The Effect of the Non-task Language When Trilingual People Use Two Languages in a Language Switching Experiment

Chen, Jianlin; Liu, Hong

doi:10.3389/fpsyg.2020.00754

ORIGINAL RESEARCH article

Front. Psychol., 30 April 2020

Sec. Psychology of Language

Volume 11 - 2020 | https://doi.org/10.3389/fpsyg.2020.00754

This article is part of the Research Topic Behavioral and Neurophysiological Approaches to Code-Switching and Language Switching View all 19 articles

The Effect of the Non-task Language When Trilingual People Use Two Languages in a Language Switching Experiment

$\r\nJianlin Chen$ Jianlin Chen

Hong Liu^*

Department of English, School of Foreign Languages and Literatures, Lanzhou University, Lanzhou, China

This study investigated the effect of non-task language in a language switching experiment. Non-task language refers to participants’ languages (regardless of proficiency level) that are not used in any trials throughout the experiment. We recruited 60 Tibetan-Chinese-English trilinguals (12th-grade high school students with a median age of 17) to perform a lexical decision (word vs. non-word) task in only two of their languages. We repeated the experiment three times to present each language pair once. In each experiment, the participants were divided into two groups that significantly contrasted with each other in their non-task language while remaining comparable in the two task languages. Response time (RT) and error rate (ER) have been examined to evaluate task performance. The interaction between task performance and the participants’ proficiency in the non-task language was also examined. The results showed anull effect of language switching. In addition, the effect of the non-task language was not found. These results were interpreted with reference to the main models of bilingual visual word recognition and the role of orthography specificity.

Introduction

In this study, we investigated how Tibetan-Chinese-English trilinguals process their languages in terms of the word recognition process. Specially, we examined the effect of the non-task language, the language that is not explicitly activated for the task purpose, on a task performance involving the other two languages in a language switching experiment. By doing so, we have attempted to incorporate the processing of the non-task language and the role of orthography specificity in the existing models of bilingual word recognition, i.e., BIA+ (Dijkstra and van Heuven, 2002) and its modification BIA + s Casaponsa et al., 2019).

Current Models on Bilingual Lexical Processing

When bilinguals are visually presented with a word, the non-selective assumption (Hermans et al., 1998; van Heuven et al., 1998; De Groot et al., 2000; Dijkstra and van Heuven, 2002; Kroll and Dijkstra, 2002; De Bot et al., 2007; Lemhöfer et al., 2008; Dijkstra et al., 2019) argues that candidates from both of their languages (including the non-target one) are activated, a phenomenon also termed “parallel activation” (Green, 1998; Costa et al., 1999; Finkbeiner et al., 2006; Philipp and Koch, 2009; Kroll et al., 2013). The non-selective activation assumption has also been argued for in the domain of language production (Dijkstra et al., 1999; von Studnitz and Green, 2002). Activation from the semantic system is spread to the lexical level, and several lexical representations are activated. The lexical selection mechanism for production is not only sensitive to the target word but also sensitive to the activation level of other non-target—but activated—words. In the meantime, some studies have extended this non-selective hypothesis in bilingual studies to trilinguals (Lemhöfer et al., 2004; Szubko-Sitarek, 2011; Kroll et al., 2013), demonstrating that when trilinguals perform a task in one language, and the other two languages are also activated.

However, the parallel activation of two or more languages very rarely causes a performance error (Gollan et al., 2011). How is a word in the target language is correctly retrieved, processed, and comprehended/produced? How are other words from the non-target languages(s) processed so as not to cause performance interference? A few models have been proposed. A major model that accounts for bilingual visual word processing is bilingual interactive activation (BIA) (Grainger and Dijkstra, 1992; Dijkstra and van Heuven, 1998) and its successor BIA+ (Dijkstra and van Heuven, 2002).

