals, different processing mechanisms might be used when reading the Roman alphabet and the Devanagari alphasyllabary. It could therefore be the case that when the target and prime are presented in these very different scripts, the processing of the prime is disrupted. This disruption could again be sufficient to effectively cancel out the influence of the prime on the processing of the target. When the target and prime are presented in the same script, however, the processing of the prime would not be similarly disrupted. This would explain why there was L2-L1 priming in the same-script experiment, but not in the cross-script experiment. A relevant question related to these alternatives is why discontinuation or disruption of the processing of the prime did not cancel out translation priming in the L1-L2 direction as well. Recall that this effect was observed in both the cross-script experiment and the same-script

45

experiment -- i.e., regardless of whether the L1 prime and L2 target were presented in different scripts or in the same script. Again, this might be due to the automaticity with which L1 words are processed at the semantic level. As discussed above, this seems plausible in light of findings indicating robust L1-L2 translation priming across tasks and across L2 proficiency levels. Although the findings of the present study do not clearly suggest that one of these alternative accounts is superior to the others, it is important to note that all of these accounts suggest separate lexical systems for bilinguals' two languages. This is particularly the case for the language detection account, which posits that facilitated language identification directs processing focus to the language of the target and (largely) discontinues the processing of L2 primes. Such an account would be difficult to accommodate under lexico-semantic organizations in which bilinguals' two languages are integrated. Finally, it is important to consider other accounts that have been put forward to explain inconsistent L2-L1 priming. Recall that as part of the orthographic cue hypothesis, Gollan et al. (1997) attributed the lack of L2-L1 priming to the idea that L2 primes are not processed quickly enough to influence the processing of L1 targets (but see Jiang, 1999; Wang & Forster, 2015). Similarly, BIA models hold that L2-L1 priming is weak and inconsistent because L2 primes are not activated strongly or quickly enough to subsequently activate semantically-related words. However, this processing/activation speed argument does not appear to apply in the present study for two reasons. First, in both experiments, L2 targets were responded to faster than L1 targets, suggesting that the Hindi-English bilinguals in this study were as proficient in L2 English as in L1 Hindi -- and possibly more proficient in their L2, at least in terms of low-level reading skills. Furthermore, such an account would have difficulty explaining how L2 English primes were

46

processed quickly enough to reveal L2-L1 priming in the same-script experiment, but not quickly enough to reveal this effect in the cross-script experiment. Other accounts for weak and inconsistent L2-L1 priming appeal to representational differences between L1 and L2 words. For instance, the episodic L2 hypothesis (Jiang & Forster, 2001; Witzel & Forster, 2013) posits that while L1 words are stored in lexical memory, L2 words are stored in episodic memory. This representational difference explains why bilinguals who do not show L2-L1 priming in lexical decision nevertheless show priming in this direction in episodic recognition. That is, when the decision system is tuned to feedback from episodic memory (as in episodic recognition tasks), the representation of an L2 prime can be easily located and can thus activate semantically-related words. When the decision system is tuned to feedback from lexical memory (as in lexical decision tasks), however, the search for the representation of the L2 prime fails, resulting in no L2-L1 priming. Interestingly, for the Hindi-English bilinguals in the present study, L2 primes were effective in the same-script experiment, but not in the cross-script experiment, but both experiments used the same lexical decision task. This suggests that the failure to find L2-L1 priming in the cross-script experiment is unlikely due to representational differences between L1 and L2 words. In conclusion, the results of the present study indicated that in highly-proficient early Hindi-English bilinguals, cross-language priming was influenced by whether L1 and L2 words were presented in the same script or in different scripts. In light of the fact the bilinguals' languages are often associated with very different orthographies, more research is necessary in order to examine the influence of script differences on bilingual lexical access and on bilingual

47

language processing more generally. Moreover, it is important to explore how bilinguals deal with multiple scripts in the L2, and whether this also affects masked translation priming.

