Hostname: page-component-77f85d65b8-2tv5m Total loading time: 0 Render date: 2026-04-16T09:18:20.190Z Has data issue: false hasContentIssue false
  • English
  • Français

The impact of multicultural experience on language switching: Evidence from language comprehension

Published online by Cambridge University Press:  16 April 2026

Jiayuan Wang  and
Bei Yang Open the ORCID record for Bei Yang [Opens in a new window]
Jiayuan Wang
Affiliation:
Department of Chinese (Zhuhai), Sun Yat-sen University , Zhuhai, China The Greater Bay Area Phonetics and Speech Processing Laboratory, Sun Yat-sen University , Zhuhai, China
Bei Yang*
Affiliation:
Department of Chinese (Zhuhai), Sun Yat-sen University , Zhuhai, China The Greater Bay Area Phonetics and Speech Processing Laboratory, Sun Yat-sen University , Zhuhai, China
*
Corresponding author: Bei Yang; Email: yangb76@mail.sysu.edu.cn
Rights & Permissions [Opens in a new window]

Abstract

The present study aimed to investigate the impact of cultural exposure conditions (i.e., Chinese culture, American culture, dual cultures and control condition) on the bilingual language switch costs during the process of language comprehension. Sixty unbalanced Chinese–English bilinguals performed a modified animacy judgment task across four cultural exposure conditions, judging whether Chinese or English words referred to a living or nonliving objects. Reaction times and accuracy rates were analyzed using repeated-measures ANOVA. The results showed smaller switch costs in dual-cultural exposure than in the control condition. Furthermore, switch costs under different cultural exposures varied only for the second language (L2), with no significant differences for the first language (L1). Moreover, in the Chinese cultural exposure and control conditions, the switch costs for L2 were found to be larger than those for L1. Conversely, in the American and dual-cultural exposure conditions, the switch costs for L2 were smaller than those for L1.

Keywords

switch costslanguage switchingmulticultural experiencelanguage comprehensionbilingual

Information

Type
Research Article
Information
Bilingualism: Language and Cognition , First View , pp. 1 - 12
Check for updates
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2026. Published by Cambridge University Press

Highlights

  • • Dual-cultural exposure reduces bilingual language switch costs in comprehension.

  • • Cultural conditions affect L2 switch costs but not L1.

  • • The asymmetrical switch costs between L1 and L2 vary across cultural exposure contexts.

  • • Higher intercultural sensitivity correlates with greater switch costs in bilinguals.

  • • Supports the adaptive control hypothesis in bilingual language comprehension processes.

1. Introduction

In the context of rapid globalization, international communication inherently involves interactions and collaborations across diverse cultural and national groups, leading to a significant rise in bilingual and multilingual populations (Aririguzoh, Reference Aririguzoh2022; Ohmae, Reference Ohmae1990). During language use, bilinguals selectively engage their target language while suppressing interference from nontarget languages, a cognitive process termed language control (Green & Abutalebi, Reference Green and Abutalebi2013). As a cognitive mechanism, language control has an impact on language switching. Language switching refers to the phenomenon where individuals switch between different languages in their daily communications according to specific situations and communicative needs (Bullock & Toribio, Reference Bullock and Toribio2009). Investigating language switching not only enhances our understanding of language processing mechanisms and representation systems, but also offers practical implications for second language (L2) acquisition, cross-cultural communication and translation practices. Understanding cultural effects on switching costs, in particular, can inform language teaching strategies, enhance cross-cultural communication training and guide the development of more effective bilingual education programs.

Language switching incurs switch costs, as evidenced by increased response times (RTs) and error rates when alternating between languages (Meuter & Allport, Reference Meuter and Allport1999; Proverbio et al., Reference Proverbio, Leoni and Zani2004). These costs demonstrate directional asymmetry when switching occurs between languages of differing proficiency levels, a phenomenon formally termed switch cost asymmetry (Grainger & Beauvillain, Reference Grainger and Beauvillain1987). Switch costs are an important index for examining the mechanisms of language switching, which refers to the additional time or effort required to switch from one task or cognitive process to another. The switch costs are typically measured through RT and error rate. RT is the time observed in response to a task immediately after switching compared with repeating the same task. Error rate is the increase in errors that may occur immediately following a switch. Experimental investigations of language switching mechanisms during comprehension predominantly employ lexical decisions and semantic categorization paradigms (Aparicio & Lavaur, Reference Aparicio and Lavaur2014; Grainger & Beauvillain, Reference Grainger and Beauvillain1987; Macizo et al., Reference Macizo, Bajo and Paolieri2012; Mosca & de Bot, Reference Mosca and de Bot2017; Thomas & Allport, Reference Thomas and Allport2000; Von Studnitz & Green, Reference Von Studnitz and Green2002a). In the lexical decision task, participants may base their judgments on semantic comprehension or rely on orthographic and phonological knowledge (Orfanidou & Sumner, Reference Orfanidou and Sumner2005). The semantic categorization task, by contrast, requires participants to explicitly judge whether a target word denotes an animate entity, a decision that must be grounded in semantic information (Macizo et al., Reference Macizo, Bajo and Paolieri2012; Von Studnitz & Green, Reference Von Studnitz and Green2002b). Compared with the lexical decision task, the semantic categorization task requires participants to base their decision on semantics and minimizes the potential influence of orthographic or phonological information on the switching of language comprehension (Liu et al., Reference Liu, Fan, Shen and Ji2013). Therefore, the present study adopted the semantic categorization task to examine language switching during the comprehension of animate and inanimate words.

Previous studies have identified proficiency, age of acquisition and cognitive flexibility as critical factors influencing language switching and associated costs (Bonfieni et al., Reference Bonfieni, Branigan, Pickering and Sorace2019; Gross et al., Reference Gross, López González, Girardin and Almeida2022; H. Liu et al., Reference Liu, Fan, Rossi, Yao and Chen2016; Tenés et al., Reference Tenés, Weiner-Bühler, Volpin, Grob, Skoruppa and Segerer2023). Among these, cognitive flexibility has emerged as a particularly significant determinant. Cognitive flexibility denotes one’s capability to neatly alternate between distinct thought processes and actions in a particular context (Chevalier & Blaye, Reference Chevalier and Blaye2009; Geurts et al., Reference Geurts, Corbett and Solomon2009; H. Liu et al., Reference Liu, Fan, Rossi, Yao and Chen2016; Samanez-Larkin et al., Reference Samanez-Larkin, Buckholtz, Cowan, Woodward, Li, Ansari, Arrington, Baldwin, Smith, Treadway, Kessler and Zald2013). For example, Liu et al. (Reference Liu, Fan, Rossi, Yao and Chen2016) recruited participants with varying levels of cognitive flexibility to complete a picture-naming task and observed that cognitive flexibility modulates the switch costs inherent in language switching. These findings suggest that cognitive flexibility serves not only as a key factor in language switching but also as a dynamic mechanism susceptible to external influences. One such influence is individuals’ life experiences, particularly multicultural experiences, which have been shown to foster openness to new ideas and enhance cognitive flexibility (Tadmor et al., Reference Tadmor, Tetlock and Peng2006). Multicultural experiences are defined as all direct and indirect experiences of exposure to or interaction with the elements and/or members of foreign cultures (Leung et al., Reference Leung, Maddux, Galinsky and Chiu2008). Currently, there are three primary methods for manipulating multicultural experiences. The first method is the recall paradigm (Cao et al., Reference Cao, Galinsky and Maddux2014; Lu et al., Reference Lu, Quoidbach, Gino, Chakroff, Maddux and Galinsky2017). Specifically, participants are asked to recall their life experiences abroad or their interactions with members of foreign cultures. The second method is the imagine paradigm. Researchers instruct participants to imagine events that occurred during their hypothetical life abroad (Maddux & Galinsky, Reference Maddux and Galinsky2009). The third method is cultural priming. The most common priming approach involves presenting participants with single-cultural or multicultural stimuli in experimental contexts, such as images, music or videos that represent specific cultures (Leung & Chiu, Reference Leung and Chiu2010; Tadmor et al., Reference Tadmor, Galinsky and Maddux2012). Recently, a study involving 89 Korean–English bilingual college students in Korea further corroborated this hypothesis. Using an online survey paradigm, the research demonstrated that multicultural experience exerted a significant positive influence on cognitive flexibility (Kim & Runco, Reference Kim and Runco2022). However, there remains a research gap regarding the direct relationship between multicultural experiences and language switching as well as its switch costs. Compared with the recall and imagine paradigms, the cultural priming paradigm allows for stricter control of priming materials, which is more conducive to drawing causal inferences between stimuli and responses. Therefore, the current study adopted the cultural priming paradigm as a means of cultural priming to investigate the relationship between multicultural experiences and language switching. Before exploring this question, we will provide an overview of some previous studies related to cultural priming effects and language switching, as well as the underlying processing mechanisms.