BIA and BIA+ assume parallel/non-selective bottom-up activation from letters to language. Orthographic features of the input word will activate similar letter strings from both target and non-target languages. The activated candidates will next activate their corresponding phonological and semantic representations. Identifying the input word results in the subsequent retrieval of the language membership information for that word, i.e., a language node. The inhibition of active lexical candidates is applied top-down via a language node (which reflects global lexical activity of the target language) to lexical items from the non-target language (BIA) or via adaptation of decision criteria (BIA+). However, these two models do not accommodate well the sub-lexical information, such as orthographical-specific (marked) features, which are shown to be a significant factor in visual word recognition (Grainger and Beauvillain, 1987; Thomas and Allport, 2000; Orfanidou and Sumner, 2005; Casaponsa and Duñabeitia, 2016). Some subsequent modifications of the models have been proposed, such as the BIA + extended model (BIA + d, van Kesteren et al., 2012), which adds a pre-lexical processing stage of language-specific feature-level information to speed up language attribution, and the BIA + s model (Casaponsa et al., 2019), which adds separate orthographic and phonological sub-lexical language nodes to account for language-selective effects emerging within an integrated lexicon.

With respect to bilingual lexical production, an influential model following the non-selective assumption is the Inhibition Control model (IC; Green, 1998), which argues that lexical access in bilingual speakers entails the reactive top-down inhibition of lexical items belonging to the non-target language. The asymmetrical switching cost reported by Meuter and Allport (1999) is considered to be supporting the notion that lexical access entails inhibitory processes. Another reference framework that has been frequently cited to explain empirical results in bilingual word production is the Revised Hierarchical Model (RHM; Kroll and Stewart, 1994; Kroll and Tokowicz, 2001, 2005). According to the RHM, there are shared semantics among a bilingual’s different languages, but the route to access the semantic information is different for different languages. For less proficient bilinguals, L2 is connected to concepts through prior activation of an L1 translation equivalent. Increased proficiency in L2 can strengthen the link between L2 representations and semantics, up to a certain point, and a direct link is established.

More recently, another model has been proposed to integrate bilingual visual word recognition and word translation, the Multilink Model (Dijkstra et al., 2019). Similar to the previous models, this computational model assumes language non-selective access and parallel activation of word-form neighbors. In this interactive model, an input word activates similar orthographic representations, which feed activation to their semantic and phonological counterparts, and associated language membership representations. However, no lateral inhibitory effects, either between languages or within languages, are assumed.

The models reviewed thus far, both verbal and implemented, are based on languages that share orthographical features or scripts, such as English and Dutch, or English and Spanish. The non-selective assumption is also based on the orthographic similarity between the input word and lexical candidates from both languages. However, it has already been confirmed that language-specific/marked orthographic features can speed up language attribution by reducing the number of candidates from the non-target language (van Kesteren et al., 2012; Casaponsa et al., 2019). More specifically, in Dijkstra and van Heuven (2002), it is posited that language-specific access is possible with language pairs that do not share orthography at all (e.g., Chinese and English). To successfully capture the mechanism underlying bilingual lexical processing, a comprehensive model should be able to generalize the lexical access and processing of bi-scriptal bilinguals and specify how words from orthographically distinct languages are retrieved and processed.

Language Switching Paradigm

The models of bilingual language processing reviewed in the above section assume the role of inhibition in the control process; how inhibition is implemented and at what stage it is involved, however, vary across different models. A dominant approach to investigate the involvement of inhibition in language processing is the language switching paradigm. It is argued that switching between languages is more costly than staying in the same language [manifested as longer response time (RT) and more performance errors], and switching into the more dominant language is even more costly (Meuter and Allport, 1999; Costa and Santesteban, 2004; Costa et al., 2006). This asymmetrical pattern of switching cost, as mentioned before, has been considered the main support for the IC model and has received much empirical support in the literature, especially related to production-based switch costs (Meuter and Allport, 1999; Jackson et al., 2001; Costa and Santesteban, 2004; Philipp et al., 2007; Declerck et al., 2012; Macizo et al., 2012; Filippi et al., 2014; Slevc et al., 2016).

However, not all studies on bilingual language processing have found a switch cost or asymmetric switch cost pattern. For example, asymmetry is not always observed in production-based switch cost experiments (Hernandez and Kohnert, 1999; Costa and Santesteban, 2004; Costa et al., 2006). In the domain of language comprehension, the picture is more complex. Some studies have replicated the findings of asymmetric switch costs (e.g., Olson, 2016; Declerck and Grainger, 2017). Some other studies have also found asymmetric switch costs, but the direction was reversed, i.e., larger cost in L2 than in L1 (Proverbio et al., 2004; Chauncey et al., 2008; Bultena et al., 2015). There are also studies that found symmetrical switch costs (e.g., Macizo et al., 2012; Hirsch et al., 2015; Philipp and Huestegge, 2015). Macizo et al. (2012) argued that competition between the two activated languages was required to trigger inhibition. Several recent comprehension-based studies even failed to find switch costs (Bultena et al., 2015; Declerck and Grainger, 2017; Declerck et al., 2019; Struys et al., 2019).