48

REFERENCES Annamalai, E. (2004). Medium of power: The question of English in education in India. In J. W. Tollefson & A. B. M. Tsui (Eds.), ​Medium of instruction policies. Which agenda? Whose agenda? ​(pp. 177-194). Mahwah, NJ: Lawrence Erlbaum Associates. Baayen, R. H., Piepenbrock, R., & van Rijn, H. (1995). The CELEX database. ​Nijmegen: Center for Lexical Information, Max Planck Institute for Psycholinguistics, CD-ROM​. Barman, U., Das, A., Wagner, J., & Foster, J. (2014). Code mixing: A challenge for language identification in the language of social media. In ​Proceedings of the first workshop on computational approaches to code switching (pp. 13–23). ​Doha, Qatar: Association for Computational Linguistics. Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. ​Journal of Memory and Language, 68​, 255–278. Basnight-Brown, D. M., & Altarriba, J. (2007). Differences in semantic and translation priming across languages: The role of language direction and language dominance. ​Memory & Cognition​, ​35​, 953-965. Bowers, J. S., & Michita, Y. (1998). An investigation into the structure and acquisition of orthographic knowledge: Evidence from cross-script Kanji-Hiragana priming. Psychonomic Bulletin & Review, 5, ​259-264. Bowers, J. S., Vigliocco, G., & Haan, R. (1998). Orthographic, phonological and articulatory contributions to masked letter and word priming. ​Journal of Experimental Psychology: Human Perception and Performance, 24, ​1705-1709.

49

Bright, W. (1996). The Devanagari script. In P. Daniels & W. Bright (Eds.), ​The world’s writing systems ​(pp. 384-390). New York: Oxford University Press. Casaponsa, A., Carreiras, M., & Duñabeitia, J. A. (2014). Discriminating languages in bilingual contexts: The impact of orthographic markedness. ​Frontiers in Psychology, 5, ​424. Casaponsa, A., Carreiras, M., & Duñabeitia, J. A. (2015). How do bilinguals identify the language of the words they read? ​Brain Research, 1624, ​153-166. Casaponsa, A., & Duñabeitia, J. A. (2016). Lexical organization of language-ambiguous and language-specific words in bilinguals. ​The Quarterly Journal of Experimental Psychology, 69, ​589-604. Chen, B., Liang, L., Cui, P., & Dunlap, S. (2014). The priming effect of translation equivalents across languages for concrete and abstract words. ​Acta Psychologica, 153, ​147-152. Coltheart, M., Davelaar, E., Jonasson, J. T., & Besner, D. (1977). Access to the internal lexicon. In S. Dornic (Ed.), ​Attention and performance VI​ (pp. 535-555). Hillsdale, NJ: Erlbaum. Cousineau, D. (2005). Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson’s method. ​Tutorials in Quantitative Methods for Psychology​, ​1​, 42-45. Davis, C. (2005). N-Watch: A program for deriving neighborhood size and other psycholinguistic statistics. ​Behavior Research Methods, 37, ​65-70. Davis, C., Sánchez-Casas, R., García-Albea, J. E., Guasch, M., Molero, M., & Ferré, P. (2009). Masked translation priming: Varying language experience and word type with Spanish-English bilinguals. ​Bilingualism: Language and Cognition, 13, ​137-155.

50

Dijkstra, T., & van Heuven, W. J. (2002). The architecture of the bilingual word recognition system: From identification to decision. ​Bilingualism: Language and Cognition​, ​5​, 175-197. Dimitropoulou, M., Duñabeitia, J. A., & Carreiras, M. (2011a). Masked translation priming effects with low proficient bilinguals. ​Memory and Cognition, 39, ​260-275. Dimitropoulou, M., Duñabeitia, J. A., & Carreiras, M. (2011b). Two words, one meaning: Evidence for automatic co-activation of translation equivalents. ​Frontiers in Psychology, 2, 188. Dimitropoulou, M., Duñabeitia, J. A., & Carreiras, M. (2011c). Transliteration and transcription effects in biscriptal readers: The case of Greeklish. ​Psychonomic Bulletin & Review, 18, 729-735. Duñabeitia, J. A., Perea, M., & Carreiras, M. (2010). Masked translation priming effects with highly proficient simultaneous bilinguals. ​Experimental Psychology, 57, ​98-107. Duyck, W., & Warlop, N. (2009). Translation priming between the native language and a second language: New evidence from Dutch-French bilinguals. ​Experimental Psychology​, ​56​, 173-179. EMILLE/CIIL (2004). ​Corpus of South Asian Languages. ​European Language Resources Association. Retrieved from​ ​http://catalog.elra.info/product_info.php?products_id=696 Ferré, P., Sánchez-Casas, R., Comesaña, M., & Demstre, J. (in press). Masked translation priming with cognates and noncognates: Is there an effect of words’ concreteness? Bilingualism: Language and Cognition.

