{"paper_id":"055a5e9a-e2cc-4d6a-84ad-c63333ec94e0","body_text":"Neural Evidence for Conflict Monitoring Advantage in Dai-Han Bilinguals: An ERP Study with the Flanker Task | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Neural Evidence for Conflict Monitoring Advantage in Dai-Han Bilinguals: An ERP Study with the Flanker Task Rui Yu, Jiemin Zhang, Fuhua Yang, Jiaci Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7008196/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Research on the bilingual advantage in cognitive control has yielded mixed results, particularly across diverse populations. This study examined whether Dai bilinguals in China demonstrate enhanced cognitive control compared to monolinguals. Participants completed a classic Eriksen Flanker task while both behavioral responses and EEG data were recorded. Analyses focused on reaction times, conflict effects, and the ERP components N2 (reflecting conflict monitoring) and P3 (reflecting attentional allocation). Although no significant group differences emerged in behavioral performance, bilinguals showed reduced N2 and increased P3 amplitudes relative to monolinguals, indicating more efficient neural conflict monitoring. No differences were observed in conflict or congruency sequence effects between the groups. These findings suggest a bilingual advantage in neural conflict monitoring, even in the absence of behavioral differences. This advantage was not lateralized and highlights the value of integrating behavioral and ERP measures to better understand bilingual cognitive processing in diverse cultural contexts. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Bilingual advantage Conflict monitoring Inhibitory control Conflict effect Congruency sequence effect Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction With the advancement of human civilization and increasing globalization, an ever-growing number of individuals have become bilingual. Throughout much of the twentieth century, however, bilingualism was discouraged by educators who believed that managing two languages hindered learning abilities and led to greater academic and intellectual deficits compared to monolingualism [1]. This view began to change with the study by Peal and Lambert, which demonstrated that bilingual individuals performed significantly better on both verbal and nonverbal measures of intelligence[2]. In the past two decades, there has been a surge of research investigating whether bilingualism confers a cognitive advantage. This interest has led to a proliferation of studies in psychology and education focusing on the relationship between bilingualism and cognitive control functions. Many of these studies have employed cognitive control tasks involving congruent (C) and incongruent (I) conditions, such as the Flanker, Simon, Stroop, and Attention Network Task (ANT). However, findings in this area remain inconsistent. Some studies report superior cognitive performance among bilinguals, supporting the bilingual advantage hypothesis[3–5]or that the advantages of bilingualism outweigh its disadvantages[6], while others have found no such evidence[7–9]. Therefore, further empirical evidence and theoretical discussion are required to determine whether bilingual experience truly confers cognitive advantages. The fundamental assumption underlying the bilingual advantage hypothesis is that bilinguals activate both languages simultaneously. When confronted with different communicative demands, they must resolve interlanguage conflict through a control mechanism that selects between the two linguistic systems. But what is the nature of this control mechanism? Two primary explanations have been proposed. One prominent account is the Inhibitory Control Theory[10], which posits that bilinguals must suppress activation of the non-target language during use of the target language. This constant practice is thought to enhance their inhibitory control compared to monolinguals. In empirical research, inhibitory control is typically measured by the difference in mean reaction times between conflict and non-conflict trial conditions—known as Conflict Effects (CE). Bilinguals are considered to possess superior inhibitory control because they generally exhibit smaller Conflict Effects than monolinguals. Another explanation is the Conflict Monitoring Theory [11]. According to this theory, the bilingual advantage may not stem from an enhanced ability to suppress interfering information, but rather from superior monitoring abilities in rapidly changing and complex tasks. This advantage likely arises from the need for bilinguals to consistently attend to and select the appropriate language for each communicative context. As a result, bilinguals may develop an improved capacity to monitor complex environments where both relevant and irrelevant stimuli are present. The theory also posits that specific brain regions, particularly the anterior cingulate cortex (ACC), are responsible for conflict monitoring. Two indicators are commonly used to measure monitoring function. The first is overall reaction time. Hilchey and Klein found that bilinguals exhibited shorter reaction times than monolinguals in both congruent and incongruent tasks, which was interpreted as reflecting bilinguals’ enhanced monitoring abilities[12]. The second indicator is the Congruency Sequence Effect (CSE), also known as the Gratton effect or conflict adaptation effect. According to Conflict Monitoring Theory, incongruent trials trigger greater ACC activation compared to congruent trials, which in turn increases attention to the conflict and reduces interference in subsequent trials. This phenomenon is reflected by shorter reaction times in inconsistent trials followed by inconsistent trials (iI) compared to inconsistent trials followed by congruent trials (cI), i.e., iI < cI; and in consistent trials followed by consistent trials (cC) compared to inconsistent trials followed by consistent trials (iC), i.e., cC < iC. Both effects may co-occur, as indicated by the finding that the conflict effect in consistent trials followed by consistent trials is greater than that in inconsistent trials followed by consistent trials, i.e., cI - cC > iI - iC. The CSE size is calculated as the difference between the conflict effects after consistent and inconsistent trials, i.e., CSE = (cI - cC) - (iI - iC)[13,14]. A larger CSE suggests that prior trial conditions have a greater influence on current performance, with slower attentional separation from previous trials[15]. The more effective conflict monitoring system of bilinguals may provide them with an advantage in conflict adaptation, as evidenced by a lower CSE [12]. Existing studies have predominantly focused on behavioral experiments, mainly examining measures such as reaction time and accuracy[16]. Compared to behavioral experiments alone, cognitive neuroscience methods are more sensitive and crucial for elucidating the complex relationship between bilingualism and cognitive control [17]. Although an increasing number of recent studies have employed cognitive neuroscience approaches to investigate bilingual advantages, research that systematically distinguishes and simultaneously analyzes both conflict monitoring and inhibitory control mechanisms remains limited [18]. In particular, there is a lack of studies that comprehensively examine all three indicators related to these two potential mechanisms. Globally, bilingualism research has predominantly focused on bilinguals with English as a second language, with minimal attention to ethnic minority groups. In recent years, the promotion of China's national lingua franca in ethnic regions has accelerated, leading to a significant increase in the number of Chinese-Chinese bilinguals among ethnic minorities. However, few empirical studies have investigated the impact of bilingual experiences on the cognitive development of ethnic minority groups. The Dai people, a unique ethnic minority in Yunnan, China, represent the most populous cross-border ethnic group in the country. The Dai language belongs to the Zhuang-Dong branch of the Sino-Tibetan language family, a distinct branch from Chinese, and is spoken by approximately 68 million people worldwide. Dai bilinguals are therefore highly representative of minority bilinguals. Thus, the present study aims to examine Dai college students as representatives of ethnic bilinguals, using the event-related potential (ERP) technique and the classical Flanker paradigm to explore the following research questions: (1) Does a bilingual advantage exist in a cognitive task involving conflicting information? (2) If a bilingual advantage exists, is it driven by inhibitory control or conflict monitoring? (3) If a conflict-monitoring advantage exists, does it arise from monitoring itself, or from the adaptation of monitoring to conflict? (4) Does the bilingual advantage correspond to a left hemisphere advantage, given that language predominantly shapes the left hemisphere of the brain? Methods Participants and Procedure A total of 48 participants participated in the experiment. One participant was excluded due to low accuracy in the behavioral task (below 75%), and two participants were excluded due to excessive EEG artifacts. The final valid sample consisted of 45 participants. All participants were Dai college students from Xishuangbanna Dai Autonomous Prefecture, Dehong Dai Jingpo Autonomous Prefecture, Gengma Dai Wa Autonomous County, Menglian Dai Lahu Wa Autonomous County, and Lincang, Yunnan Province, China, aged between 18 and 22 years. The monolingual group included 23 participants (16 females and 7 males, mean age = 19.52 ± 0.99), while the bilingual group included 22 participants (15 females and 7 males, mean age = 19.73 ± 1.12). The two groups did not differ significantly in terms of age ( t (43) = -0.652, p = .518) or gender distribution (χ²(1) = .010, p = .920). Participants in the monolingual group reported proficiency only in Chinese, with no listening skills in Dai. In contrast, participants in the bilingual group reported Dai as their first language and Chinese as their second language, with proficiency in listening to both languages. All bilingual participants were early acquirers of their second language, with the mean age of second language acquisition being 4.59 years (SD = 1.764, range 2–8 years). The bilingual group had a significantly higher age of Chinese language acquisition than the monolingual group ( t (43) = 7.113, p = .000). Language proficiency was assessed using a five-point self-assessment scale, where 1 indicated no proficiency and 5 indicated full proficiency. There was no significant difference in Chinese language proficiency between the monolingual and bilingual groups ( t (43) = 1.185, p = .243), but the bilingual group exhibited significantly higher Dai language proficiency than the monolingual group ( t (43) = 14.864, p = .000). Since family socioeconomic status (SES) has been shown to influence cognitive performance[19,20], the study also considered participants' SES. Family SES was assessed using three indicators: parents' education level, parents' occupation, and family income. Given that many college students are unaware of their family income, this study focused on parents' education level (1. Primary school and below; 2. Primary school; 3. High school or junior college; 4. College; and 5. Graduate school and above) and occupation. The standardized scores for parents' education level and occupation were summed to represent the participants' family SES. No significant difference in SES scores was found between the monolingual and bilingual groups ( t (43) = -0.865, p = .392). Relevant background information is summarized in Table 1. Table 1 Participant background information monolingual bilingualism t N 23 22 - Age 19.52（0.99） 19.73（1.12） -0.652 Age of acquisition of Dai language - 1.59（0.854） - Age of acquisition of Chinese language 1.65（0.885） 4.59（1.764） 7.113 *** Proficiency in Dai language 1.09（0.294） 4.08（0.900） 14.864 *** Proficiency in Chinese 4.87（0.344） 4.73（0.456） 1.185 SES 0.422（3.13） -0.442（3.56） -0.865 *** represents p < .001 All participants were right-handed and had normal or corrected-to-normal vision. The study complied with relevant ethical standards and was approved by the Ethics Committee of Yunnan University of Chinese Medicine. After providing informed consent, participants completed the Flanker task following the collection of background information. The entire experiment lasted approximately 40 minutes, and participants received a reward upon completion. Flanker Task The experimental program was developed using E-Prime 2.0 software, and stimuli were presented at the center of a 19-inch computer screen positioned approximately 50 cm from the participant. The screen refresh rate was 75 Hz. The task was a classic arrow-based Flanker paradigm, using five arrows (< or >) displayed in 18-point Verdana font. There were four stimulus types: the congruent condition (C), in which all arrows pointed in the same direction (e.g., >>>>> or <<<<<), and the incongruent condition (I), in which the central arrow pointed in the opposite direction from the flanking arrows (e.g., >><>> or <<><<). Participants were instructed to ignore the flanking arrows and respond only to the direction of the central arrow. If the central arrow pointed left (<), participants pressed the \"S\" key with their left index finger; if it pointed right (>), they pressed the \"K\" key with their right index finger. Responses were required within 2000 ms, after which the program automatically proceeded to the next stimulus. The experiment consisted of nine blocks: one practice block and eight formal blocks. The practice block included 16 trials—8 congruent and 8 incongruent. Participants proceeded to the formal experiment only after achieving an accuracy rate of at least 85% in the practice block. In the formal phase, each of the four stimulus types was presented 64 times, resulting in a total of 256 trials. Because the congruency sequence effect (CSE) involves the influence of the previous trial on the current one, trials were pseudo-randomized to ensure a balanced number of transitions between trial types and to precisely define trial categories. Excluding the first trial of each block (which had no preceding trial), the remaining 248 trials included 62 trials in which both the previous and current trials were congruent (cC), 62 trials in which the previous trial was congruent and the current trial was incongruent (cI), 63 trials in which the previous trial was incongruent and the current trial was congruent (iC), and 61 trials in which both the previous and current trials were incongruent (iI). All 256 trials were presented in a predetermined order and divided into eight blocks of 32 trials each. After each block, participants were given a rest period, the duration of which they could control themselves. The detailed experimental procedure is illustrated in Figure 1. The example in the procedure illustrates a case in which the first stimulus is an incongruent type (I) and the second stimulus is a congruent type (C). Considering the sequential relationship between trials, the second stimulus is thus categorized as an iC trial. Data Acquisition and Preprocessing Behavioral data, including participants' keypress reaction times and error rates, were recorded using E-Prime 2.0. EEG data were recorded using a 64-channel EEG amplifier system (Brain Products GmbH, Germany), with electrodes positioned according to the international 10–20 system. The online reference was set at FCz, and the ground electrode was located at the midpoint between FPz and Fz. Horizontal electrooculogram (HEOG) signals were recorded using an electrode placed at the outer canthus of the right eye. The data were sampled at 500 Hz and recorded with an online band-pass filter ranging from 0.01 to 70 Hz. Scalp electrode impedances were maintained below 5 kΩ throughout the recording. Offline EEG processing was performed using EEGLAB 13.0 running on MATLAB R2013a. Continuous EEG data were first re-referenced to the average of the left and right mastoid electrodes. Independent Component Analysis (ICA) was applied to the continuous data to identify and remove components associated with ocular artifacts, including eye blinks. Subsequently, the data were filtered using a 0.01–30 Hz finite impulse response (FIR) band-pass filter with a transition bandwidth of approximately 0.5 Hz and a roll-off rate of ~12 dB/octave. Epochs were extracted from −200 ms to 800 ms relative to stimulus onset, and baseline correction was performed using the pre-stimulus interval (−200 to 0 ms). Epochs were excluded from averaging if the amplitude exceeded ±80 μV in any EEG channel. The ERP components of interest were N2 and P3[21]. The N2, a negative deflection associated with early conflict detection[22,23], typically exhibits a fronto-central distribution [24]. To examine hemispheric differences, six frontal electrode sites were selected: left frontal (F1, F3, F5), frontal midline (Fz), and right frontal (F2, F4, F6). The P3 component, a positive wave linked to attentional resource allocation[25], is primarily observed in the central-parietal region[26,27]. For P3 analysis, six central-parietal electrodes were selected: left central-parietal (CP1, CP3, CP5), central-parietal midline (CPz), and right central-parietal (CP2, CP4, CP6). To avoid bias in time-window selection, time windows for the N2 and P3 components were determined based on the grand average ERP waveforms averaged across all participants and conditions, as recommended by Kappenman and Luck[28]. The time windows were defined around clear peaks of the grand average global field power (GFP), resulting in windows of 280–360 ms for N2 and 360–500 ms for P3. Behavioral reaction times and EEG data analyses included only correct trials, excluding practice trials, the first trial of each block, trials with reaction times below 200 ms or above 1000 ms, trials outside 2.5 standard deviations from the mean reaction time, and trials immediately following errors. Statistical Analyses Behavioral and EEG data were analyzed using SPSS 22.0. For EEG data, a four-way repeated measures ANOVA was conducted with factors of Group (monolingual, bilingual), Previous Trial Consistency (congruent [c], incongruent [i]), Current Trial Consistency (congruent [C], incongruent [I]), and Brain Region (left, center, right). The dependent variables were the mean amplitudes of the N2 and P3 components, analyzed separately. Previous trial consistency, current trial consistency, and brain region were treated as within-subject factors, while group was a between-subject factor. The Greenhouse-Geisser correction was applied to adjust degrees of freedom when the sphericity assumption was violated. Multiple comparisons were adjusted using the Bonferroni correction. Statistical significance was set at α = 0.05. Results Behavioral Experiments-Overall Response Time Analysis A 2 (Group: monolingual, bilingual) × 2 (Previous Trial Consistency: congruent [c], incongruent [i]) × 2 (Current Trial Consistency: congruent [C], incongruent [I]) three-way repeated measures ANOVA was conducted with response time as the dependent variable. Group served as a between-subjects factor, while previous and current trial consistencies were within-subjects factors. Accuracy rates across all conditions exceeded 95%, so no further analyses of accuracy were performed. Analysis of reaction times (see Figure 1) revealed no significant main effect of Group, F (1, 43) = 0.154, p = .697, but a significant main effect of Current Trial Consistency, F (1, 43) = 493.237, p < .001, η p ² = 0.920, with longer reaction times observed in incongruent compared to congruent trials. Neither the Group × Previous Trial