What accounts for the mechanism of the cultural priming process? For instance, after engaging in the cultural practices of a given group, individuals develop a cognitive representation of that culture, which in turn activates its core concepts (Collins & Loftus, Reference Collins and Loftus1975; Leung & Chiu, Reference Leung and Chiu2010). The activation process can be linked to the spreading-activation theory, which posits that memory concepts are linked through a node-based network, with activation spreading along these associative routes.

In the domains of language production and comprehension, various language contexts and nonlinguistic contexts significantly modulated language control, thereby influencing performance in language switching tasks (Declerck & Philipp, Reference Declerck and Philipp2015; Liu et al., Reference Liu, Timmer, Jiao and Wang2020; Olson, Reference Olson2016; Timmer et al., Reference Timmer, Christoffels and Costa2019). Olson (Reference Olson2016) used a prompt picture-naming paradigm to conduct experiments under different language backgrounds (monolingual and bilingual) and found that switch costs were influenced by language proficiency and language background together. Timmer et al. (Reference Timmer, Christoffels and Costa2019) used a language-switching task paradigm to study the flexibility of bilingual language control and how it adapts to different language environments. They found that in an environment where the first language (L1) was predominantly used, switch cost was equal for both languages, while in an environment where the L2 was predominantly used, switching to the weaker language was more difficult. Declerck and Philipp (Reference Declerck and Philipp2015) employed a sentence generation task paradigm to investigate the impact of sentence environments on language switching. They required German–English bilinguals to alternate between grammatically structured sentences or disordered sentences. The results showed that no switch costs were observed in linguistically unspecific sentences, while switch costs were observed in linguistically specific or disordered sentences. Liu et al. (Reference Liu, Timmer, Jiao and Wang2020) used a comprehension-based language-switching task paradigm to investigate the impact of different environments (non-conflict and conflict environments) on bilingual language switch costs. They found that switch costs in conflict environments were greater than those in non-conflict environments, and asymmetric switch costs were observed in non-conflict environments, while symmetric switch costs were observed in conflict environments. These findings collectively support the adaptive control hypothesis (ACH, Green & Abutalebi, Reference Green and Abutalebi2013). The ACH suggests that bilingual individuals adapt their language control mechanisms in response to the demands placed on them by different interactional contexts (Green & Abutalebi, Reference Green and Abutalebi2013; Timmer et al., Reference Timmer, Christoffels and Costa2019). Therefore, the present study aims to expand the application of the ACH in different cultural exposure contexts.

Given that the ACH can account only for the influence of context on language control, it fails to provide detailed recommendations regarding how language control specifically adapts. The Bilingual Interactive Activation Plus (BIA+) model, which is highly influential in the field of language comprehension, was chosen to solve this problem (Dijkstra & van Heuven, Reference Dijkstra and van Heuven2002; C. Liu et al., Reference Liu, Timmer, Jiao and Wang2020). The BIA+ model posits that the lexical information of the two languages of bilinguals is stored in an integrated mental lexicon, and it differentiates between the word identification system and the task/decision system. The word identification system consists of orthography, phonology, semantics and language nodes. It judges the belonging language nodes (L1/L2) according to the orthographic, phonetic and semantic information of the input information. The processing of language nodes will affect the switch costs. For unbalanced bilinguals, the L1 lexical representations are more active than the L2 lexical representations. Consequently, when switching from L1 to L2, the inhibitory effect of the L1 language node on L2 words will be greater than that of the L2 language node on L1 words, resulting in the switch costs of L2 being greater than those of L1 (see Figure 1). The information within the word identification system will be transmitted to the task/decision system. The task/decision system generates the final response based on the information provided by the word identification system as well as the nonlinguistic situational information.

Figure 1. The switch costs of L2 were larger than those of L1 for unbalanced bilinguals. Arrows signify activation while squares denote inhibition. Solid lines and dashed lines represent L1 and L2, respectively.

Thus, the current study employed a cultural priming approach to manipulate different cultural exposure conditions and integrated an animacy judgment task to examine the impact of multicultural experiences on language switching in language comprehension. Cultural exposure scenarios included single-cultural exposure (i.e., Chinese culture, American culture), dual-cultural exposure and control conditions. Previous research demonstrated that cultural cues facilitated language production when they were consistent with the language (Jared et al., Reference Jared, Poh and Paivio2013; Li et al., Reference Li, Yang, Suzanne Scherf and Li2013; C. Liu et al., Reference Liu, Li, Jiao and Wang2021). Therefore, we hypothesized that in the cultural exposure conditions, the (a)symmetric switch costs between the two languages would vary depending on the context. The Chinese cultural exposure context would be more closely associated with L1, which facilitated faster recognition of L1 and resulted in smaller switch costs for L1 compared with L2. The opposite would be observed in the American cultural exposure context. To address the dual-cultural exposure scenario, we developed a twofold hypothesis. When both L1 and L2 were associated with images of L1 and L2, respectively, we expected that symmetric costs would be observed. Another possible scenario was the emergence of asymmetric costs. For bilinguals with a dominant L1 cultural background, L2 cultural stimuli (which were encountered less frequently in daily life) might capture more attentional resources (Chang, Reference Chang2013; Kartushina et al., Reference Kartushina, Frauenfelder and Golestani2016), thereby enhancing L2 activation and leading to smaller L2 switch costs. Meanwhile, given the importance of individual differences in the field of bilingualism research (Bonfieni et al., Reference Bonfieni, Branigan, Pickering and Sorace2020), we further explored the correlations between individual traits (e.g., cultural experiences and intercultural sensitivity) and switch costs across different cultural contexts. The Multicultural Experience Assessment (MExA) Scale was adopted to measure multicultural exposures and multicultural interactions (Aytug et al., Reference Aytug, Kern and Dilchert2018), while the Intercultural Sensitivity Scale (ISS) was used to capture sensitivity during intercultural interactions (Chen & Starosta, Reference Chen and Starosta2000). Our prediction was that individual differences exerted an impact on switch costs under the control condition, whereas the effects of individual differences on switch costs in other cultural conditions differed from those in the control condition.

2. Methods

2.1. Participants

A total of 60 healthy college participants were recruited to participate in the experiment. An a priori power analysis using the G*power 3.1.9.7 (Faul et al., Reference Faul, Erdfelder, Lang and Buchner2007) toolbox indicated that a sample of 28 participants would be enough to detect a medium effect size (f = .25, α = .05, power = .80). Three participants were excluded due to an error rate exceeding 20% on the task, resulting in a final sample of 57 participants (28 males, 21.49 ± 2.29 years; range: 18–25 years). The participants’ average age of acquisition was 8.28 ± 2.46 years for English, and all were native Chinese speakers. They had not lived or traveled in English-speaking countries for more than three months. All participants were non-English major students, and all of them passed the CET-4 (College English Test Band 4, max point is 710), and their CET-4 scores ranged from 500 to 600 (M = 550 ± 12 points, their English proficiency is classified as intermediate). Administered by the Ministry of the People’s Republic of China, the CET-4 is an official and well-recognized English proficiency assessment specifically designed for non-English major college students in China, evaluating their competence in four key skills: writing, listening, reading, and translating. Participants’ proficiency in Chinese and English across the four skills (listening, speaking, reading, and writing) was assessed via self-report on a five-point Likert scale, ranging from 1 (no proficiency) to 5 (very high proficiency), based on previous studies (de Bruin & Xu, Reference de Bruin and Xu2023; Jiang et al., Reference Jiang, Meng and Chen2024; C. Liu et al., Reference Liu, Jiao, Wang, Wang, Wang and Wu2019; Macizo et al., Reference Macizo, Bajo and Paolieri2012). Wilcoxon signed-rank tests found significant differences between L1 and L2 proficiency ratings across all four language skills, indicating that the participants were unbalanced bilinguals, with greater proficiency in L1 than in L2 (see Table 1). All of them were right-handed and had normal or corrected-to-normal vision. All participants signed an informed consent form before the experiment. Each participant completed the ISS (Cronbach’s alpha coefficient = .88; see Appendix A) and the MExA (Cronbach’s alpha coefficient = .86; see Appendix B) prior to the experiment (Aytug et al., Reference Aytug, Kern and Dilchert2018; Chen & Starosta, Reference Chen and Starosta2000). The MExA evaluated individuals’ multicultural experiences, including the frequency, duration and breadth of their contact and interactions. The ISS measured individuals’ sensitivity in cross-cultural communication.