Thus far, the short review has shown that findings on the language processing mechanism in comprehension tasks are inconclusive, with evidence provided both for and against the involvement of inhibition. The inconsistent findings on switch costs in both production- and comprehension-based studies calls for more solid evidence for or against the involvement of inhibition in bilingual language processes, especially when it is shown that asymmetric switching costs can be accounted for by persisting activation of the weaker language rather than persisting inhibition of the dominant language (Philipp et al., 2007). There may be different types of inhibition (e.g., see Colzato et al., 2008 for active inhibition and local reactive inhibition; see Christoffels et al., 2007 for global inhibition and local inhibition; see Declerck, 2019 for a review on proactive language control), and switch costs may not be able to assess them all (Bobb and Wodniecka, 2013; Declerck, 2019). There may be certain conditions to be satisfied before inhibition is implemented. The differences between experiments with regard to the differences in the task nature and demands, stimuli composition, and participants’ expectancies can affect these conditions.

In addition, the studies in bilingual language control share one common feature in the design of their experiment, i.e., they examine how bilinguals/multilinguals control and process the languages that are explicitly activated for task performance. A question then arises: what is the status of non-task language(s)? Non-task language is different from non-target language. Non-target languages are not the target in the current trial but will be activated afterward in a different trial. In this sense, target and non-target language(s) are explicitly activated in alternation during the experiment. Thus, we may refer to them as task languages. Non-task languages are the languages that are known by the participant but not used at all throughout the experiment. For example, when a Chinese-English-Tibetan trilingual participates in a language switch experiment where switch costs between Chinese and English are examined, Tibetan is in this case the non-task language.

Some studies on neighborhood effects suggest that when subjects perform a task in a monolingual context, e.g., performing an English lexicon decision task, their non-task language is also activated and competes with target items (van Heuven et al., 1998; Midgley et al., 2008; van Kesteren et al., 2012). Such studies are based on languages that share scripts to some extent, such as English and Spanish or English and Dutch. Wu and Thierry (2010) showed that the co-activation of the non-task language can happen to languages that do not share scripts, such as English and Chinese. The results of their experiments suggest that processing in L2 can activate native language translations, but only at the phonological level. More importantly, the accessed phonology information of the non-task native language Chinese was shown to be facilitative when the participants were asked to judge the semantic relatedness of words in L2. This finding shows that the co-activated language is not necessarily inhibited and incurs cost in processing. Therefore, the current scholarship seems to suggest that the non-task language is activated at some level, but the evidence on how it interacts with the task performance is inconclusive.

Current Study

The primary focus of the present study is on the non-task language effects in language switching experiments where only two of the trilingual participants’ languages are explicitly activated for the task performance. We explore whether the non-task language will be processed in a manner that might affect the switching performance of the two task languages. The current scholarship on language processing mechanism informs us little on this question. However, knowing how the non-task language is processed not only provides further insights into the involvement of inhibition in language processing but also contributes to a more comprehensive theory on language control mechanism.

In addition, we have attempted to provide some insight into the effects of orthographic specificity in language switching and language processing in general. We conducted three experiments where trilinguals with three orthographically and phonologically different languages (i.e., Tibetan, Chinese, and English) performed a comprehension-based language switching task—a generalized lexical decision task using only two of their languages in each experiment. The participants were required to respond with “yes” to words (of either language) and with “no” to non-words (Dijkstra et al., 1998; van Heuven et al., 1998; Lemhöfer and Dijkstra, 2004). The design of the present experiments has been specified in the following sections.

According to the BIA + model or its modifications (Dijkstra and van Heuven, 2002; van Kesteren et al., 2012; Casaponsa et al., 2019), bilinguals’ languages should be simultaneously activated at the orthographical and/or phonological level, regardless of whether or not they are the target. Therefore, the non-target language not used in the current trial and the non-task language not explicitly used at all in the experiment should both be activated and exert some effects on the task performance. However, previous studies have shown that orthographic specificity can reduce switching effects (Grainger and Beauvillain, 1987; Orfanidou and Sumner, 2005; although see Thomas and Allport, 2000). By investigating whether and how the effects of orthographic markedness may emerge in a language switching experiment where both the non-target and the non-task language are examined, the present study made a tentative attempt to test whether and how the BIA + model or its modifications can be generalized to explain the experiment results involving orthographically and phonologically different languages.