Consistency interaction, the Group × Current Trial Consistency interaction, nor the three-way interaction among Group, Previous Trial Consistency, and Current Trial Consistency reached significance (all F s < 1). A significant interaction between Previous Trial Consistency and Current Trial Consistency was observed, F (1, 43) = 16.019, p < .001, η p ² = 0.271. Simple effects analyses indicated that when the previous trial was congruent, reaction times were significantly longer for incongruent current trials (cI) than for congruent current trials (cC) ( p < .001). Similarly, when the previous trial was incongruent, reaction times were longer for incongruent current trials (iI) than for congruent current trials (iC) (p < .001). Moreover, reaction times were significantly longer for incongruent current trials following congruent previous trials (iI > iC, p < .001). When the current trial was congruent, reaction times were longer if the previous trial was incongruent compared to congruent (iC > cC, p = .020). Conversely, when the current trial was incongruent, reaction times were longer if the previous trial was congruent rather than incongruent (cI > iI, p = .001). Behavioral Experiments- Analysis of Conflict Effects To complement the omnibus ANOVA, we additionally calculated two widely used indices of conflict processing: the conflict effect (CE = incongruent − congruent) and the congruency sequence effect (CSE = (cI − cC) − (iI − iC)). These metrics were computed separately for each level of the preceding trial condition. While the omnibus ANOVA already revealed the relevant interaction effects, these additional indices allow for a more direct and interpretable assessment of both immediate conflict costs and dynamic conflict adaptation. This approach also facilitates more precise comparisons with previous findings in the literature. A similar analytical rationale was applied to the ERP data, where condition-wise comparisons were used to evaluate neural correlates of conflict processing. For the sake of clarity and brevity, this logic will not be repeated in the subsequent ERP sections. A 2 (Group: monolingual, bilingual) × 2 (Conflict Effect Type: post-consistent trial, post-inconsistent trial) repeated measures ANOVA on the conflict effect magnitude in response time (CE = I − C) revealed no significant main effect of Group, F (1, 43) = 0.246, p = .622. However, the main effect of Conflict Effect Type was significant, F (1, 43) = 16.019, p < .001, ηp² = 0.271, indicating that the conflict effect following consistent trials was significantly larger than that following inconsistent trials. The interaction between Group and Conflict Effect Type was not significant, Fs < 1. Behavioral Experiments- Analysis of Congruency Sequence Effect An independent samples t-test was conducted to compare the congruency sequence effect (CSE = (cI − cC) − (iI − iC)) on response times between monolingual and bilingual participants. Results showed no significant difference in CSE magnitude between the groups, t (43) = −0.048, p = .962. The results for both conflict effects and the congruency sequence effect are illustrated in Figure 3. Electroencephalography-N2 Component Average Amplitude Analysis A four-way repeated measures ANOVA with factors of Group (2: monolingual, bilingual), Previous Trial Consistency (2: congruent [c], incongruent [i]), Current Trial Consistency (2: congruent [c], incongruent [i]), and Brain Region (3: left, centre, right) was conducted on the mean amplitude of the N2 component. Previous trial consistency, current trial consistency, and brain region were within-subject variables, and group was a between-subject variable. Results revealed a significant main effect of Group, F (1, 43) = 9.278, p = .004, ηp² = 0.177, with the monolingual group exhibiting significantly larger N2 amplitudes than the bilingual group. The main effect of Current Trial Consistency was also significant, F (1, 43) = 4.779, p = .034, ηp² = 0.100, showing greater N2 amplitude in the current trial incongruent condition compared to the congruent condition. Additionally, there was a significant main effect of Brain Region, F (2, 86) = 5.442, p = .006, ηp² = 0.112, with significantly larger amplitudes observed in the central region than in the right hemisphere ( p = .003), as shown in Figure 6a. A significant interaction between Previous Trial Consistency and Current Trial Consistency was found, F (1, 43) = 4.440, p = .041, ηp² = 0.094. Simple effects analyses indicated that when the previous trial was congruent, the N2 amplitude was significantly larger in the current trial incongruent condition (cI) compared to the congruent condition (cC) ( p = .005), i.e., cI > cC. However, when the previous trial was incongruent, no significant difference was found between current incongruent (iI) and congruent (iC) conditions ( p = .390). In the current incongruent condition, the N2 amplitude was significantly greater following a previous congruent trial (cI) than following a previous incongruent trial (iI) ( p = .027), i.e., cI > iI. Conversely, in the current congruent condition, the difference between previous congruent (cC) and previous incongruent (iC) trials was not significant ( p = .471). All other main effects and interactions were nonsignificant ( F s < 2.6). See Figure 4 for N2 amplitude results and Figure 7a for topographical maps. Conflict Effects Size Analysis A 2 (Group: monolingual, bilingual) × 2 (Conflict Effect Type: post-consistent trial, post-inconsistent trial) × 3 (Brain Region: left, centre, right) repeated measures ANOVA was conducted on the magnitude of conflict effects in the N2 component amplitudes. Results showed a significant main effect of Conflict Effect Type, F (1, 43) = 4.440, p = .041, ηp² = 0.094, with larger conflict effects following consistent trials than inconsistent trials. No significant main effects of Group or Brain Region, nor any significant interactions, were found (all F s < 2.6). The conflict effect sizes for the N2 component are illustrated in Figure 5. Congruency Sequence Effect Size Analysis A 2 (Group: monolingual, bilingual) × 3 (Brain Region: left, centre, right) repeated measures ANOVA was performed on the congruency sequence effect (CSE) magnitude in the N2 component amplitude. Neither the main effects of Group and Brain Region nor their interaction were significant (all F s < 2.6). The magnitude of the CSE on the N2 component is displayed in Figure 5, with a corresponding topographical map shown in Figure 9a. Electroencephalography-P3 Component Average Amplitude Analysis A four-factor repeated measures ANOVA with 2 (Group: monolingual, bilingual) × 2 (Previous Trial Consistency: c, i) × 2 (Current Trial Consistency: C, I) × 3 (Brain Region: left, centre, right) was conducted, using the mean amplitude of the P3 component as the dependent variable. The results revealed a significant main effect of Group, F (1, 43) = 8.570, p = .005, ηp² = 0.166, with the monolingual group showing significantly smaller P3 amplitudes than the bilingual group. The main effect of Brain Region was also significant, F (2, 86) = 10.346, p < .001, ηp² = 0.194, with amplitudes in the central region significantly larger than those in both the left ( p < .001) and right (p = .038) regions. No significant difference was found between the left and right regions. All other main effects and interactions were nonsignificant ( F s < 2.4). See Figure 6 for P3 amplitudes and Figure 7b for the topographical map. Conflict Effect Size Analysis A repeated-measures ANOVA with 2 (Group: monolingual, bilingual) × 2 (Conflict Effect Type: post-consistent trial, post-inconsistent trial) × 3 (Brain Region: left, centre, right) was conducted on the amplitude of the P3 component. The analysis revealed no significant main effects or interactions, except for a significant interaction between Conflict Effect Type and Brain Region, F (2, 86) = 7.434, p = .001, ηp² = 0.147. Simple effects analyses indicated that after inconsistent trials, the conflict effect was significantly greater in the right brain region than in the left ( p = .040). After consistent trials, the conflict effect was slightly greater in the left brain region than in the right, with a borderline significant difference ( p = .053). The conflict effect amplitudes for the P3 component are shown in Figure 8. Congruency Sequence Effect (CSE) Size Analysis A repeated-measures ANOVA with 2 (Group: monolingual, bilingual) × 3 (Brain Region: left, centre, right) was conducted on the amplitude of the P3 component CSE. The analysis showed a significant main effect of Brain Region, F (2, 86) = 7.435, p = .001, ηp² = 0.147. Post hoc comparisons revealed that the CSE amplitude was significantly larger in the left brain region than in the right ( p = .001), whereas the central region did not significantly differ from either the left or right regions. The main effect of Group was not significant, F (1, 43) = 1.825, p = .184, nor was the Group × Brain Region interaction, F (2, 86) = 0.473, p = .625. The CSE amplitude results for the P3 component are displayed in Figure 8, with topographical maps shown in Figure 9b. Discussion Conflict monitoring-Overall response time/amplitude Overall reaction time is a traditional behavioral indicator of conflict monitoring. In the present study, no significant differences in overall reaction time were found between bilinguals and monolinguals, suggesting that bilinguals did not exhibit an advantage in conflict monitoring at the behavioral level. This result aligns with previous findings[29]. Some researchers have noted that the influence of bilingual experience on conflict monitoring may not be readily observable through behavioral outcomes alone, and that neurophysiological indicators may offer deeper insights into the relationship between bilingualism and cognitive control[30]. Indeed, our ERP findings revealed between-group differences in both the