Table 1. Means (SDs) of the language proficiency ratings in four language skills

Note: Ratings were assessed on a 5-point scale (1 = no proficiency; 5 = very high proficiency).

2.2. Materials

2.2.1. Words stimuli

Participants performed a revised version of animacy judgment task (see Procedure section). Unlike the classic animacy judgment task, participants were first exposed to pictures representing different cultures (serving as cultural primes). Subsequently, participants were asked to judge whether a word referred to a living or nonliving object (Macizo et al., Reference Macizo, Bajo and Paolieri2012; Von Studnitz & Green, Reference Von Studnitz and Green2002b; Zeelenberg & Pecher, Reference Zeelenberg and Pecher2003). The words stimuli consisted of 72 Chinese–English translation pairs (see Appendix C). Words referring to 72 living things (animate entities) and 72 nonliving things (inanimate entities) were selected using the category norms (Battig & Montague, Reference Battig and Montague1969; Van Overschelde et al., Reference Van Overschelde, Rawson and Dunlosky2004). Thirty-six pairs referred to living beings and thirty-six pairs referred to inanimate objects. Examples of living beings include peach and rabbit, whereas examples of inanimate objects include chair and book (Macizo et al., Reference Macizo, Bajo and Paolieri2012; Räling et al., Reference Räling, Hanne, Schröder, Keßler and Wartenburger2017; Von Studnitz & Green, Reference Von Studnitz and Green2002b; Zeelenberg & Pecher, Reference Zeelenberg and Pecher2003). A group of 30 undergraduate students (non-English majors) who were not involved in the formal experiment used a five-point Likert scale to evaluate the familiarity of Chinese–English words (1 = very unfamiliar; 5 = very familiar). The familiarity rating of all stimulus words in the current study was greater than 4.80. The English words ranged from one to three syllables, while the Chinese words were two characters long. The difference in average familiarity between animate and inanimate words in Chinese was not significant, nor was the difference in average familiarity between animate and inanimate words in English (see Table 2). Additionally, the difference in average familiarity between animate and inanimate words in Chinese and English was not significant. The difference in the average number of character strokes between animate and inanimate words in Chinese was not significant, nor was the difference in the average number of syllables between animate and inanimate words in English.

Table 2. t-test results of the stimulus attributes

2.2.2. Cultural pictures stimuli

The cultural priming stimuli comprised images depicting Chinese and American cultural elements, spanning a diverse range of thematic categories including apparel, architecture, cuisine, entertainment, home decorations, life, movies and scenery (Leung & Chiu, Reference Leung and Chiu2010). These pictures were initially selected on the Internet, and then they were edited into the same format using Photoshop. Subsequently, 30 non-English major students, who did not participate in the formal experiment, were asked to evaluate the representativeness, familiarity and categorization of the cultural images (see Table 3). “Representativeness” refers to the extent to which an image can stand for a particular culture. Finally, 108 images representing Chinese culture and 108 images representing American culture were obtained. Of these, 72 Chinese culture images (i.e., 36 pairs of Chinese culture stimuli) and 72 American culture images (i.e., 36 pairs of American culture stimuli) were selected as priming stimuli for Chinese and American culture conditions. The remaining 72 images (36 Chinese and 36 American) were selected and paired based on category, such as Chinese food images paired with American food images, forming 36 pairs of dual-cultural stimuli. This served as the experimental material for the dual-cultural exposure condition, ensuring that participants processed the two cultural images at the same cognitive level. For the control condition, 72 neutral images, composed of geometric shapes with no cultural significance, were used as stimuli. A post-experiment check revealed that participants were able to quickly and accurately categorize the images into Chinese culture, American culture, and neutral images.

Table 3. Means, SDs and t-test results of the attributes of Chinese and American culture images

2.3. Procedure

The experiment employed a culture priming manipulation with the animacy judgment task (see Figure 2), with data collected using E-prime 3.0 software. Each trial began with a fixation presented for 500 ms, followed by two cultural images displayed for 2000 ms. After a 100 ms blank screen, a stimulus word in L1 or L2 appeared. Participants were required to judge whether the word represented something living by pressing the “F” key for living and the “J” key for nonliving (key responses were counterbalanced across participants). If no response was given, the word disappeared after 3000 ms. A blank screen appeared for 500 ms before the start of the next trial. In the single-cultural priming condition, participants were presented with one cultural image on each side of the screen. The types of images used under the single-cultural condition were balanced (e.g., mooncake and hot pot; pizza and donut). In the dual cultures condition, a Chinese image and an American image were displayed on the left and right sides, with the lateral positions of the cultural images balanced and the categories matched (e.g., Chinese architecture versus American architecture). In the control condition, neutral images composed of geometric color blocks were presented on both sides of the screen. In the experiment, the repeat condition referred to at least two consecutive trials with the same response language (L1-L1 or L2-L2). The switch condition referred to trials where the response language differed from the previous trial (L1-L2 or L2-L1). Stimulus words were presented in the sequence “L1-L1-L2-L2-L2-L1,” and data were collected only from the underlined positions, representing four conditions: L1 non-switch, L2 switch, L2 non-switch, and L1 switch. The implementation of a fixed sequence was intended to ensure that the repetition effects were exclusively derived from the repeated trials (as opposed to “L1-L1-L2-L2-L1”) while eliminating any lag effects associated with task switching (in contrast to “L1-L2-L1-L2”) (Mosca & de Bot, Reference Mosca and de Bot2017). The experimental design comprised 576 trials, including 96 repetitions of L1 or L2, 96 switch trials between L1 and L2 or L2 and L1, and 192 filler trials. The trial types (switch versus non-switch) and stimuli words (living versus nonliving) were presented in a pseudorandom order. For six words in a fixed sequence, pictures primed by the same culture condition are used. There were 10 practice trials before the formal experiment, which began only if the practice accuracy exceeded 95%. The formal experiment lasted approximately 60 minutes.

Figure 2. Experimental timeline for the revised version of the animacy judgment task.

2.4. Data analyses

Behavioral data were analyzed for RTs using SPSS 24.0 Software. RTs less than 200 ms and error trials were excluded from the analysis. For each condition, we also excluded trials that were more than 2.5 standard deviations (SDs) from the mean RTs (C. Liu et al., Reference Liu, Jiao, Wang, Wang, Wang and Wu2019; Wang et al., Reference Wang, Fan, Liu and Cai2014). According to these criteria, 5.8% of the total data were excluded from the analysis. In the current study, the RTs and accuracy rates were used. The switch costs were calculated by subtracting the RTs and accuracy rates in the repeat condition from the RTs in the switching condition. In addition, we performed a correlation analysis between the questionnaire which includes the ISS and the MExA Scale (the two scales were mentioned in “Participants” section above) and the switch costs. Correlations were calculated using Spearman or Pearson correlation according to data distribution. Specifically, if the data were normally distributed, Pearson correlation was employed; otherwise, Spearman correlation was used.