Experiment 1

Methods

The participants performed a generalized lexical decision task, i.e., words or non-words, where they made yes/no response to the visual presentations of words and non-words from both languages (L1 and L2 in Experiment 1, L1 and L3 in Experiment 2, and L2 and L3 in Experiment 3). The letter strings or the characters activate orthographic, semantic, and phonological codes, which help the participants discriminate between word and non-word input. The order of the experiments was counterbalanced for participants. The experiments were conducted within the same day, however, with an interval of a few classes (including English, Chinese, and Tibetan classes).

Participants

The participants were recruited from one of the secondary schools reserved for Tibetan students in the northwest region of mainland China. The reason we chose high school students as our participants, instead of college students as other studies have been doing, was because it was much less likely that we could collect a sufficient number of comparable Tibetan-Chinese-English participants in other places than those reserved school for Tibetan students. The present study did not interfere with the participants’ classes. We collected data at the end of the semester and all participants gave written informed consent for data collection in accordance with the Declaration of Helsinki.

There are two types of curricula in those schools: the Tibetan-mediated curriculum (TMC) and the Chinese-mediated curriculum (CMC). Tibetan is the language of instruction in the TMC, and Chinese in the CMC. Sixty 12th-grade students [with a median age of 17 (SD = 0.8)], 30 from the TMC and 30 from the CMC, participated in the experiments. A language history questionnaire adopted from Chen (2018) was used to investigate the participants’ language-learning background. Their language proficiency was self-reported on a seven-point scale (1 = lowest proficiency, 7 = highest proficiency). The self-report was administered by their respective teachers and then subjected to their teachers’ confirmation of reliability. The results are shown in Table 1. Most of the students from both curriculum types began to learn Chinese as a second language in Grade 1 (about 6 years old, with an exposure of about 11 years or more), or even earlier, and English as a third language in Grade 3 (about 9 years old, with an exposure of about 8 years). Therefore, for these students, Tibetan is their L1, Chinese L2, and English their L3.

TABLE 1

Table 1. Language history information (mean and standard deviation) of the participants.

However, within each group, their relative proficiency in L1 and L2 differed. For the TMC students, they were most proficient in their L1 Tibetan, as indicated by the significant difference between L1 and L2, t = 4.52, p < 0.001, and between L1 and L3, t = 10.45, p < 0.001. Their L2 Chinese was also significantly more proficient than L3 English (t = 5.79, p < 0.001). As for the CMC students, there were significant differences between their L1 and L3 (t = 9.05, p < 0.001), and between L2 and L3 (t = 10.76, p < 0.001). However, there was no significant difference between their L1 and L2 (t = −0.89, p = 0.38). Between the two groups, they were comparable in L1 (t = 1.32, p = 0.19) and L3 (t = 1.55, p = 0.13), but they significantly differed in L2 (t = −4.1, p < 0.01). However, it should be noted that both groups can be considered overall proficient in their L1 and L2, as indexed by the mean values of 4.9 or above for both languages reported by both groups.

In terms of language use, the TMC students used Tibetan much more frequently than the other two languages both in school and at home. The CMC students used Tibetan usually at home but used Chinese for nearly half of the time in school. Their exposure to and degree of use of L2 English was greater than that of the TMC students. For both groups of students, English use was confined to classroom settings.

In order to test whether the participants’ proficiency in the non-task language interacted with their task performance in the experiments, for each experiment, we split the participants by mean into the higher and lower non-task language proficiency groups who contrasted significantly only in their non-task language and remained comparable in the task languages. Therefore, for Experiment 1, the participants were divided into the higher L3 group (N = 26, M = 4.42, SD = 0.57) and lower L3 group (N = 24, M = 2.75, SD = 0.63). Ten participants had to be removed because their inclusion would result in a significant difference between the higher and lower L3 groups on their L2 or L1. The difference between these two groups on L3 proficiency was significant (t = 10.45, p < 0.001). There was no significant difference in L1 proficiency between the two groups, M (L3 higher) = 6.08 (0.73), M (L3 lower) = 5.88 (1.05), t = 0.77, p = 0.45, nor in their L2, M (L3 higher) = 5.81 (0.79), M (L3 lower) = 5.71 (0.73), t = 0.41, p = 0.69.