51

Finkbeiner, M., Forster, K., Nicol, J., & Nakamura, K. (2004). The role of polysemy in masked semantic and translation priming. ​Journal of Memory and Language​, ​51​, 1-22. Forster, K. I. (2009). The intervenor effect in masked priming: How does masked priming survive across an intervening word? ​Journal of Memory and Language​, ​60​, 36-49. Forster, K. I. (2013). How many words can we read at once? More intervenor effects in masked priming. ​Journal of Memory and Language, 69, ​563-573. Forster, K. I., & Forster, J. C. (2003). DMDX: A Windows display program with millisecond accuracy. ​Behavior Research Methods, Instruments, & Computers​, ​35​, 116-124. Gella, S., Bali, K., & Choudhury, M. (2014). “ye word kis lang ka hai bhai?” Testing the limits of word level language identification. In D. S. Sharma, R. Sangal, & J. D. Pawar (Eds.), Proceedings of the eleventh international conference on natural language processing​ (pp. 368–377). Goa, India: NLPAI. Gollan, T. H., Forster, K. I., & Frost, R. (1997). Translation priming with different scripts: Masked priming with cognates and noncognates in Hebrew-English bilinguals. ​Journal of Experimental Psychology: Learning, Memory, and Cognition​, ​23​, 1122-1139. Gor, K., Chrabaszcz, A., & Cook, S. (2017). Processing of native and nonnative inflected words: Beyond affix stripping. ​Journal of Memory and Language​, ​93​, 315-332. Grainger, J., & Beauvillain, C. (1987). Language blocking and lexical access in bilinguals. Quarterly Journal of Experimental Psychology: Human Experimental Psychology, 39​A, 295-319.

52

Grainger, J., & Dijkstra, T. (1992). On the representation and use of language information in bilinguals. In R. J. Harris (Ed.), ​Cognitive processing in bilinguals ​(pp. 207-220). Amsterdam: Elsevier Science Publishers. Grainger, J., & Frenck-Mestre, C. (1998). Masked priming by translation equivalents in proficient bilinguals. ​Language and Cognitive Processes, 13, ​601-623. Hino, Y., Lupker, S. J., Ogawa, T., & Sears, C. R. (2003). Masked repetition priming and word frequency effects across different types of Japanese scripts: An examination of the lexical activation account. ​Journal of Memory and Language, 48, ​33-66. Hoshino, N., Midgley, K. J., Holcomb, P. J., & Grainger, J. (2010). An ERP investigation of masked cross-script translation priming. ​Brain Research, 1344, ​159-172. Jiang, N. (1999). Testing processing explanations for the asymmetry in masked cross-language priming. ​Bilingualism: Language and Cognition​, ​2​, 59-75. Jiang, N., & Forster, K. I. (2001). Cross-language priming asymmetries in lexical decision and episodic recognition. ​Journal of Memory and Language​, ​44​, 32-51. Kim, J., & Davis, C. (2003). Task effects in masked cross-script translation and phonological priming. ​Journal of Memory and Language, 49, ​484-499. Kinoshita, S., & Kaplan, L. (2008). Priming of abstract letter identities in the letter match task. The Quarterly Journal of Experimental Psychology, 61, ​1873-1885. Kouider, S., & Dupoux, E. (2001). A functional disconnection between spoken and visual word recognition: Evidence from unconscious priming. ​Cognition​, ​82​(1), B35-B49. Lee, J. (2016). ​Plausibility and structural reanalysis in online L1 and L2 sentence processing (Unpublished doctoral dissertation). University of Texas at Arlington, Arlington, TX.