3. Results

3.1. Response times and switch costs

The RTs of participants when completing the animacy judgment task were regarded as one of the behavioral outcomes. A 4 (cultural exposure condition: Chinese culture versus American culture versus dual cultures versus control condition) × 2 (language type: L1 versus L2) repeated-measures ANOVA was performed on the RTs (see Table 4). A significant main effect of cultural exposure condition was observed in the results (F(3,168) = 3.339, p = .021, ηp2 = 0.056), as reflected by a smaller switch costs in the dual-cultural exposure condition than in the control condition (p = .007). Comparisons of switch costs under other cultural exposure conditions were not significant (all ps ≥ .175). These findings indicated that the dual-cultural exposure condition had a significant reduction effect on switch costs.

Table 4. Mean RTs (ms) and SDs of the non-switch and switch trials for L1 and L2 in different priming conditions

Although the main effect of language type was not significant (p = .992), the interaction between cultural exposure condition and language type was significant (F(3,168) = 8.817, p < .001, ηp2 = 0.136, see Table 4). Post hoc analyses demonstrated that when processing L2, the Chinese cultural exposure condition showed significantly higher switch costs than both the American condition (p = .018) and the dual-cultural exposure condition (p = .004). The switch costs in the control condition were also higher than those in the American condition (p = .001) and the dual-cultural exposure condition (p < .001). No significant differences in switch costs across cultural conditions were observed during L1 processing. These findings suggested that when processing L2, the switch costs in the Chinese cultural exposure condition and the control condition were, respectively, larger than those in the American cultural exposure condition and the dual-cultural exposure condition (see Figure 3A).

Figure 3. The switch costs (RTs) of L1 and L2 in the Chinese cultural exposure condition, American cultural exposure condition, dual-cultural exposure condition and control condition. (A) Fixing different levels of language to explore the simple effects of cultural exposure context. (B) Fixing different levels of cultural exposure context to explore the simple effects of language. (C) The switch costs (accuracy rates) of L1 and L2 in the Chinese cultural exposure condition, American cultural exposure condition, dual-cultural exposure condition and control condition. (D) Positive correlation between Intercultural Sensitivity Scale (ISS) scores and L2 switch costs in the control condition. Note: * p < . 05. ** p < . 01. *** p < .001.

The post hoc analyses also revealed that in the Chinese cultural exposure condition (p = .024) and control condition (p = .009), the switch costs for L2 were significantly larger than those for L1, whereas in both the American cultural exposure condition (p = .011) and dual-cultural exposure condition (p = .013), the switch costs for L2 were indicated to be substantially smaller than those for L1. These findings revealed that the switch costs were asymmetric under all cultural exposure conditions. Specifically, the switch costs of L2 were larger than those of L1 in the Chinese cultural exposure condition and the control condition, and the switch costs of L2 were smaller than those of L1 in the American cultural exposure condition and the dual-cultural exposure condition (see Figure 3B).

3.2. Accuracy rates and switch costs

We also conducted a 4 (cultural exposure condition: Chinese culture versus American culture versus dual cultures versus control condition) × 2 (language type: L1 versus L2) repeated-measures ANOVA on the accuracy rates (see Table 5). The results found no significant main effect, but a significant interaction effect was detected (F(3,168) = 3.365, p = .020, ηp2 = 0.057). Post hoc analysis revealed that in the control condition, the switch costs of L2 were higher than those of L1 (p = .038). In the American cultural exposure condition (p = .033), the switch costs of L2 were lower than those of L1. Furthermore, additional analyses concerning accuracy rates did not demonstrate any significant effects (all ps ≥ .283). These two accuracy rate results were consistent with the RTs results. These findings demonstrated that the switch costs were asymmetric in the control condition and the American cultural exposure condition, i.e., the switch costs of L2 were higher than those of L1 in the control condition, and the switch costs of L2 were lower than those of L1 in the American cultural exposure condition (see Figure 3C).

Table 5. Mean accuracy and SDs of the non-switch and switch trials for L1 and L2 in different priming conditions

3.3. Associations between personality traits and behavior

Pearson’s r correlation analyses showed significant positive correlations between ISS and L2 switch costs in the control condition (r = .397, p = .002; Figure 3D), indicating that individuals with higher intercultural sensitivity levels exhibited larger L2 switch costs in the control condition. No significant correlation was found between MExA and switch costs (all ps ≥ .139). The positive correlations between ISS and L2 switch costs were significant after multiple comparison corrections (Bonferroni-corrected p = 0.016).

4. Discussion

The present study adopted the modified animacy judgment task to explore the impact of multicultural experiences on language switching during bilingual language comprehension. We identified the following four key findings based on the RT data: (1) The switch costs in the dual-cultural exposure condition were significantly lower compared with those in the control condition. (2) When processing L2, participants in the Chinese cultural exposure condition exhibited significantly larger switch costs than those in both the American condition and the dual-cultural exposure condition. The control condition also showed a pattern similar to that of the Chinese cultural exposure condition. (3) In the Chinese cultural exposure and control conditions, switch costs were larger for L2 than for L1, whereas the American and dual-cultural exposure conditions showed the opposite pattern. (4) A positive correlation was observed between L2 switch costs and ISS scores in the control condition.

First, results from RTs showed that the dual-cultural exposure condition elicited significantly reduced language switch costs relative to the control condition. This finding is the first to demonstrate the regulatory effect of multicultural experiences on language switching. Previous studies have demonstrated that multicultural experiences exert extensive impacts on individuals’ higher-order cognitive and sociopsychological functioning, such as creativity, psychological adjustment, intergroup bias and trust (Leung et al., Reference Leung, Maddux, Galinsky and Chiu2008; Maddux et al., Reference Maddux, Lu, Affinito and Galinsky2021; Sparkman et al., Reference Sparkman, Eidelman and Blanchar2016; Tadmor et al., Reference Tadmor, Hong, Chao, Wiruchnipawan and Wang2012). Most of these studies have focused on social cognition or complex cognitive functions, whereas the present study extends the boundary of multicultural influences further to switch costs. Our finding indicates that the benefits of multicultural exposure are not confined to higher-order psychological functions but permeate more basic cognitive control processes. The present finding also offers novel empirical evidence in support of the BIA+ model (Dijkstra & van Heuven, Reference Dijkstra and van Heuven2002). In the dual-cultural exposure condition, compared with the control condition, the association between cultural pictures and their corresponding languages was stronger than that of neutral images. We interpret this as suggesting faster processing and activation within the word identification system, thereby reducing switch costs. The current findings are also interpretable through the lens of spreading-activation theory (Collins & Loftus, Reference Collins and Loftus1975). The theory posits that memory concepts are interconnected via a network of nodes, with activation propagating along these associative pathways. In the dual-cultural exposure condition, the strong connection between cultural pictures and language functioned as an “activation pathway.” When cultural pictures were activated as cues, associated language information rapidly spread through the word recognition system, accelerating input processing and activation rates, thereby reducing switch costs.

Second, analyses of RT data indicated that during L2 processing, participants in the Chinese cultural exposure condition incurred significantly greater language switch costs than those in the American cultural exposure condition and the dual-cultural exposure condition. The control condition showed a cognitive performance pattern matching that of the Chinese cultural exposure condition. These results indicate that the activation of native culture or the default state without specific cultural priming significantly increases the cognitive cost of L2 switching for bilinguals. The present findings are interpretable within the framework of the BIA+ model (Dijkstra & van Heuven, Reference Dijkstra and van Heuven2002). Compared with the American and dual-cultural exposure contexts, L2 words had lower associativity with Chinese cultural images and neutral images, leading to greater conflict during L2 processing and slower RTs. Thus, when processing L2, switching costs were higher in the Chinese cultural exposure and control conditions than in the American and dual-cultural exposure contexts where the associativity was stronger. Furthermore, previous studies had indicated that L1 processing was associated with relatively stable factors, such as working memory capacity, whereas L2 processing was related to proficiency and age of acquisition (Bice & Kroll, Reference Bice and Kroll2021; Dąbrowska, Reference Dąbrowska2018). The present study further expands the conclusions of previous research and advances the understanding of bilinguals’ sensitive dependence on contextual cues during L2 processing. Meanwhile, we could also infer that the result of the main effect (i.e., the switch costs in the dual-cultural exposure condition were smaller than those in the control condition) was driven by smaller L2 switch costs in the dual-cultural exposure condition.