Task and Stimuli

The participants were asked to perform the generalized lexical decision task in their L1 (Tibetan) and L2 (Chinese). There were three experimental blocks in the experiment, and each block consisted of 20 trials in each language, 10 word trials and 10 non-word trials. Each language consisted of 50% switch trials and 50% repetition trials. The sequence of trials was randomized for each participant group. Each participant needed to perform 30 word trials and 30 non-word trials per language in each experiment.

The stimuli pool of the three experiments consisted of 60 words and 60 non-words for each language. The selection of the stimuli words was as follows. A total of 120 L2 words were selected from The Contemporary Chinese Dictionary. These words were comparable in terms of strokes, with an average number of 15.52 strokes (SD = 7.62). Ten peer students who did not attend the experiments rated the familiarity of these words using a seven-point scale (1 = least familiar, 7 = most familiar). Results showed that the participants were all familiar with the L2 words (with a mean score of 6.58). A total of 60 of these words were used as word stimuli (30 in experiment 1 and 30 in experiment 3), and the other 60 were used to create L2 non-words for experiments 1 and 3 by replacing one character in two-/three-character Chinese words with a homonym to make the word meaningless. For example, the non-word www.frontiersin.org was created by replacing the first character of the word (computer) with its homonym

Another 120 L2 words that were not used as word stimuli, but they were comparable to stimuli L2 words in terms of word familiarity were also selected and translated by a local Tibetan-Chinese bilingual teacher to L1 Tibetan equivalents. The Tibetan equivalents were also rated for similarity by these 10 peer students. The results showed that they were familiar with them (with a mean score of 6.71). The average number of syllabus of these words is 2.1 (SD = 0.83). Sixty of these Tibetan words were used as word stimuli (30 in experiment 1 and 30 in experiment 2), and the rest were used to create L1 non-words for experiments 1 and 2 by deleting or adding a vowel or a consonant to a word to make it meaningless (see Figure 1).

FIGURE 1

Figure 1. An example of a word and a non-word in Tibetan, meaning “flower” (the left is a word and the right is a non-word).

A total of 120 L3 English words, which were comparable in terms of syllabus (with a median of 1.74 syllabus, SD = 0.71), were selected from the English textbook used in the junior Tibetan schools (Grade 7–9) and subjected to familiarity test. The results showed a high level of familiarity (mean score: 6.43). Similarly, 60 of these English words were used as stimuli words (30 in experiment 2 and 30 in experiment 3), and the rest were used to create non-words for experiments 2 and 3 by deleting or adding a letter in a legitimate word (e.g., wrd and booy).

Overall, the participants’ familiarity with the chosen words from the three languages did not differ significantly (F (1, 118) = 0.801, p = 0.372). The complete stimuli lists for experiments 1, 2, and 3 are provided in Supplementary Appendices A–C, respectively. It should be noted that non-words in the three languages were created in different ways. With L2 Chinese, the non-words are pronounceable but meaningless. However, the characters that make up the non-words are legitimate characters in Chinese. With L1 Tibetan, the non-words are meaningless and illegitimate in Tibetan, but they are pronounceable. As for L3 English, the non-words are meaningless, illegitimate, and unpronounceable¹.

Procedure

Prior to the experiment, the instructions were presented both orally and visually. The participants were told that a series of letter strings/characters would appear on the screen, one after the other, and that they had to decide as quickly and as accurately as possible whether each of the presented items was a word (Tibetan or Chinese) or not. Following the instructions, the participants performed a practice block of six trials. Stimuli were visually presented one by one for 3000 ms in the center of the computer screen with black text on a white background. The participants indicated their decision by pressing the right SHIFT key for word and the left SHIFT key for non-word (the mapping of the response keys to either decision was counterbalanced across participants). Stimuli stayed visible during the 3000 ms duration or until a response was registered. After the participant’s response there was a 600 ms interval until the next stimulus would be presented.

Analysis and Results

For the three experiments reported in this paper, participants’ RTs and error rates (ERs), which were recorded by DMDX software (Forster and Davis, 1984), were analyzed. Error trials were excluded from RT analyses. RTs and ERs on non-words were not included in the current analyses. In addition, RT above or below three standard deviations from the mean (per participant) were deleted. Data of the participants whose ERs were above 50% were also deleted. Taking these criteria into account resulted in the exclusion of 4.7% of the data. The mean RTs and ERs in Experiment 1 for different conditions and groups are shown in Table 2.