53

Lemhöfer, K., & Dijkstra, T. (2004). Recognizing cognates and interlingual homographs: Effects of code similarity in language-specific and generalized lexical decision. ​Memory & Cognition​, ​32​(4), 533-550. Lemhöfer, K., & Radach, R. (2009). Task context effects in bilingual nonword processing. Experimental Psychology​, ​56​(1), 41-47. Nakamura, K. Dehaene, S., Jobert, A., Le Bihan, D., & Kouider, S. (2005). Subliminal convergence of kanji and kana words: Further evidence for functional parcellation of the posterior temporal cortex in visual word perception. ​Journal of Cognitive Neuroscience, 17, ​954-968. Nakayama, M., Ida, K., & Lupker, S. J. (2016). Cross-script L2-L1 noncognate translation priming in lexical decision depends on L2 proficiency: Evidence from Japanese-English bilinguals. ​Bilingualism: Language and Cognition, 19, ​1001-1022​. Nakayama, M., Lupker, S. J., & Itaguchi, Y. (in press). An examination of L2-L1 noncognate translation priming in the lexical decision task: Insights from distributional and frequency-based analyses. ​Bilingualism: Language and Cognition. Nakayama, M., Sears, C. R., Hino, Y., & Lupker, S. J. (2013). Masked translation priming with Japanese-English bilinguals: Interactions between cognate status, target frequency and L2 proficiency. ​Journal of Cognitive Psychology, 2, ​949-981. Oganian, Y., Conrad, M., Aryani, A., Heekeren, H. R., & Spalek, K. (2016). Interplay of bigram frequency and orthographic neighborhood statistics in language membership decision. Bilingualism: Language & Cognition, 19, ​578-596.

54

Okano, K., Grainger, J., & Holcomb, P. J. (2013). An ERP investigation of visual word recognition in syllabary scripts. ​Cognitive, Affective, & Behavioral Neuroscience, 13, 390-404. Pauszek, J. R., Sztybel, P., & Gibson, B. S. (in press). Evaluating Amazon’s Mechanical Turk for psychological research on the symbolic control of attention. ​Behavior Research Methods​. Pollatsek, A., & Well, A. D. (1995). On the use of counterbalanced designs in cognitive research: A suggestion for a better and more powerful analysis. ​Journal of Experimental Psychology: Learning, Memory, and Cognition​, ​21​, 785-794. Price, I. K., & Witzel, J. (2017). Sources of relative clause processing difficulty: Evidence from Russian. ​Journal of Memory and Language, 97​, 208-244. Price, I. K., Witzel, N., & Witzel, J. (2015). Orthographic and phonological form interference during silent reading. ​Journal of Experimental Psychology: Learning, Memory, and Cognition​, ​41​, 1628-1647. Rao, C., Mathur, A., & Singh, N. C. (2013). ‘Cost in transliteration’: The neurocognitive processing of Romanized writing. ​Brain and Language, 124, ​205-212. Rastle, K., Harrington, J., & Coltheart, M. (2002). 358,534 nonwords: The ARC Nonword Database. ​Quarterly Journal of Experimental Psychology, 55A​, 1339-1362. Romanova, N., & Gor, K. (2017). Processing of gender and number agreement in Russian as a second language: The devil is in the details. ​Studies in Second Language Acquisition, 39, 97-128.