N2 and P3 components. Specifically, bilinguals exhibited smaller N2 amplitudes than monolinguals under both congruent and incongruent conditions. This finding is consistent with results reported by Kousaie et al. [23]. The N2 component, an event-related potential associated with conflict monitoring and cognitive control, typically reflects the extent of conflict detection, with greater N2 amplitudes indicating greater engagement of monitoring processes[31]. Thus, the smaller N2 amplitudes observed in bilinguals may reflect more efficient language processing and better regulation of cognitive resources, especially in switching between languages, resulting in reduced cognitive load during task performance. This interpretation is supported by Lamm et al., who found that N2 amplitude decreases from childhood to adolescence, reflecting the maturation of cognitive control[32]. Smaller N2 amplitudes have been associated with better performance on independent executive function tasks. Additionally, bilinguals have been shown to exhibit reduced activation in the anterior cingulate cortex (ACC) compared to monolinguals, which corresponds to lower N2 amplitudes and suggests more efficient ACC engagement[33]. Yeung and Cohen also noted that increased N2 amplitude may reflect enhanced processing of task-irrelevant information[22]. Collectively, these findings support the interpretation that bilinguals’ reduced N2 amplitudes reflect more efficient conflict monitoring mechanisms at the neural level. Regarding the P3 component, the mean amplitude across the two groups exhibited a pattern opposite to that observed for the N2 component: bilinguals showed significantly larger P3 amplitudes than monolinguals. The P3 is considered a reliable neural marker of attentional resource allocation, with greater amplitudes reflecting increased attention engagement[25]. Thus, the enhanced P3 amplitudes observed in bilinguals suggest that they recruit more cognitive resources when performing cognitive tasks. This finding is consistent with previous research. For instance, Kousaie et al., using Simon, Stroop, and Flanker tasks, also reported that bilinguals exhibited greater P3 amplitudes than monolinguals, indicating heightened attentional engagement across a range of conflict monitoring paradigms[34]. Although bilinguals and monolinguals performed similarly at the behavioral level, neurophysiological data suggest distinct underlying mechanisms. Bilinguals exhibited reduced N2 amplitudes—possibly reflecting more efficient conflict monitoring and reduced need for anterior cingulate cortex (ACC) engagement—yet showed increased P3 amplitudes, indicating greater attentional allocation. Together, these findings imply that bilinguals may be more efficient in resolving conflict and mobilizing attentional resources. Importantly, these results also suggest that Dai-Chinese bilinguals demonstrate similar cognitive advantages and underlying mechanisms as bilinguals whose second language is English. This supports the view that the cognitive benefits of bilingualism are not restricted to specific language pairs or cultural contexts, but rather represent a universal phenomenon. Conflict monitoring- Congruency Sequence Effects The congruency sequence effect (CSE) is another important index of conflict monitoring. In the present study, analysis of conflict effect size revealed that the magnitude of conflict effects was greater following congruent trials than after incongruent trials. This behavioral pattern reflects a serial consistency effect, commonly referred to as the conflict adaptation effect. It suggests that individuals dynamically adjust their attentional strategies based on the congruency of the previous trial. Specifically, when the preceding trial is incongruent, individuals are more likely to engage in conflict monitoring, which reduces the conflict effect on the subsequent trial. The conflict adaptation effect reflects experience-driven modulation of attention in response to cognitive conflict. In EEG results, this pattern was observed on the N2 component: the conflict effect was significantly larger following congruent than incongruent trials, indicating that the neural mechanism for conflict monitoring is sensitive to sequential congruency. However, this pattern was not observed on the P3 component, where the conflict effect did not differ significantly between post-congruent and post-incongruent trials. This suggests that while conflict adaptation affects monitoring processes (as indexed by the N2), it may not influence attentional resource allocation (as indexed by the P3). The CSE is thought to reflect an individual's ability to adaptively regulate attention and cognitive control in response to previous conflict. Grundy et al. reported that bilinguals exhibited a smaller conflict adaptation effect than monolinguals, despite no group differences in overall reaction times[15]. They interpreted this as evidence that bilinguals are better at dissociating their attentional responses to congruent versus incongruent stimuli. However, the present study found no significant group differences in CSE magnitude, either in behavioral measures or in EEG components. This aligns with previous research by Goldsmith and Morton, who also found no differences between bilingual and monolingual adults using the Flanker task[35]. Similarly, studies using the size congruency task, Simon task, and Attention Network Task (ANT) have reported no significant group differences in conflict adaptation effects[16,36] . Taken together, our results suggest that bilinguals do not show an advantage over monolinguals in terms of conflict adaptation or in the dynamic adjustment of cognitive resources. Although bilinguals demonstrated a monitoring advantage—as evidenced by differences in N2 and P3 amplitudes—this advantage appears limited to the conflict detection process itself and does not extend to the adaptive modulation of conflict monitoring. Suppression Control The conflict effect refers to the negative influence of conflicting information on task performance during cognitive processing. Behaviorally, it manifests as increased reaction times in incongruent conditions compared to congruent ones. In the present study, participants responded significantly faster in congruent trials than in incongruent trials, indicating the presence of a classic conflict effect. When considering the congruency relationship between the preceding and current trials, response times in the cC condition (congruent preceded by congruent) were significantly shorter than in the cI condition (incongruent preceded by congruent), and response times in the iC condition (congruent preceded by incongruent) were also shorter than those in the iI condition (incongruent preceded by incongruent). These results indicate that conflict effects were observed after both congruent and incongruent trials, demonstrating the stability of this effect. Consistent with the behavioral findings, the EEG results showed that N2 amplitudes were significantly larger in incongruent trials compared to congruent trials, suggesting the presence of conflict effects at the neural level as well. Specifically, when the preceding trial was congruent, a significant conflict effect was observed on the N2 component of the current trial. When the preceding trial was incongruent, although the difference did not reach statistical significance, the N2 amplitude in the incongruent condition was still numerically larger than in the congruent condition, reflecting a trend consistent with conflict processing. Conflict effect size—defined as the difference in mean reaction times or EEG amplitudes between incongruent and congruent trials—is commonly used as an index of inhibitory control. A smaller conflict effect size typically indicates stronger inhibitory control. Some studieshave reported that bilinguals exhibit smaller conflict effects, suggesting a greater ability to inhibit irrelevant or conflicting information compared to monolinguals[3,37,38] . However, the present study found no significant differences in conflict effect size between bilinguals and monolinguals in behavioral outcomes. This finding is consistent with previous studies, all of which reported no significant association between bilingual experience and conflict effect sizes in reaction time measures using the Flanker or Stroop tasks[13,15,39]. Similarly, EEG results showed no significant differences between groups in conflict effect size on either the N2 or P3 components. These converging findings suggest that, contrary to some prior claims, bilinguals may not exhibit a general advantage in inhibitory control as measured by conflict effect size. Differences in Brain Regions It is well known that for right-handed individuals, the dominant hemisphere is typically the left hemisphere of the brain. Language functions are predominantly controlled by this dominant hemisphere, which is why the left hemisphere is often referred to as the “language brain”. Because bilinguals receive more extensive training in language use than monolinguals, Rodriguez et al. proposed that bilinguals engage more language-related control areas in the left hemisphere, such as the left caudate nucleus and the left inferior frontal gyrus (LIFG)[40]. Other studies have also found structural differences, with bilinguals showing a thicker cortex in the LIFG[41] and the left middle frontal gyrus, compared to monolinguals[36]. These findings support the notion that the bilingual advantage is primarily associated with left hemisphere regions. However, in the present study, no significant differences between bilinguals and monolinguals were observed in any of the EEG components of interest—including waveform amplitudes, conflict effects, or congruency sequence effects (CSE)—with respect to brain region activation. This suggests that bilinguals did not exhibit greater activation in left hemisphere