Third, consistent with our hypothesis, in both the Chinese cultural exposure condition and the control condition, the switch costs were larger for L2 than for L1. In the control condition, the switch costs derived from both RT and accuracy rate metrics showed that L2 exceeded L1, yielding an asymmetric pattern. This finding aligns with previous research, such as the study by Liu et al. (Reference Liu, Jiao, Wang, Wang, Wang and Wu2019), which demonstrated that unbalanced bilinguals exhibit higher switch costs for L2 compared with L1 in non-conflict situations. Another study used 12 unbalanced French–English bilinguals to perform a lexical decision task, in which they judged whether letter strings were words or not. The results also showed higher switch costs (Aparicio & Lavaur, Reference Aparicio and Lavaur2014). This asymmetry can be explained by the BIA+ model (Dijkstra & van Heuven, Reference Dijkstra and van Heuven2002), which posits that L1 words have a higher resting activation level than L2 words for unbalanced bilinguals. Consequently, the inhibitory control exerted by the L1 language node on L2 words is stronger than that exerted by the L2 language node on L1 words, leading to larger switch costs for L2.

In the Chinese cultural exposure condition, participants showed a RT switch-cost pattern matching the control condition, with larger costs observed in L2 than in L1. Given that L2 was more susceptible to influences from the cultural exposure context, the observed pattern primarily resulted from the inconsistency between Chinese cultural images and L2. This inconsistency slowed down L2 recognition speed, leading to a larger L2 switch cost (C. Liu et al., Reference Liu, Li, Jiao and Wang2021; Figure 4A). This observation can also be explained by the spreading-activation theory (Collins & Loftus, Reference Collins and Loftus1975). When switching to L2 judgment under the condition of Chinese cultural exposure, there is a “cognitive conflict” with the Chinese cultural framework activated by the current cultural images. At this point, switching to L2 required inhibiting the interference of the native culture embedded in the Chinese cultural framework, so the switching cost to L2 was significantly higher than that to L1.

Figure 4. (A) In the Chinese cultural exposure context, the incongruence between cultural image cues and L2 led to slower recognition speed of L2 and higher switch costs for L2. (B) Images were more strongly associated with L2 in the American cultural exposure context, which accelerated the recognition of L2 words in the identification system and decreased the switch costs for L2. Arrows signify activation while squares denote inhibition. Solid lines and dashed lines represent L1 and L2 influence, respectively. The red color signifies that the activation of L2 language node by L2 words was enhanced in the context of American culture. Meanwhile, the blue color indicates that the inhibition of L2 words by L1 language node was weakened in the same cultural context.

The American cultural exposure condition exhibited asymmetrical switch costs (L2 switch costs < L1 switch costs) both in terms of RT and accuracy rate metrics, which aligned with our hypothesis. This pattern suggests that the American cultural stimuli were more closely associated with the participants’ L2, facilitating faster activation of L2 words (see Figure 4B). This result can be justified by the BIA+ model: The facilitatory effect of American cultural stimuli on L2 processing likely strengthened the recognition of the L2 words, resulting in smaller switch costs for L2 than for L1 in the American cultural exposure context (Dijkstra & van Heuven, Reference Dijkstra and van Heuven2002). In addition, according to the spreading-activation theory (Collins & Loftus, Reference Collins and Loftus1975), exposure to American cultural cues (e.g., the Statue of Liberty) directionally activates the American construct network. At this time, the temporary accessibility of this network exceeds that of the Chinese cultural construct network, making it the dominant framework for cognitive processing. Moreover, L2 processing is highly compatible with the activated American cultural construct network, which allows for the direct utilization of corresponding cultural-linguistic resources and thus results in low cognitive coordination costs during switching.

In the dual-cultural exposure condition, we observed an asymmetric switch cost (L2 switch costs < L1 switch costs) based on RT data. This observation is congruent with the asymmetric hypothesis that we advanced for the present study. Previous studies have shown that faces with cultural cues facilitate language production when they match the language to be spoken (Jared et al., Reference Jared, Poh and Paivio2013; Li et al., Reference Li, Yang, Suzanne Scherf and Li2013). Additionally, for unbalanced Chinese–English bilinguals, the connection between Chinese cultural cues and L1 was stronger than that between Western cultural cues and L2, as these bilinguals were more familiar with the cultural background associated with Chinese cultural cues than with that of Western cultural cues (C. Liu et al., Reference Liu, Li, Jiao and Wang2021). However, inconsistent with the previous finding, when exposed to dual-cultural environments (i.e., when participants were simultaneously exposed to both Chinese and American cultural images), participants were more likely to be influenced by American cultural images, leading to a facilitation effect on L2 recognition and reduced L2 switch costs. This inconsistency was likely driven by the stability of L1 and the novelty effect induced by L2 (Chang, Reference Chang2013; Kartushina et al., Reference Kartushina, Frauenfelder and Golestani2016). In addition, the dual-cultural exposure condition yielded a similar pattern consistent with that of the American cultural context, which further furnishes empirical evidence for the activation competition that arises in the presence of concurrent multiple cultural cues. The findings of this study can further be applied to the domain of bilingual education and training, where differentiated training protocols can be formulated in light of these outcomes.

The observed asymmetry in switch cost patterns across varying cultural exposure conditions provides further empirical support for the ACH. The ACH has been widely applied in language production, but its application in language comprehension is relatively limited (Green & Abutalebi, Reference Green and Abutalebi2013). A previous study has demonstrated that the processing context (i.e., non-conflict context or conflict context) affects the control process of language comprehension (C. Liu et al., Reference Liu, Timmer, Jiao and Wang2020). The contribution of the current study lies in further supporting and extending the application of the ACH in language comprehension, and in proving that the cultural exposure context modulated the control process of language switching.

Lastly, Pearson’s r correlation analyses found significant positive correlations between ISS and L2 switch costs in the control condition. This finding is consistent with our prior prediction that individual differences would exert an impact on switch costs under the control condition. Individuals with higher scores on the ISS demonstrated greater sensitivity to other cultures during cross-cultural communication (Cancino & Nuñez, Reference Cancino and Nuñez2023; Chen, Reference Chen1997; Chen & Starosta, Reference Chen and Starosta2000). In the control condition, participants with higher ISS scores exhibited greater sensitivity to American cultural pictures and allocated more attention to the corresponding L2, leading to longer durations in recognizing L2 words. According to the BIA+ model, participants with higher ISS exhibited a slower processing rate of L2 within the recognition system, resulting in larger L2 switch costs (Dijkstra & van Heuven, Reference Dijkstra and van Heuven2002). However, the current study did not find a correlation between other cultural conditions and ISS. Given that previous studies have shown that when individuals are exposed to culturally specific stimuli, the brain engages in additional cognitive processing (e.g., cultural identification, cultural categorization) – a process that increases cognitive load and subsequently affects the allocation of cognitive resources to other tasks (Lottridge et al., Reference Lottridge, Chignell and Yasumura2012; Sanders, Reference Sanders1997; Spears et al., Reference Spears, Haslam and Jansen1999). This line of evidence suggests that ISS may correlate with L2 switch costs by influencing cognitive control resources. Therefore, in the control condition where no additional cultural stimuli are present, cognitive control resources can be fully allocated to the switch task, allowing the inherent correlation between ISS and L2 switch costs to emerge clearly. In contrast, in the cultural conditions, cultural cues themselves require cognitive processing. This “resource competition” reduces the overlap between ISS-related resources and those used for L2 switching, leading to the disappearance of their correlation. This finding further offers empirical support for the research and development of intelligent cross-cultural communication tools, enabling the provision of tailored services for users across varying ISS levels.