55

Sabourin, L., Brien, C., & Burkholder, M. (2014). The effect of age of L2 acquisition on the organization of the bilingual lexicon: Evidence from masked priming. ​Bilingualism: Language and Cognition, 17, ​542-555. Sánchez-Casas, R. M., Davis, C. W., & García-Albea, J. E. (1992). Bilingual lexical processing: Exploring the cognate/non-cognate distinction. ​European Journal of Cognitive Psychology, 4, ​293-310. Schoonbaert, S., Duyck, W., Brysbaert, M., & Hartsuiker, R. J. (2009). Semantic and translation priming from a first language to a second and back: Making sense of the findings. ​Memory and Cognition, 37, ​569-586. Shafiullah, M., & Monsell, S. (1999). The cost of switching between kanji and kana while reading Japanese. ​Language and Cognitive Processes, 14, ​567-607. Vaid, J., & Frenck-Mestre, C. (2002). Do orthographic cues aid language recognition? A laterality study with French-English bilinguals. ​Brain and Language, 82, ​47-53. Vaid, J., & Gupta, A. (2002). Exploring word recognition in a semi-alphabetic script: The case of Devanagari. ​Brain and Language, 81, ​679-690. Van Heuven, W. J., Dijkstra, T., & Grainger, J. (1998). Orthographic neighborhood effects in bilingual word recognition. ​Journal of Memory and Language​, ​39​, 458-483. Van Heuven, W. J. B., & Coderre, E. L. (2015). Orthographic processing in bilinguals. In J. W. Schwieter (Ed.), ​Handbook of bilingual processing ​(pp. 308-326). Cambridge: Cambridge University Press.

56

Van Kesteren, R., Dijkstra, T., & de Smedt, K. (2012). Markedness effects in Norwegian-English bilinguals: Task-dependent use of language-specific letters and bigrams. ​The Quarterly Journal of Experimental Psychology, 65, ​2129-2154. Voga, M., & Grainger, J. (2007). Cognate status and cross-script translation priming. ​Memory and Cognition, 35, ​938-952. Wang, X. (2013). Language dominance in translation priming: Evidence from balanced and unbalanced Chinese-English bilinguals. ​The Quarterly Journal of Experimental Psychology​, ​66​, 727-743. Wang, X., & Forster, K. I. (2010). Masked translation priming with semantic categorization: Testing the Sense Model. ​Bilingualism: Language and Cognition​, ​13​, 327-340. Wang, X., & Forster, K. (2015). Is translation priming asymmetry due to partial awareness of the prime? ​Bilingualism: Language and Cognition, 18, ​657-669. Wen, Y., & van Heuven, W. J. B. (2017). Non-cognate translation priming in masked priming lexical decision experiments: A meta-analysis. ​Psychonomic Bulletin & Review, 24​, 879-886. Witzel, J., Cornelius, S., Witzel, N., Forster, K. I., & Forster, J. C. (2013). Testing the viability of webDMDX for masked priming experiments. ​The Mental Lexicon​, ​8​, 421-449. Witzel, N. O., & Forster, K. I. (2012). How L2 words are stored: The episodic L2 hypothesis. Journal of Experimental Psychology: Learning, Memory, and Cognition​, ​38​, 1608-1621. Woods, A. T., Velasco, C., Levitan, C. A., Wan, X., & Spence, C. (2015). Conducting perception research over the internet: A tutorial review. ​PeerJ​, ​3​, e1058.

Table 1. Mean Reaction Times (RTs) in Milliseconds and Percentage Error Rates (ERs) for Each Priming Condition, with Standard Errors of the Mean for Repeated Measures (Cousineau, 2005) in Parentheses, Experiment 1

Translation Priming L1-L2 RT

Repetition Priming L2-L1

L1-L1

L2-L2

ER

RT

ER

RT

ER

RT

ER

Related

568 (9)

2.3 (0.7)

681 (12)

2.9 (0.7)

663 (11)

6.4 (1.0)

573 (11)

4.7 (0.9)

Unrelated

589 (11)

2.5 (0.6)

680 (11)

3.5 (0.8)

705 (13)

7.6 (1.0)

602 (11)

6.3 (1.1)

Priming

21** *** p < .001

0.2 ** p < .01

-1 * p < .05

0.6

42***

1.2

29**

1.6

Table 2. Mean Reaction Times (RTs) in Milliseconds and Percentage Error Rates (ERs) for Each Priming Condition, with Standard Errors of the Mean for Repeated Measures in Parentheses, Experiment 2