regions compared to monolinguals. Furthermore, the current study found no significant differences between the left and right hemispheres in the amplitudes of either the N2 or P3 components. The largest amplitudes were observed in the midline electrode sites, indicating that both monolingual and bilingual participants may engage bilateral brain regions cooperatively during cognitive processing. An interesting additional finding was that, for the P3 component, the magnitude of the CSE was significantly greater in the left hemisphere compared to the right. This could suggest that the right hemisphere may exhibit better conflict adaptation in terms of attentional resource allocation. However, this interpretation remains speculative and should be verified through more precise neuroimaging techniques and further empirical research. Conclusions This study used the Flanker task and ERP techniques to compare cognitive control between Dai–Han bilinguals and monolinguals. Results showed a bilingual advantage in conflict monitoring, reflected in reduced N2 and increased P3 amplitudes, suggesting more efficient conflict detection and attentional allocation. However, no significant group differences were found in conflict adaptation (CSE) or inhibitory control (conflict effect size), indicating that the advantage is specific to monitoring processes.No clear left-hemisphere dominance was observed; instead, both groups showed bilateral activation, though the left hemisphere exhibited a stronger CSE in P3 amplitude. This may imply lateralized attentional adaptation, warranting further research. Overall, findings support a selective bilingual advantage in monitoring efficiency. These results, based on Dai–Han bilinguals, expand current understanding by showing that such advantages are not limited to Indo-European language contexts. Future studies should employ neuroimaging tools like fMRI or MEG to further explore the neural basis and developmental trajectory of bilingual cognitive advantages. Declarations Acknowledgements The authors wish to thank all of the participants involved in this study. Author Contributions Conceptualization, R.Y. and F.Y.; methodology, J.L.; validation, J.Z.; formal analysis, R.Y. and J.L.; investigation, R.Y. and J.Z.; resources, F.Y.; data curation, J.Z.; writing—original draft preparation, R.Y. and J.Z.; writing—review and editing, R.Y. and F.Y.; visualization, J.L. supervision, F.Y. and J.L.; project administration, J.L.; funding acquisition, F.Y.. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the Yunnan Provincial Department of Education Scientific Research Fund, grant number 2024J0412, and the Joint Special Key Project of Yunnan Federation of Social Sciences and Yunnan University of Chinese Medicine, grant number LHZX202403. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki. Approval was granted by the Institutional Ethics Committee of Yunnan University of Chinese Medicine (Project Code: XXLW-2025-002; Date of Approval: 16 January 2025). Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Conflicts of Interest The authors declare no conflict of interest. References Bhatia, T. K. & Ritchie, W. C. The Handbook of Bilingualism and Multilingualism; John Wiley & Sons, ; ISBN 1-118-33241-5. (2012). Peal, E. & Lambert, W. E. 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Neural Correlates of Cognitive Control in Childhood and Adolescence: Disentangling the Contributions of Age and Executive Function. Neuropsychologia 44 , 2139–2148. 10.1016/j.neuropsychologia.2005.10.013 (2006). Abutalebi, J. et al. Bilingualism Tunes the Anterior Cingulate Cortex for Conflict Monitoring. Cereb. Cortex . 22 , 2076–2086. 10.1093/cercor/bhr287 (2012). Kousaie, S., Phillips, N. A. A. & Behavioural and Electrophysiological Investigation of the Effect of Bilingualism on Aging and Cognitive Control. Neuropsychologia 2017 , 94 , 23–35, 10.1016/j.neuropsychologia.2016.11.013 Goldsmith, S. F. et al. Sequential Congruency Effects in Monolingual and Bilingual Adults: A Failure to Replicate Grundy. Front. Psychol. 2018, 9, 2476, (2017). 10.3389/fpsyg.2018.02476 Goldsmith, S. F. et al. No Bilingual Advantage in Children’s Attentional Disengagement: Congruency and Sequential Congruency Effects in a Large Sample of Monolingual and Bilingual Children. J. Exp. Child. Psychol. 233 , 105692. 10.1016/j.jecp.2023.105692 (2023). Bialystok, E., Craik, F. I., Klein, R., Viswanathan, M. & Bilingualism Aging, and Cognitive Control: Evidence from the Simon Task. Psychol. Aging 2004 , 19 , 290, 10.1037/0882-7974.19.2.290 Bialystok, E., Craik, F. & Luk, G. Cognitive Control and Lexical Access in Younger and Older Bilinguals. J. Exp. Psychol. Learn. Mem. Cogn. 34 , 859–873. 10.1037/0278-7393.34.4.859 (2008). Paap, K. R. & Greenberg, Z. I. There Is No Coherent Evidence for a Bilingual Advantage in Executive Processing. Cognit Psychol. 66 , 232–258. 10.1016/j.cogpsych.2012.12.002 (2013). Rodríguez-Pujadas, A. et al. Bilinguals Use Language-Control Brain Areas More than Monolinguals to Perform Non-Linguistic Switching Tasks. PLoS One . 8 , e73028. 10.1371/journal.pone.0073028 (2013). Klein, D., Mok, K., Chen, J. K. & Watkins, K. E. Age of Language Learning Shapes Brain Structure: A Cortical Thickness Study of Bilingual and Monolingual Individuals. Brain Lang. 131 , 20–24. 10.1016/j.bandl.2013.05.014 (2014). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Sep, 2025 Reviews received at journal 03 Sep, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviews received at journal 04 Aug, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers invited by journal 24 Jul, 2025 Editor assigned by journal 24 Jul, 2025 Editor invited by journal 30 Jun, 2025 Submission checks completed at journal 30 Jun, 2025 First submitted to journal 30 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Advantage in Dai-Han Bilinguals: An ERP Study with the Flanker Task\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eWith the advancement of human civilization and increasing globalization, an ever-growing number of individuals have become bilingual. Throughout much of the twentieth century, however, bilingualism was discouraged by educators who believed that managing two languages hindered learning abilities and led to greater academic and intellectual deficits compared to monolingualism\\u0026nbsp;[1]. This view began to change with the study by Peal and Lambert, which demonstrated that bilingual individuals performed significantly better on both verbal and nonverbal measures of intelligence[2].\\u003c/p\\u003e\\n\\u003cp\\u003eIn the past two decades, there has been a surge of research investigating whether bilingualism confers a cognitive advantage. This interest has led to a proliferation of studies in psychology and education focusing on the relationship between bilingualism and cognitive control functions. Many of these studies have employed cognitive control tasks involving congruent (C) and incongruent (I) conditions, such as the Flanker, Simon, Stroop, and Attention Network Task (ANT). However, findings in this area remain inconsistent. Some studies report superior cognitive performance among bilinguals, supporting the bilingual advantage hypothesis[3\\u0026ndash;5]or that the advantages of bilingualism outweigh its disadvantages[6], while others have found no such evidence[7\\u0026ndash;9]. Therefore, further empirical evidence and theoretical discussion are required to determine whether bilingual experience truly confers cognitive advantages.\\u003c/p\\u003e\\n\\u003cp\\u003eThe fundamental assumption underlying the bilingual advantage hypothesis is that bilinguals activate both languages simultaneously. When confronted with different communicative demands, they must resolve interlanguage conflict through a control mechanism that selects between the two linguistic systems. But what is the nature of this control mechanism? Two primary explanations have been proposed.\\u003c/p\\u003e\\n\\u003cp\\u003eOne prominent account is the Inhibitory Control Theory[10], which posits that bilinguals must suppress activation of the non-target language during use of the target language. This constant practice is thought to enhance their inhibitory control compared to monolinguals. In empirical research, inhibitory control is typically measured by the difference in mean reaction times between conflict and non-conflict trial conditions\\u0026mdash;known as Conflict Effects (CE). Bilinguals are considered to possess superior inhibitory control because they generally exhibit smaller Conflict Effects than monolinguals.\\u003c/p\\u003e\\n\\u003cp\\u003eAnother explanation is the Conflict Monitoring Theory\\u0026nbsp;[11]. According to this theory, the bilingual advantage may not stem from an enhanced ability to suppress interfering information, but rather from superior monitoring abilities in rapidly changing and complex tasks. This advantage likely arises from the need for bilinguals to consistently attend to and select the appropriate language for each communicative context. As a result, bilinguals may develop an improved capacity to monitor complex environments where both relevant and irrelevant stimuli are present. The theory also posits that specific brain regions, particularly the anterior cingulate cortex (ACC), are responsible for conflict monitoring.