TABLE 2

Table 2. RTs and ERs in experiment 1.

For the analysis of RTs, we used linear mixed-effect (LMM) models with items and participants as random effects (Baayen, 2008). As fixed effects, we entered language (Experiment 1: Chinese as reference vs. Tibetan; Experiment 2: English as reference vs. Tibetan; Experiment 3: Chinese as reference vs. English), trial type (switch vs. non-switch as reference), and proficiency in the non-task language (high as reference vs. low) as well as the interaction terms. The models were fitted with the lmer function from the lme4 package (Bates et al., 2015) in the R statistical computing environment. Regression coefficients (b), standard errors (SE), and t-values for significant factors and interactions were reported. Fixed effects were considered reliable if | t| > 1.96 (Baayen, 2008). To analyze ERs, we fitted the logistic mixed models (Jaeger, 2008) with the glmer function in the R environment, again with items and participants as random effects and the same set of fixed factors and their interaction terms. Here, instead of reporting t-values, we reported z-values, which can be interrupted in the same way as the t-values.

In the analyses of RTs, the results showed no main effect of any individual factors. No significant interaction was found. In the analyses of ERs, there was a marginally significant interaction between language and proficiency in the non-task language (Tibetan: low L3: b = −1.30, SE = 0.73, z = −1.97). The high and low L3 groups performed similarly in Chinese. The high L3 group had an ER of 2.59% and the low L3 group an ER of 2.75%. However, the low L3 group made more mistakes in Tibetan (8.12%) while the high group made less (5.00%). The interaction is shown in Figure 2.

FIGURE 2

Figure 2. The interaction between language and the non-task language proficiency in ERs in Experiment 2.

Discussion

In Experiment 1, no effect of trial type was found. This means that the participants’ performance in making their lexical decision in the current trial was not affected significantly by whether the previous trial was in a different language or in the same language, thus resulting in no switch cost. A possible account for this result is to assume that the participants had language-specific access to their languages (Tibetan and Chinese). The BIA + model and its successors claim that when an input letter strings/characters are presented to bilinguals, orthographic representations that are similar to the input word regardless of language membership are activated (Grainger and Dijkstra, 1992; Dijkstra and van Heuven, 1998, 2002; van Kesteren et al., 2012). If the two languages differ with respect to their input codes, the activated set of neighbors may become much smaller. For languages that do not share input codes at all, the lexical items from the non-target language should not be activated at all, at least at the level of orthographic representation. The activation of the orthographic representation will next activate the corresponding semantic representation and/or phonological representation. The activation of the semantic representation can further feed activation to corresponding phonological representations of both languages (Casaponsa et al., 2019). However, the activation of phonological representations from the non-target language does not seem to adversely affect the lexical decision in the target language as the trial type did not significantly influence the task performance.

The interaction between language and non-task language proficiency in ERs suggested that the lower L3 group made more mistakes in Tibetan, whereas non-task language proficiency did not affect the task performance in Chinese. Here, there were two questions worth asking. One is why the non-task language affected task performance while the switching between the task languages did not. The other is why task performance in Tibetan and not in Chinese was affected. To answer these two questions, we needed to analyze the typological features of the three languages first. The three languages under discussion are orthographically and phonologically different. However, compared to Chinese, which features no correspondence between its character and pronunciation, Tibetan is closer to English in that there is a sort of mapping between spelling and pronunciation, despite the fact that English is an alphabetic language whereas Tibetan uses a syllabary. Therefore, it is possible that an improved proficiency in English means the participants are better at mapping pronunciation to spelling and vice versa. This improved ability can be carried over to Tibetan in that the orthographic input can be more quickly mapped onto the existing phonological representation (cf. Wu and Thierry, 2010). This can in turn facilitate lexical decision as orthographic input is mapped twice onto existing mental lexicon, once through semantic representation and once through phonological representation. This may explain why lexical decision in Tibetan was more accurate with the participants more proficient in English. Because Chinese is opaque in terms of the correspondence between its characters and pronunciation, the participants’ performance in the Chinese lexical decision would not benefit from their proficiency in the non-task language English.