Translation Priming L1-L2 RT

Repetition Priming L2-L1

ER

RT

L1-L1 ER

RT

L2-L2 ER

RT

ER

Related

514 (6)

7.0 (1.2)

649 (9)

7.8 (1.0)

597 (7)

5.9 (1.1)

485 (8)

2.7 (0.7)

Unrelated

551 (9)

5.7 (1.0)

673 (11)

11.9 (1.3)

634 (6)

7.8 (0.9)

537 (8)

8.0 (1.2)

Priming

37** *** p < .001

-1.3 ** p < .01

24*

4.1^

* p < .05

^ p = .077

37***

1.9

52***

5.3**

Supplemental Material A for

Script differences and masked translation priming: Evidence from Hindi-English bilinguals Namrata Dubey, Naoko Witzel, & Jeffrey Witzel

L1-L2 Related Prime Unrelated Prime Target LEAF पत्ता खज़ाना APPLE सेब राख CLOUD बादल पीठ MOON चााँद थैला BUTTER मक्खन चू ड़ी SCISSORS कैंची खून DOOR दरवाज़ा शरीर BLUE नीला पुल NEIGHBOUR पड़ोसी झाड़ू BONE हड्डी बाल्टी TABLE मेज़ इमारत CHAIR कुसी काजू RIVER नदी गोभी POTATO आलू शहर STOMACH पेट घड़ी FRUIT फल कपड़ा WHITE सफ़ेद खज़ाना VILLAGE गााँ व राख DAUGHTER बे टी पीठ COUNTRY दे श थैला HEAD सर चू ड़ी BLACK काला खून PLANT पौधा शरीर PAPER कागज़ पुल GREEN हरा झाड़ू BANANA केला बाल्टी SHOES जू ते इमारत LEMON नीींबू काजू WEATHER मौसम गोभी CAMEL ऊाँट शहर RED लाल घड़ी MORNING सुबह कपड़ा

L2-L1 Related Prime yellow eye vegetable book needle window milk chili bed ear curtain sister language kitchen spoon brick toy hammer brother queen thread tree finger month salt nose story word father mango carrot tail

Unrelated Prime wall lecture laughter photo rain tongue notebook ink religion salary stool stamp circle member wind lock wall lecture laughter photo rain tongue notebook ink religion salary stool stamp circle member wind lock

Target पीला आाँ ख सब्जी ककताब सुई खखड़् की दू ध कमची कबस्तर कान पदाा बहन भाषा रसोई चम्मच ईींट खखलौना हथौड़ा भाई रानी धागा पेड़ उाँ गली महीना नमक नाक कहानी शब्द कपता आम गाजर पूाँछ

L1-L1 Related Prime Unrelated Prime Target कींघी कींघी मोर रुई रुई तस्वीर कौवा कौवा तककया खीरा खीरा प्रश्न खजूर खजू र मूली कदन कदन सड़क सपना सपना पत्थर ज़मीन ज़मीन छत चुनाव चु नाव रस्सी दु श्मन दु श्मन रे त चेहरा चे हरा कवध्यालय पींख पींख वाक्य आग आग कमीज़ दोस्त दोस्त दु कान खेल खेल सााँ प लड़की लड़की कचकड़या बकरी बकरी मोर बाल बाल तस्वीर घींटा घीं टा तककया ग्रह ग्रह प्रश्न चाकू चाकू मूली वकील वकील सड़क पैर पैर पत्थर शेर शे र छत आदमी आदमी रस्सी पैसा पैसा रे त पहाड़ पहाड़ कवध्यालय नाखून नाखून वाक्य हार हार कमीज़ प्याज़ प्याज़ दु कान तोता तोता सााँ प मरीज़ मरीज़ कचकड़या

L2-L2 Related Prime air revolver oxygen map grass eraser student tissue laser powder canvas committee farmer symbol fish caption cash rice dot egg earphones material tooth label glass minister hostel music award minute family soap