Nevertheless, the current study has several limitations. First, the study focused exclusively on Chinese–English bilinguals, which limits the generalizability of the findings to other language pairs. Future studies should explore different language combinations to elucidate the universality and specificity of multicultural experiences in language switching. Second, the participant pool was restricted to college students. Future research should expand the sample to include bilinguals from diverse age groups, occupational fields and educational backgrounds to provide a more comprehensive understanding of switch costs. Third, the current design lacked neuroimaging techniques. Incorporating approaches such as functional magnetic resonance imaging (fMRI) or electroencephalography (EEG) in future studies could unveil the neural underpinnings of language control and the interplay between cultural cognition and language processing (Cohen, Reference Cohen2017; Logothetis, Reference Logothetis2008). Fourth, our findings were restricted to the animacy judgment task used in the present experiment; whether similar results will be obtained in other language-comprehension studies remains to be tested in future research. Fifth, we note that the experimental trial order was pseudorandom in the current study. Future experiments could employ a completely randomized sequence to further investigate additional correlations between variables.

In summary, the present study demonstrated that L2 processing relied on cultural exposure contexts, whereas L1 did not. Moreover, language switch costs showed varying magnitudes and asymmetrical manifestations in different cultural exposure scenarios. We also found that individual differences (e.g., ISS) played a significant role in modulating switch costs. Consequently, these results underscore the critical role of multicultural experiences with diverse characteristics in regulating bilingual language switch costs. The findings not only deepen our understanding of how multicultural experiences shape language switching but also offer theoretical insights into the underlying cognitive mechanisms.

5. Conclusion

In conclusion, the current study demonstrates that multicultural experience asymmetrically modulates language-switching costs in Chinese–English bilinguals, with L2 processing being more sensitive to cultural context than L1. Results support the BIA+ model and ACH, revealing that L2 switch costs exceeded those of L1 in Chinese cultural and control conditions, whereas the opposite pattern occurred in the American and dual-cultural conditions. Individual differences in ISS further influenced L2 switch costs in the control condition. Limitations include sample specificity and the lack of neural evidence. Future work should incorporate diverse language pairs, broader populations and neuroimaging methods to generalize mechanisms and applications in bilingual education and cross-cultural communication.

Data availability statement

The data that support the findings of this study are available upon request to the authors.

Acknowledgements

We express our sincere gratitude to the participants in our experiment, whose valuable time and cooperation made this research possible. We also express our gratitude for the support from the National Social Science Fund of China (19WYYB007) and the Language Cooperation Center of the Ministry of Education (YHJC21ZD-034).

Competing interests

No competing financial interests are declared by the authors.

Ethical standard

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Appendix A

Intercultural Sensitivity Scale

Below is a series of statements concerning intercultural communication. There are no right or wrong answers. Please work quickly and record your first impression by indicating the degree to which you agree or disagree with the statement. Thank you for your cooperation (5 = strongly agree; 4 = agree; 3 = uncertain; 2 = disagree; 1 = strongly disagree).

1. I enjoy interacting with people from different cultures.

2. I think people from other cultures are narrow-minded.

3. I am pretty sure of myself in interacting with people from different cultures.

4. I find it very hard to talk in front of people from different cultures.

5. I always know what to say when interacting with people from different cultures.

6. I can be as sociable as I want to be when interacting with people from different cultures.

7. I do not like to be with people from different cultures.

8. I respect the values of people from different cultures.

9. I get upset easily when interacting with people from different cultures.

10. I feel confident when interacting with people from different cultures.

11. I tend to wait before forming an impression of culturally-distinct counterparts.

12. I often get discouraged when I am with people from different cultures.

13. I am open-minded to people from different cultures.

14. I am very observant when interacting with people from different cultures.

15. I often feel useless when interacting with people from different cultures.

16. I respect the ways people from different cultures behave.

17. I try to obtain as much information as I can when interacting with people from different cultures.

18. I would not accept the opinions of people from different cultures.

19. I am sensitive to my culturally-distinct counterpart’s subtle meanings during our interaction.

20. I think my culture is better than other cultures.

21. I often give positive responses to my culturally different counterpart during our interaction.

22. I avoid those situations where I will have to deal with culturally-distinct persons.

23. I often show my culturally-distinct counterpart my understanding through verbal or nonverbal cues.

24. I have a feeling of enjoyment towards differences between my culturally-distinct counterpart and me.

Appendix B

Multicultural Experience Assessment

Instructions: Please read these important notes before you answer the questions:

  • • In this survey, “culture” refers to cultures of countries. For example, Italian culture, French culture, Kenyan culture.

  • • Please choose one culture as your primary/dominant culture, even if you are bicultural or multicultural. In this survey, “foreign or different culture” means any culture other than the primary culture you chose.

All frequency items used the following 6-point Likert-type scale: 1 = never; 2 = once a year or less frequently; 3 = 2–11 times a year; 4 = 1–3 times a month; 5 = 1–6 days a week; and 6 = every day or multiple times a day.

  • • Watching movies that take place in different cultures?

  • • Reading books about foreign people?

  • • Listening to music of foreign cultures?

  • • Watching foreign TV channels?

  • • Watching different cultures’ celebrations (e.g., festivals, parades) on TV?

  • • See art (e.g., plays, opera, architecture, sculpture, paintings) of foreign cultures?

  • • Talking to people from different cultures?

  • • Socializing with people from different cultures?

  • • Sharing feelings with people from different cultures?

  • • Communicating via writing (e.g., emails, text messages, instant messaging) with people from different cultures?