\\u003c/p\\u003e\\n\\u003cp\\u003eTwo indicators are commonly used to measure monitoring function. The first is overall reaction time. Hilchey and Klein found that bilinguals exhibited shorter reaction times than monolinguals in both congruent and incongruent tasks, which was interpreted as reflecting bilinguals\\u0026rsquo; enhanced monitoring abilities[12]. The second indicator is the Congruency Sequence Effect (CSE), also known as the Gratton effect or conflict adaptation effect. According to Conflict Monitoring Theory, incongruent trials trigger greater ACC activation compared to congruent trials, which in turn increases attention to the conflict and reduces interference in subsequent trials. This phenomenon is reflected by shorter reaction times in inconsistent trials followed by inconsistent trials (iI) compared to inconsistent trials followed by congruent trials (cI), i.e., iI \\u0026lt; cI; and in consistent trials followed by consistent trials (cC) compared to inconsistent trials followed by consistent trials (iC), i.e., cC \\u0026lt; iC. Both effects may co-occur, as indicated by the finding that the conflict effect in consistent trials followed by consistent trials is greater than that in inconsistent trials followed by consistent trials, i.e., cI - cC \\u0026gt; iI - iC. The CSE size is calculated as the difference between the conflict effects after consistent and inconsistent trials, i.e., CSE = (cI - cC) - (iI - iC)[13,14]. A larger CSE suggests that prior trial conditions have a greater influence on current performance, with slower attentional separation from previous trials[15]. The more effective conflict monitoring system of bilinguals may provide them with an advantage in conflict adaptation, as evidenced by a lower CSE\\u0026nbsp;[12].\\u003c/p\\u003e\\n\\u003cp\\u003eExisting studies have predominantly focused on behavioral experiments, mainly examining measures such as reaction time and accuracy[16]. Compared to behavioral experiments alone, cognitive neuroscience methods are more sensitive and crucial for elucidating the complex relationship between bilingualism and cognitive control\\u0026nbsp;[17]. Although an increasing number of recent studies have employed cognitive neuroscience approaches to investigate bilingual advantages, research that systematically distinguishes and simultaneously analyzes both conflict monitoring and inhibitory control mechanisms remains limited\\u0026nbsp;[18]. In particular, there is a lack of studies that comprehensively examine all three indicators related to these two potential mechanisms.\\u003c/p\\u003e\\n\\u003cp\\u003eGlobally, bilingualism research has predominantly focused on bilinguals with English as a second language, with minimal attention to ethnic minority groups. In recent years, the promotion of China\\u0026apos;s national lingua franca in ethnic regions has accelerated, leading to a significant increase in the number of Chinese-Chinese bilinguals among ethnic minorities. However, few empirical studies have investigated the impact of bilingual experiences on the cognitive development of ethnic minority groups. The Dai people, a unique ethnic minority in Yunnan, China, represent the most populous cross-border ethnic group in the country. The Dai language belongs to the Zhuang-Dong branch of the Sino-Tibetan language family, a distinct branch from Chinese, and is spoken by approximately 68 million people worldwide. Dai bilinguals are therefore highly representative of minority bilinguals.\\u003c/p\\u003e\\n\\u003cp\\u003eThus, the present study aims to examine Dai college students as representatives of ethnic bilinguals, using the event-related potential (ERP) technique and the classical Flanker paradigm to explore the following research questions: (1) Does a bilingual advantage exist in a cognitive task involving conflicting information? (2) If a bilingual advantage exists, is it driven by inhibitory control or conflict monitoring? (3) If a conflict-monitoring advantage exists, does it arise from monitoring itself, or from the adaptation of monitoring to conflict? (4) Does the bilingual advantage correspond to a left hemisphere advantage, given that language predominantly shapes the left hemisphere of the brain?\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003ch3\\u003eParticipants and Procedure\\u003c/h3\\u003e\\n\\u003cp\\u003eA total of 48 participants participated in the experiment. One participant was excluded due to low accuracy in the behavioral task (below 75%), and two participants were excluded due to excessive EEG artifacts. The final valid sample consisted of 45 participants. All participants were Dai college students from Xishuangbanna Dai Autonomous Prefecture, Dehong Dai Jingpo Autonomous Prefecture, Gengma Dai Wa Autonomous County, Menglian Dai Lahu Wa Autonomous County, and Lincang, Yunnan Province, China, aged between 18 and 22 years. The monolingual group included 23 participants (16 females and 7 males, mean age = 19.52 \\u0026plusmn; 0.99), while the bilingual group included 22 participants (15 females and 7 males, mean age = 19.73 \\u0026plusmn; 1.12). The two groups did not differ significantly in terms of age (\\u003cem\\u003et\\u003c/em\\u003e(43) = -0.652, \\u003cem\\u003ep\\u003c/em\\u003e = .518) or gender distribution (\\u0026chi;\\u0026sup2;(1) = .010, \\u003cem\\u003ep\\u003c/em\\u003e = .920).\\u003c/p\\u003e\\n\\u003cp\\u003eParticipants in the monolingual group reported proficiency only in Chinese, with no listening skills in Dai. In contrast, participants in the bilingual group reported Dai as their first language and Chinese as their second language, with proficiency in listening to both languages. All bilingual participants were early acquirers of their second language, with the mean age of second language acquisition being 4.59 years (SD = 1.764, range 2\\u0026ndash;8 years). The bilingual group had a significantly higher age of Chinese language acquisition than the monolingual group (\\u003cem\\u003et\\u003c/em\\u003e(43) = 7.113, \\u003cem\\u003ep\\u003c/em\\u003e = .000).\\u003c/p\\u003e\\n\\u003cp\\u003eLanguage proficiency was assessed using a five-point self-assessment scale, where 1 indicated no proficiency and 5 indicated full proficiency. There was no significant difference in Chinese language proficiency between the monolingual and bilingual groups (\\u003cem\\u003et\\u003c/em\\u003e(43) = 1.185, \\u003cem\\u003ep\\u003c/em\\u003e = .243), but the bilingual group exhibited significantly higher Dai language proficiency than the monolingual group (\\u003cem\\u003et\\u003c/em\\u003e(43) = 14.864, \\u003cem\\u003ep\\u003c/em\\u003e = .000).\\u003c/p\\u003e\\n\\u003cp\\u003eSince family socioeconomic status (SES) has been shown to influence cognitive performance[19,20], the study also considered participants\\u0026apos; SES. Family SES was assessed using three indicators: parents\\u0026apos; education level, parents\\u0026apos; occupation, and family income. Given that many college students are unaware of their family income, this study focused on parents\\u0026apos; education level (1. Primary school and below; 2. Primary school; 3. High school or junior college; 4. College; and 5. Graduate school and above) and occupation. The standardized scores for parents\\u0026apos; education level and occupation were summed to represent the participants\\u0026apos; family SES. No significant difference in SES scores was found between the monolingual and bilingual groups (\\u003cem\\u003et\\u003c/em\\u003e(43) = -0.865, \\u003cem\\u003ep\\u003c/em\\u003e = .392). Relevant background information is summarized in Table 1.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1 Participant background information\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"595\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 236px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003emonolingual\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003ebilingualism\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003et\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 236px;\\\"\\u003e\\n \\u003cp\\u003eN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e23\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003e22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 236px;\\\"\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e19.52（0.99）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003e19.73（1.12）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e-0.652\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 236px;\\\"\\u003e\\n \\u003cp\\u003eAge of acquisition of Dai language\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003e1.59（0.854）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 236px;\\\"\\u003e\\n \\u003cp\\u003eAge of acquisition of Chinese language\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e1.65（0.885）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003e4.59（1.764）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e7.113\\u0026nbsp;***\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 236px;\\\"\\u003e\\n \\u003cp\\u003eProficiency in Dai language\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e1.09（0.294）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003e4.08（0.900）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e14.864\\u0026nbsp;***\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 236px;\\\"\\u003e\\n \\u003cp\\u003eProficiency in Chinese\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e4.87（0.344）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003e4.73（0.456）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e1.185\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 236px;\\\"\\u003e\\n \\u003cp\\u003eSES\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 123px;\\\"\\u003e\\n \\u003cp\\u003e0.422（3.13）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 125px;\\\"\\u003e\\n \\u003cp\\u003e-0.442（3.56）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 111px;\\\"\\u003e\\n \\u003cp\\u003e-0.865\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e*** represents \\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt; .001\\u003c/p\\u003e\\n\\u003cp\\u003eAll participants were right-handed and had normal or corrected-to-normal vision. The study complied with relevant ethical standards and was approved by the Ethics Committee of Yunnan University of Chinese Medicine. After providing informed consent, participants completed the Flanker task following the collection of background information. The entire experiment lasted approximately 40 minutes, and participants received a reward upon completion.