Unrelated Prime war poetry firewood fan flower square page telephone bench party object mirror tub pen visitor product war poetry firewood fan flower square page telephone bench party object mirror tub pen visitor product

Target AIR REVOLVER OXYGEN MAP GRASS ERASER STUDENT TISSUE LASER POWDER CANVAS COMMITTEE FARMER SYMBOL FISH CAPTION CASH RICE DOT EGG EARPHONES MATERIAL TOOTH LABEL GLASS MINISTER HOSTEL MUSIC AWARD MINUTE FAMILY SOAP

Supplemental Material B for

Script differences and masked translation priming: Evidence from Hindi-English bilinguals Namrata Dubey, Naoko Witzel, & Jeffrey Witzel

L1-L2 Related Prime sabzi sui doodh bhasha chammach khilona hathoda rani dhaga mahina kahani shabd pita poonch mirchi kaan parda rasoi bhai ungli saal naak aam namak kutta billi andhera chawal chai kapda ullu khargosh

Unrelated Prime mukut chumbak patang matar dahi ghadi gussa gulabi prithvi chunav grah mareez sona tamatar kitaab tukda mukut chumbak patang matar dahi ghadi gussa gulabi prithvi chunav grah mareez sona tamatar kitaab tukda

Target VEGETABLE NEEDLE MILK LANGUAGE SPOON TOY HAMMER QUEEN THREAD MONTH STORY WORD FATHER TAIL CHILI EAR CURTAIN KITCHEN BROTHER FINGER YEAR NOSE MANGO SALT DOG CAT DARKNESS RICE TEA CLOTH OWL RABBIT

L2-L1 Related Prime leaf apple door blue neighbor bone chair white daughter country banana weather yellow eyes window river potato plant green shoes red treasure ash blood body bridge cashew cabbage bed gun egg tooth

Unrelated Prime wall lecture laughter photo rain tongue notebook city religion salary stool stamp circle member wind lock wall lecture laughter photo rain tongue notebook city religion salary stool stamp circle member wind lock

Target PATTA SEB DARWAZA NEELA PADOSI HADDI KURSI SAFED BETI DESH KELA MAUSAM PEELA AANKH KHIDKI NADI AALOO PAUDHA HARA JOOTE LAAL KHAZANA RAAKH KHOON SHAREER PUL KAAJU GOBHI BISTAR BANDOOK ANDA DAANT

L1-L1 Related Prime kanghi rui kheera din sapna zameen dushman chehra pankh aag dost khel ladki bakri baal ghanta sher aadmi paisa haar tota tasveer takiya sadak rassi kameez dukaan saanp chidiya angoor ghoda titli

Unrelated Prime gudiya chaabi ghosla thanda chaar ghaas pahaad panna ameer saat bees koyla sitara log dil haathi gudiya chaabi ghosla thanda chaar ghaas pahaad panna ameer saat bees koyla sitara log dil haathi

Target KANGHI RUI KHEERA DIN SAPNA ZAMEEN DUSHMAN CHEHRA PANKH AAG DOST KHEL LADKI BAKRI BAAL GHANTA SHER AADMI PAISA HAAR TOTA TASVEER TAKIYA SADAK RASSI KAMEEZ DUKAAN SAANP CHIDIYA ANGOOR GHODA TITLI

L2-L2 Related Prime air apron oxygen map eraser student tissue laser powder canvas committee symbol caption cash dot earphones material label glass minister hostel music award minute family cow bear boat belt orange camera lion

Unrelated Prime lemon button frog cactus helicopter reindeer ink rattle ambulance acorn avocado bowl beans clown ostrich ruler lemon button frog cactus helicopter reindeer ink rattle ambulance acorn avocado bowl beans clown ostrich ruler

Target AIR APRON OXYGEN MAP ERASER STUDENT TISSUE LASER POWDER CANVAS COMMITTEE SYMBOL CAPTION CASH DOT EARPHONES MATERIAL LABEL GLASS MINISTER HOSTEL MUSIC AWARD MINUTE FAMILY COW BEAR BOAT BELT ORANGE CAMERA LION