Appendix C

Stimuli used in Experiment

References

Aparicio, X., & Lavaur, J.-M. (2014). Recognising words in three languages: Effects of language dominance and language switching. International Journal of Multilingualism, 11(2), 164–181.10.1080/14790718.2013.783583CrossRefGoogle Scholar
Aririguzoh, S. (2022). Communication competencies, culture and SDGs: Effective processes to cross-cultural communication. Humanities and Social Sciences Communications, 9(1), 1–11. https://doi.org/10.1057/s41599-022-01109-4.CrossRefGoogle Scholar
Aytug, Z. G., Kern, M. C., & Dilchert, S. (2018). Multicultural experience: Development and validation of a multidimensional scale. International Journal of Intercultural Relations, 65, 1–16. https://doi.org/10.1016/j.ijintrel.2018.04.004.CrossRefGoogle Scholar
Battig, W. F., & Montague, W. E. (1969). Category norms of verbal items in 56 categories A replication and extension of the Connecticut category norms. Journal of Experimental Psychology, 80(3, Pt.2), 1–46. https://doi.org/10.1037/h0027577.CrossRefGoogle Scholar
Bice, K., & Kroll, J. F. (2021). Grammatical processing in two languages: How individual differences in language experience and cognitive abilities shape comprehension in heritage bilinguals. Journal of Neurolinguistics, 58, 100963. https://doi.org/10.1016/j.jneuroling.2020.100963.CrossRefGoogle ScholarPubMed
Bonfieni, M., Branigan, H. P., Pickering, M. J., & Sorace, A. (2019). Language experience modulates bilingual language control: The effect of proficiency, age of acquisition, and exposure on language switching. Acta Psychologica, 193, 160–170. https://doi.org/10.1016/j.actpsy.2018.11.004.CrossRefGoogle ScholarPubMed
Bonfieni, M., Branigan, H. P., Pickering, M. J., & Sorace, A. (2020). Cognitive control in bilinguals: Effects of language experience and individual variability. Bilingualism: Language and Cognition, 23(1), 219–230. https://doi.org/10.1017/S1366728918001086.CrossRefGoogle Scholar
Bullock, B. E., & Toribio, A. J. E. (2009). The Cambridge handbook of linguistic code-switching. Cambridge University Press.10.1017/CBO9780511576331CrossRefGoogle Scholar
Cancino, M., & Nuñez, I. (2023). EFL teachers perceptions towards multicultural classrooms: The role of intercultural sensitivity in developing intercultural communicative competence. rEFLections, 30(3), 620–646.10.61508/refl.v30i3.267833CrossRefGoogle Scholar
Cao, J., Galinsky, A. D., & Maddux, W. W. (2014). Does travel broaden the mind? Breadth of foreign experiences increases generalized trust. Social Psychological and Personality Science, 5(5), 517–525. https://doi.org/10.1177/1948550613514456.CrossRefGoogle Scholar
Chang, C. B. (2013). A novelty effect in phonetic drift of the native language. Journal of Phonetics, 41(6), 520–533. https://doi.org/10.1016/j.wocn.2013.09.006.CrossRefGoogle Scholar
Chen, G.-M. (1997). A review of the concept of intercultural sensitivity. Human Communication, 1, 1–16.Google Scholar
Chen, G.-M., & Starosta, W. J. (2000). The development and validation of the intercultural sensitivity scale. Human Communication, 3, 1–15.Google Scholar
Chevalier, N., & Blaye, A. (2009). Setting goals to switch between tasks: Effect of cue transparency on children’s cognitive flexibility. Developmental Psychology, 45(3), 782–797. https://doi.org/10.1037/a0015409.CrossRefGoogle ScholarPubMed
Cohen, M. X. (2017). Where does EEG come from and what does it mean? Trends in Neurosciences, 40(4), 208–218. https://doi.org/10.1016/j.tins.2017.02.004.CrossRefGoogle ScholarPubMed
Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407–428. https://doi.org/10.1037/0033-295X.82.6.407.CrossRefGoogle Scholar
Dąbrowska, E. (2018). Experience, aptitude and individual differences in native language ultimate attainment. Cognition, 178, 222–235. https://doi.org/10.1016/j.cognition.2018.05.018.CrossRefGoogle ScholarPubMed
de Bruin, A., & Xu, T. (2023). Language switching in different contexts and modalities: Response-stimulus interval influences cued-naming but not voluntary-naming or comprehension language-switching costs. Bilingualism: Language and Cognition, 26(2), 402–415. https://doi.org/10.1017/S1366728922000554.CrossRefGoogle Scholar
Declerck, M., & Philipp, A. M. (2015). A sentence to remember: Instructed language switching in sentence production. Cognition, 137, 166–173. https://doi.org/10.1016/j.cognition.2015.01.006.CrossRefGoogle ScholarPubMed
Dijkstra, T., & van Heuven, W. J. B. (2002). The architecture of the bilingual word recognition system: From identification to decision. Bilingualism: Language and Cognition, 5(3), 175–197. https://doi.org/10.1017/S1366728902003012.CrossRefGoogle Scholar
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/bf03193146.CrossRefGoogle Scholar
Geurts, H. M., Corbett, B., & Solomon, M. (2009). The paradox of cognitive flexibility in autism. Trends in Cognitive Sciences, 13(2), 74–82. https://doi.org/10.1016/j.tics.2008.11.006.CrossRefGoogle ScholarPubMed
Grainger, J., & Beauvillain, C. (1987). Language blocking and lexical access in bilinguals. The Quarterly Journal of Experimental Psychology Section A, 39(2), 295–319. https://doi.org/10.1080/14640748708401788.CrossRefGoogle Scholar
Green, D. W., & Abutalebi, J. (2013). Language control in bilinguals: The adaptive control hypothesis. Journal of Cognitive Psychology, 25(5), 515–530. https://doi.org/10.1080/20445911.2013.796377.CrossRefGoogle ScholarPubMed
Gross, M. C., López González, A. C., Girardin, M. G., & Almeida, A. M. (2022). Code-switching by Spanish–English bilingual children in a code-switching conversation sample: Roles of language proficiency, interlocutor behavior, and parent-reported code-switching experience. Language, 7(4), 4. https://doi.org/10.3390/languages7040246.Google Scholar
Jared, D., Poh, R. P. Y., & Paivio, A. (2013). L1 and L2 picture naming in Mandarin–English bilinguals: A test of bilingual dual coding theory. Bilingualism: Language and Cognition, 16(2), 383–396. https://doi.org/10.1017/S1366728912000685.CrossRefGoogle Scholar
Jiang, S., Meng, Y., & Chen, B. (2024). The impact of emotional states on bilingual language control in cued and voluntary switching contexts. Journal of Memory and Language, 137, 104527. https://doi.org/10.1016/j.jml.2024.104527.CrossRefGoogle Scholar
Kartushina, N., Frauenfelder, U. H., & Golestani, N. (2016). How and when does the second language influence the production of native speech sounds: A literature review. Language Learning, 66(S2), 155–186. https://doi.org/10.1111/lang.12187.CrossRefGoogle Scholar
Kim, D., & Runco, M. A. (2022). Role of cognitive flexibility in bilingualism and creativity. Journal of Creativity, 32(3), 100032. https://doi.org/10.1016/j.yjoc.2022.100032.CrossRefGoogle Scholar
Leung, A. K., & Chiu, C. (2010). Multicultural experience, idea receptiveness, and creativity. Journal of Cross-Cultural Psychology, 41(5–6), 723–741. https://doi.org/10.1177/0022022110361707.CrossRefGoogle Scholar
Leung, A. K., Maddux, W. W., Galinsky, A. D., & Chiu, C. (2008). Multicultural experience enhances creativity: The when and how. American Psychologist, 63(3), 169–181. https://doi.org/10.1037/0003-066X.63.3.169.CrossRefGoogle Scholar
Li, Y., Yang, J., Suzanne Scherf, K., & Li, P. (2013). Two faces, two languages: An fMRI study of bilingual picture naming. Brain and Language, 127(3), 452–462. https://doi.org/10.1016/j.bandl.2013.09.005.CrossRefGoogle ScholarPubMed
Liu, C., Jiao, L., Wang, Z., Wang, M., Wang, R., & Wu, Y. J. (2019). Symmetries of bilingual language switch costs in conflicting versus non-conflicting contexts. Bilingualism: Language and Cognition, 22(3), 624–636. https://doi.org/10.1017/S1366728918000494.CrossRefGoogle Scholar
Liu, C., Li, L., Jiao, L., & Wang, R. (2021). Bilingual language control flexibly adapts to cultural context. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.744289.Google ScholarPubMed
Liu, C., Timmer, K., Jiao, L., & Wang, R. (2020). Symmetries of comprehension-based language switch costs in conflicting versus non-conflicting contexts. International Journal of Bilingualism, 24(4), 588–598. https://doi.org/10.1177/1367006919848487.CrossRefGoogle Scholar
Liu, H., Fan, N., Rossi, S., Yao, P., & Chen, B. (2016). The effect of cognitive flexibility on task switching and language switching. International Journal of Bilingualism, 20(5), 563–579. https://doi.org/10.1177/1367006915572400.CrossRefGoogle Scholar
Liu, H., Fan, N., Shen, X., & Ji, J. (2013). Effect of cognitive flexibility on language switching in non-proficient bilinguals: An ERPs study: Effect of cognitive flexibility on language switching in non-proficient bilinguals: An ERPs study. Acta Psychologica Sinica, 45(6), 636–648. https://doi.org/10.3724/SP.J.1041.2013.00636.CrossRefGoogle Scholar
Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453(7197), 869–878. https://doi.org/10.1038/nature06976.CrossRefGoogle Scholar
Lottridge, D., Chignell, M., & Yasumura, M. (2012). Identifying emotion through implicit and explicit measures: Cultural differences, cognitive load, and immersion. IEEE Transactions on Affective Computing, 3(2), 199–210. https://doi.org/10.1109/T-AFFC.2011.36.CrossRefGoogle Scholar
Lu, J. G., Quoidbach, J., Gino, F., Chakroff, A., Maddux, W. W., & Galinsky, A. D. (2017). The dark side of going abroad: How broad foreign experiences increase immoral behavior. Journal of Personality and Social Psychology, 112(1), 1.10.1037/pspa0000068CrossRefGoogle ScholarPubMed
Macizo, P., Bajo, T., & Paolieri, D. (2012). Language switching and language competition. Second Language Research, 28(2), 131–149.10.1177/0267658311434893CrossRefGoogle Scholar
Maddux, W. W., & Galinsky, A. D. (2009). Cultural borders and mental barriers: The relationship between living abroad and creativity. Journal of Personality and Social Psychology, 96(5), 1047.10.1037/a0014861CrossRefGoogle ScholarPubMed
Maddux, W. W., Lu, J. G., Affinito, S. J., & Galinsky, A. D. (2021). Multicultural experiences: A systematic review and new theoretical framework. Academy of Management Annals, 15(2), 345–376. https://doi.org/10.5465/annals.2019.0138.CrossRefGoogle Scholar
Meuter, R. F. I., & Allport, A. (1999). Bilingual language switching in naming: Asymmetrical costs of language selection. Journal of Memory and Language, 40(1), 25–40. https://doi.org/10.1006/jmla.1998.2602.CrossRefGoogle Scholar
Mosca, M., & de Bot, K. (2017). Bilingual language switching: Production vs. Recognition. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.00934.Google ScholarPubMed
Ohmae, K. (1990). The borderless world. Harper Business Press.Google Scholar
Olson, D. J. (2016). The gradient effect of context on language switching and lexical access in bilingual production. Applied PsychoLinguistics, 37(3), 725–756. https://doi.org/10.1017/S0142716415000223.CrossRefGoogle Scholar
Orfanidou, E., & Sumner, P. (2005). Language switching and the effects of orthographic specificity and response repetition. Memory & Cognition, 33(2), 355–369. https://doi.org/10.3758/BF03195323.CrossRefGoogle ScholarPubMed
Proverbio, A. M., Leoni, G., & Zani, A. (2004). Language switching mechanisms in simultaneous interpreters: An ERP study. Neuropsychologia, 42(12), 1636–1656. https://doi.org/10.1016/j.neuropsychologia.2004.04.013.CrossRefGoogle ScholarPubMed
Räling, R., Hanne, S., Schröder, A., Keßler, C., & Wartenburger, I. (2017). Judging the animacy of words: The influence of typicality and age of acquisition in a semantic decision task. Quarterly Journal of Experimental Psychology (2006), 70(10), 2094–2104. https://doi.org/10.1080/17470218.2016.1223704.CrossRefGoogle Scholar
Samanez-Larkin, G. R., Buckholtz, J. W., Cowan, R. L., Woodward, N. D., Li, R., Ansari, M. S., Arrington, C. M., Baldwin, R. M., Smith, C. E., Treadway, M. T., Kessler, R. M., & Zald, D. H. (2013). A thalamocorticostriatal dopamine network for psychostimulant-enhanced human cognitive flexibility. Biological Psychiatry, 74(2), 99–105. https://doi.org/10.1016/j.biopsych.2012.10.032.CrossRefGoogle Scholar
Sanders, A. F. (1997). A summary of resource theories from a behavioral perspective. Biological Psychology, 45(1–3), 5–18. https://doi.org/10.1016/S0301-0511(96)05220-9.CrossRefGoogle ScholarPubMed
Sparkman, D. J., Eidelman, S., & Blanchar, J. C. (2016). Multicultural experiences reduce prejudice through personality shifts in openness to experience. European Journal of Social Psychology, 46(7), 840–853. https://doi.org/10.1002/ejsp.2189.CrossRefGoogle Scholar
Spears, R., Haslam, S. A., & Jansen, R. (1999). The effect of cognitive load on social categorization in the category confusion paradigm. European Journal of Social Psychology, 29(5–6), 621–639. https://doi.org/10.1002/(SICI)1099-0992(199908/09)29:5/6%3c621::AID-EJSP96%3e3.0.CO;2-W.3.0.CO;2-W>CrossRefGoogle Scholar
Tadmor, C. T., Galinsky, A. D., & Maddux, W. W. (2012). Getting the most out of living abroad: Biculturalism and integrative complexity as key drivers of creative and professional success. Journal of Personality and Social Psychology, 103(3), 520–542. https://doi.org/10.1037/a0029360.CrossRefGoogle ScholarPubMed
Tadmor, C. T., Hong, Y., Chao, M. M., Wiruchnipawan, F., & Wang, W. (2012). Multicultural experiences reduce intergroup bias through epistemic unfreezing. Journal of Personality and Social Psychology, 103(5), 750–772. https://doi.org/10.1037/a0029719.CrossRefGoogle ScholarPubMed
Tadmor, C. T., Tetlock, P. E., & Peng, K. (2006). Biculturalism and integrative complexity: Testing the acculturation complexity model. Academy of Management Proceedings, 2006(1), C1–C5. https://doi.org/10.5465/ambpp.2006.22898587.CrossRefGoogle Scholar
Tenés, L. T. S., Weiner-Bühler, J. C., Volpin, L., Grob, A., Skoruppa, K., & Segerer, R. K. (2023). Language proficiency predictors of code-switching behavior in dual-language-learning children. Bilingualism: Language and Cognition, 26(5), 942–958. https://doi.org/10.1017/S1366728923000081.CrossRefGoogle Scholar
Thomas, M. S., & Allport, A. (2000). Language switching costs in bilingual visual word recognition. Journal of Memory and Language, 43(1), 44–66.10.1006/jmla.1999.2700CrossRefGoogle Scholar
Timmer, K., Christoffels, I. K., & Costa, A. (2019). On the flexibility of bilingual language control: The effect of language context. Bilingualism: Language and Cognition, 22(3), 555–568. https://doi.org/10.1017/S1366728918000329.CrossRefGoogle Scholar
Van Overschelde, J. P., Rawson, K. A., & Dunlosky, J. (2004). Category norms: An updated and expanded version of the Battig and Montague (1969) norms. Journal of Memory and Language, 50(3), 289–335. https://doi.org/10.1016/j.jml.2003.10.003.CrossRefGoogle Scholar
Von Studnitz, R. E., & Green, D. W. (2002a). Interlingual homograph interference in German–English bilinguals: Its modulation and locus of control. Bilingualism: Language and Cognition, 5(1), 1–23.10.1017/S1366728902000111CrossRefGoogle Scholar
Von Studnitz, R. E., & Green, D. W. (2002b). The cost of switching language in a semantic categorization task. Bilingualism: Language and Cognition, 5(3), 241–251. https://doi.org/10.1017/S1366728902003036.CrossRefGoogle Scholar
Wang, R., Fan, X., Liu, C., & Cai, Z. G. (2014). Cognitive control and word recognition speed influence the Stroop effect in bilinguals. International Journal of Psychology, 51(2), 93–101.10.1002/ijop.12115CrossRefGoogle ScholarPubMed
Zeelenberg, R., & Pecher, D. (2003). Evidence for long-term cross-language repetition priming in conceptual implicit memory tasks. Journal of Memory and Language, 49(1), 80–94. https://doi.org/10.1016/S0749-596X(03)00020-2.CrossRefGoogle Scholar
Figure 0