\\u003c/p\\u003e\\n\\u003ch3\\u003eFlanker Task\\u003c/h3\\u003e\\n\\u003cp\\u003eThe experimental program was developed using E-Prime 2.0 software, and stimuli were presented at the center of a 19-inch computer screen positioned approximately 50 cm from the participant. The screen refresh rate was 75 Hz. The task was a classic arrow-based Flanker paradigm, using five arrows (\\u0026lt; or \\u0026gt;) displayed in 18-point Verdana font. There were four stimulus types: the congruent condition (C), in which all arrows pointed in the same direction (e.g., \\u0026gt;\\u0026gt;\\u0026gt;\\u0026gt;\\u0026gt; or \\u0026lt;\\u0026lt;\\u0026lt;\\u0026lt;\\u0026lt;), and the incongruent condition (I), in which the central arrow pointed in the opposite direction from the flanking arrows (e.g., \\u0026gt;\\u0026gt;\\u0026lt;\\u0026gt;\\u0026gt; or \\u0026lt;\\u0026lt;\\u0026gt;\\u0026lt;\\u0026lt;). Participants were instructed to ignore the flanking arrows and respond only to the direction of the central arrow. If the central arrow pointed left (\\u0026lt;), participants pressed the \\u0026quot;S\\u0026quot; key with their left index finger; if it pointed right (\\u0026gt;), they pressed the \\u0026quot;K\\u0026quot; key with their right index finger. Responses were required within 2000 ms, after which the program automatically proceeded to the next stimulus.\\u003c/p\\u003e\\n\\u003cp\\u003eThe experiment consisted of nine blocks: one practice block and eight formal blocks. The practice block included 16 trials\\u0026mdash;8 congruent and 8 incongruent. Participants proceeded to the formal experiment only after achieving an accuracy rate of at least 85% in the practice block.\\u003c/p\\u003e\\n\\u003cp\\u003eIn the formal phase, each of the four stimulus types was presented 64 times, resulting in a total of 256 trials. Because the congruency sequence effect (CSE) involves the influence of the previous trial on the current one, trials were pseudo-randomized to ensure a balanced number of transitions between trial types and to precisely define trial categories. Excluding the first trial of each block (which had no preceding trial), the remaining 248 trials included 62 trials in which both the previous and current trials were congruent (cC), 62 trials in which the previous trial was congruent and the current trial was incongruent (cI), 63 trials in which the previous trial was incongruent and the current trial was congruent (iC), and 61 trials in which both the previous and current trials were incongruent (iI).\\u003c/p\\u003e\\n\\u003cp\\u003eAll 256 trials were presented in a predetermined order and divided into eight blocks of 32 trials each. After each block, participants were given a rest period, the duration of which they could control themselves. The detailed experimental procedure is illustrated in Figure 1.\\u003c/p\\u003e\\n\\u003cp\\u003eThe example in the procedure illustrates a case in which the first stimulus is an incongruent type (I) and the second stimulus is a congruent type (C). Considering the sequential relationship between trials, the second stimulus is thus categorized as an iC trial.\\u003c/p\\u003e\\n\\u003ch3\\u003eData Acquisition and Preprocessing\\u003c/h3\\u003e\\n\\u003cp\\u003eBehavioral data, including participants\\u0026apos; keypress reaction times and error rates, were recorded using E-Prime 2.0.\\u0026nbsp;EEG data were recorded using a 64-channel EEG amplifier system (Brain Products GmbH, Germany), with electrodes positioned according to the international 10\\u0026ndash;20 system. The online reference was set at FCz, and the ground electrode was located at the midpoint between FPz and Fz. Horizontal electrooculogram (HEOG) signals were recorded using an electrode placed at the outer canthus of the right eye. The data were sampled at 500 Hz and recorded with an online band-pass filter ranging from 0.01 to 70 Hz. Scalp electrode impedances were maintained below 5 k\\u0026Omega; throughout the recording.\\u003c/p\\u003e\\n\\u003cp\\u003eOffline EEG processing was performed using EEGLAB 13.0 running on MATLAB R2013a. Continuous EEG data were first re-referenced to the average of the left and right mastoid electrodes. Independent Component Analysis (ICA) was applied to the continuous data to identify and remove components associated with ocular artifacts, including eye blinks. Subsequently, the data were filtered using a 0.01\\u0026ndash;30 Hz finite impulse response (FIR) band-pass filter with a transition bandwidth of approximately 0.5 Hz and a roll-off rate of ~12 dB/octave. Epochs were extracted from \\u0026minus;200 ms to 800 ms relative to stimulus onset, and baseline correction was performed using the pre-stimulus interval (\\u0026minus;200 to 0 ms). Epochs were excluded from averaging if the amplitude exceeded \\u0026plusmn;80 \\u0026mu;V in any EEG channel.\\u003c/p\\u003e\\n\\u003cp\\u003eThe ERP components of interest were N2 and P3[21]. The N2, a negative deflection associated with early conflict detection[22,23], typically exhibits a fronto-central distribution\\u0026nbsp;[24]. To examine hemispheric differences, six frontal electrode sites were selected: left frontal (F1, F3, F5), frontal midline (Fz), and right frontal (F2, F4, F6). The P3 component, a positive wave linked to attentional resource allocation[25], is primarily observed in the central-parietal region[26,27]. For P3 analysis, six central-parietal electrodes were selected: left central-parietal (CP1, CP3, CP5), central-parietal midline (CPz), and right central-parietal (CP2, CP4, CP6). To avoid bias in time-window selection, time windows for the N2 and P3 components were determined based on the grand average ERP waveforms averaged across all participants and conditions, as recommended by Kappenman and Luck[28]. The time windows were defined around clear peaks of the grand average global field power (GFP), resulting in windows of 280\\u0026ndash;360 ms for N2 and 360\\u0026ndash;500 ms for P3.\\u003c/p\\u003e\\n\\u003cp\\u003eBehavioral reaction times and EEG data analyses included only correct trials, excluding practice trials, the first trial of each block, trials with reaction times below 200 ms or above 1000 ms, trials outside 2.5 standard deviations from the mean reaction time, and trials immediately following errors.\\u003c/p\\u003e\\n\\u003ch3\\u003eStatistical Analyses\\u003c/h3\\u003e\\n\\u003cp\\u003eBehavioral and EEG data were analyzed using SPSS 22.0. For EEG data, a four-way repeated measures ANOVA was conducted with factors of Group (monolingual, bilingual), Previous Trial Consistency (congruent [c], incongruent [i]), Current Trial Consistency (congruent [C], incongruent [I]), and Brain Region (left, center, right). The dependent variables were the mean amplitudes of the N2 and P3 components, analyzed separately. Previous trial consistency, current trial consistency, and brain region were treated as within-subject factors, while group was a between-subject factor. The Greenhouse-Geisser correction was applied to adjust degrees of freedom when the sphericity assumption was violated. Multiple comparisons were adjusted using the Bonferroni correction. Statistical significance was set at \\u0026alpha; = 0.05.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cem\\u003eBehavioral Experiments-Overall Response Time Analysis\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA 2 (Group: monolingual, bilingual) \\u0026times; 2 (Previous Trial Consistency: congruent [c], incongruent [i]) \\u0026times; 2 (Current Trial Consistency: congruent [C], incongruent [I]) three-way repeated measures ANOVA was conducted with response time as the dependent variable. Group served as a between-subjects factor, while previous and current trial consistencies were within-subjects factors. Accuracy rates across all conditions exceeded 95%, so no further analyses of accuracy were performed.\\u003c/p\\u003e\\n\\u003cp\\u003eAnalysis of reaction times (see Figure 1) revealed no significant main effect of Group,\\u003cem\\u003e\\u0026nbsp;F\\u003c/em\\u003e(1, 43) = 0.154, \\u003cem\\u003ep\\u003c/em\\u003e = .697, but a significant main effect of Current Trial Consistency, \\u003cem\\u003eF\\u003c/em\\u003e(1, 43) = 493.237, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .001, \\u0026eta;\\u003csub\\u003ep\\u003c/sub\\u003e\\u0026sup2; = 0.920, with longer reaction times observed in incongruent compared to congruent trials. Neither the Group \\u0026times; Previous Trial Consistency interaction, the Group \\u0026times; Current Trial Consistency interaction, nor the three-way interaction among Group, Previous Trial Consistency, and Current Trial Consistency reached significance (all \\u003cem\\u003eF\\u003c/em\\u003es \\u0026lt; 1).\\u003c/p\\u003e\\n\\u003cp\\u003eA significant interaction between Previous Trial Consistency and Current Trial Consistency was observed, \\u003cem\\u003eF\\u003c/em\\u003e(1, 43) = 16.019, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .001, \\u0026eta;\\u003csub\\u003ep\\u003c/sub\\u003e\\u0026sup2; = 0.271. Simple effects analyses indicated that when the previous trial was congruent, reaction times were significantly longer for incongruent current trials (cI) than for congruent current trials (cC) (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .001). Similarly, when the previous trial was incongruent, reaction times were longer for incongruent current trials (iI) than for congruent current trials (iC) (p \\u0026lt; .001). Moreover, reaction times were significantly longer for incongruent current trials following congruent previous trials (iI \\u0026gt; iC, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .001). When the current trial was congruent, reaction times were longer if the previous trial was incongruent compared to congruent (iC \\u0026gt; cC, \\u003cem\\u003ep\\u003c/em\\u003e = .020). Conversely, when the current trial was incongruent, reaction times were longer if the previous trial was congruent rather than incongruent (cI \\u0026gt; iI, \\u003cem\\u003ep\\u003c/em\\u003e = .001).