Figure 1. The switch costs of L2 were larger than those of L1 for unbalanced bilinguals. Arrows signify activation while squares denote inhibition. Solid lines and dashed lines represent L1 and L2, respectively.

Figure 1

Table 1. Means (SDs) of the language proficiency ratings in four language skills

Figure 2

Table 2. t-test results of the stimulus attributes

Figure 3

Table 3. Means, SDs and t-test results of the attributes of Chinese and American culture images

Figure 4

Figure 2. Experimental timeline for the revised version of the animacy judgment task.

Figure 5

Table 4. Mean RTs (ms) and SDs of the non-switch and switch trials for L1 and L2 in different priming conditions

Figure 6

Figure 3. The switch costs (RTs) of L1 and L2 in the Chinese cultural exposure condition, American cultural exposure condition, dual-cultural exposure condition and control condition. (A) Fixing different levels of language to explore the simple effects of cultural exposure context. (B) Fixing different levels of cultural exposure context to explore the simple effects of language. (C) The switch costs (accuracy rates) of L1 and L2 in the Chinese cultural exposure condition, American cultural exposure condition, dual-cultural exposure condition and control condition. (D) Positive correlation between Intercultural Sensitivity Scale (ISS) scores and L2 switch costs in the control condition. Note: * p < . 05. ** p < . 01. *** p < .001.

Figure 7

Table 5. Mean accuracy and SDs of the non-switch and switch trials for L1 and L2 in different priming conditions

Figure 8

Figure 4. (A) In the Chinese cultural exposure context, the incongruence between cultural image cues and L2 led to slower recognition speed of L2 and higher switch costs for L2. (B) Images were more strongly associated with L2 in the American cultural exposure context, which accelerated the recognition of L2 words in the identification system and decreased the switch costs for L2. Arrows signify activation while squares denote inhibition. Solid lines and dashed lines represent L1 and L2 influence, respectively. The red color signifies that the activation of L2 language node by L2 words was enhanced in the context of American culture. Meanwhile, the blue color indicates that the inhibition of L2 words by L1 language node was weakened in the same cultural context.