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eBehavioral Experiments-\\u003c/em\\u003e \\u003cem\\u003eAnalysis of Conflict Effects\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo complement the omnibus ANOVA, we additionally calculated two widely used indices of conflict processing: the conflict effect (CE = incongruent \\u0026minus; congruent) and the congruency sequence effect (CSE = (cI \\u0026minus; cC) \\u0026minus; (iI \\u0026minus; iC)). These metrics were computed separately for each level of the preceding trial condition. While the omnibus ANOVA already revealed the relevant interaction effects, these additional indices allow for a more direct and interpretable assessment of both immediate conflict costs and dynamic conflict adaptation. This approach also facilitates more precise comparisons with previous findings in the literature. A similar analytical rationale was applied to the ERP data, where condition-wise comparisons were used to evaluate neural correlates of conflict processing. For the sake of clarity and brevity, this logic will not be repeated in the subsequent ERP sections.\\u003c/p\\u003e\\n\\u003cp\\u003eA 2 (Group: monolingual, bilingual) \\u0026times; 2 (Conflict Effect Type: post-consistent trial, post-inconsistent trial) repeated measures ANOVA on the conflict effect magnitude in response time (CE = I \\u0026minus; C) revealed no significant main effect of Group, \\u003cem\\u003eF\\u003c/em\\u003e(1, 43) = 0.246, \\u003cem\\u003ep\\u003c/em\\u003e = .622. However, the main effect of Conflict Effect Type was significant, \\u003cem\\u003eF\\u003c/em\\u003e(1, 43) = 16.019, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .001, \\u0026eta;p\\u0026sup2; = 0.271, indicating that the conflict effect following consistent trials was significantly larger than that following inconsistent trials. The interaction between Group and Conflict Effect Type was not significant, Fs \\u0026lt; 1.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eBehavioral Experiments-\\u003c/em\\u003e \\u003cem\\u003eAnalysis of Congruency Sequence Effect\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAn independent samples t-test was conducted to compare the congruency sequence effect (CSE = (cI \\u0026minus; cC) \\u0026minus; (iI \\u0026minus; iC)) on response times between monolingual and bilingual participants. Results showed no significant difference in CSE magnitude between the groups, \\u003cem\\u003et\\u003c/em\\u003e(43) = \\u0026minus;0.048, \\u003cem\\u003ep\\u003c/em\\u003e = .962. The results for both conflict effects and the congruency sequence effect are illustrated in Figure 3.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eElectroencephalography-N2 Component\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAverage Amplitude Analysis\\u003c/p\\u003e\\n\\u003cp\\u003eA four-way repeated measures ANOVA with factors of Group (2: monolingual, bilingual), Previous Trial Consistency (2: congruent [c], incongruent [i]), Current Trial Consistency (2: congruent [c], incongruent [i]), and Brain Region (3: left, centre, right) was conducted on the mean amplitude of the N2 component. Previous trial consistency, current trial consistency, and brain region were within-subject variables, and group was a between-subject variable.\\u003c/p\\u003e\\n\\u003cp\\u003eResults revealed a significant main effect of Group, \\u003cem\\u003eF\\u003c/em\\u003e(1, 43) = 9.278, \\u003cem\\u003ep\\u003c/em\\u003e = .004, \\u0026eta;p\\u0026sup2; = 0.177, with the monolingual group exhibiting significantly larger N2 amplitudes than the bilingual group. The main effect of Current Trial Consistency was also significant, \\u003cem\\u003eF\\u003c/em\\u003e(1, 43) = 4.779, \\u003cem\\u003ep\\u003c/em\\u003e = .034, \\u0026eta;p\\u0026sup2; = 0.100, showing greater N2 amplitude in the current trial incongruent condition compared to the congruent condition. Additionally, there was a significant main effect of Brain Region, \\u003cem\\u003eF\\u003c/em\\u003e(2, 86) = 5.442, \\u003cem\\u003ep\\u003c/em\\u003e = .006, \\u0026eta;p\\u0026sup2; = 0.112, with significantly larger amplitudes observed in the central region than in the right hemisphere (\\u003cem\\u003ep\\u003c/em\\u003e = .003), as shown in Figure 6a.\\u003c/p\\u003e\\n\\u003cp\\u003eA significant interaction between Previous Trial Consistency and Current Trial Consistency was found, \\u003cem\\u003eF\\u003c/em\\u003e(1, 43) = 4.440, \\u003cem\\u003ep\\u003c/em\\u003e = .041, \\u0026eta;p\\u0026sup2; = 0.094. Simple effects analyses indicated that when the previous trial was congruent, the N2 amplitude was significantly larger in the current trial incongruent condition (cI) compared to the congruent condition (cC) (\\u003cem\\u003ep\\u003c/em\\u003e = .005), i.e., cI \\u0026gt; cC. However, when the previous trial was incongruent, no significant difference was found between current incongruent (iI) and congruent (iC) conditions (\\u003cem\\u003ep\\u003c/em\\u003e = .390). In the current incongruent condition, the N2 amplitude was significantly greater following a previous congruent trial (cI) than following a previous incongruent trial (iI) (\\u003cem\\u003ep\\u003c/em\\u003e = .027), i.e., cI \\u0026gt; iI. Conversely, in the current congruent condition, the difference between previous congruent (cC) and previous incongruent (iC) trials was not significant (\\u003cem\\u003ep\\u003c/em\\u003e = .471).\\u003c/p\\u003e\\n\\u003cp\\u003eAll other main effects and interactions were nonsignificant (\\u003cem\\u003eF\\u003c/em\\u003es \\u0026lt; 2.6). See Figure 4 for N2 amplitude results and Figure 7a for topographical maps.\\u003c/p\\u003e\\n\\u003cp\\u003eConflict Effects Size Analysis\\u003c/p\\u003e\\n\\u003cp\\u003eA 2 (Group: monolingual, bilingual) \\u0026times; 2 (Conflict Effect Type: post-consistent trial, post-inconsistent trial) \\u0026times; 3 (Brain Region: left, centre, right) repeated measures ANOVA was conducted on the magnitude of conflict effects in the N2 component amplitudes. Results showed a significant main effect of Conflict Effect Type, \\u003cem\\u003eF\\u003c/em\\u003e(1, 43) = 4.440, \\u003cem\\u003ep\\u003c/em\\u003e = .041, \\u0026eta;p\\u0026sup2; = 0.094, with larger conflict effects following consistent trials than inconsistent trials. No significant main effects of Group or Brain Region, nor any significant interactions, were found (all \\u003cem\\u003eF\\u003c/em\\u003es \\u0026lt; 2.6). The conflict effect sizes for the N2 component are illustrated in Figure 5.\\u003c/p\\u003e\\n\\u003cp\\u003eCongruency Sequence Effect Size Analysis\\u003c/p\\u003e\\n\\u003cp\\u003eA 2 (Group: monolingual, bilingual) \\u0026times; 3 (Brain Region: left, centre, right) repeated measures ANOVA was performed on the congruency sequence effect (CSE) magnitude in the N2 component amplitude. Neither the main effects of Group and Brain Region nor their interaction were significant (all \\u003cem\\u003eF\\u003c/em\\u003es \\u0026lt; 2.6). The magnitude of the CSE on the N2 component is displayed in Figure 5, with a corresponding topographical map shown in Figure 9a.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eElectroencephalography-P3 Component\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAverage Amplitude Analysis\\u003c/p\\u003e\\n\\u003cp\\u003eA four-factor repeated measures ANOVA with 2 (Group: monolingual, bilingual) \\u0026times; 2 (Previous Trial Consistency: c, i) \\u0026times; 2 (Current Trial Consistency: C, I) \\u0026times; 3 (Brain Region: left, centre, right) was conducted, using the mean amplitude of the P3 component as the dependent variable. The results revealed a significant main effect of Group, \\u003cem\\u003eF\\u003c/em\\u003e(1, 43) = 8.570, \\u003cem\\u003ep\\u003c/em\\u003e = .005, \\u0026eta;p\\u0026sup2; = 0.166, with the monolingual group showing significantly smaller P3 amplitudes than the bilingual group. The main effect of Brain Region was also significant, \\u003cem\\u003eF\\u003c/em\\u003e(2, 86) = 10.346, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .001, \\u0026eta;p\\u0026sup2; = 0.194, with amplitudes in the central region significantly larger than those in both the left (\\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; .001) and right (p = .038) regions. No significant difference was found between the left and right regions. All other main effects and interactions were nonsignificant (\\u003cem\\u003eF\\u003c/em\\u003es \\u0026lt; 2.4). See Figure 6 for P3 amplitudes and Figure 7b for the topographical map.\\u003c/p\\u003e\\n\\u003cp\\u003eConflict Effect Size Analysis\\u003c/p\\u003e\\n\\u003cp\\u003eA repeated-measures ANOVA with 2 (Group: monolingual, bilingual) \\u0026times; 2 (Conflict Effect Type: post-consistent trial, post-inconsistent trial) \\u0026times; 3 (Brain Region: left, centre, right) was conducted on the amplitude of the P3 component. The analysis revealed no significant main effects or interactions, except for a significant interaction between Conflict Effect Type and Brain Region, \\u003cem\\u003eF\\u003c/em\\u003e(2, 86) = 7.434, \\u003cem\\u003ep\\u003c/em\\u003e = .001, \\u0026eta;p\\u0026sup2; = 0.147. Simple effects analyses indicated that after inconsistent trials, the conflict effect was significantly greater in the right brain region than in the left (\\u003cem\\u003ep\\u003c/em\\u003e = .040). After consistent trials, the conflict effect was slightly greater in the left brain region than in the right, with a borderline significant difference (\\u003cem\\u003ep\\u003c/em\\u003e = .053). The conflict effect amplitudes for the P3 component are shown in Figure 8.\\u003c/p\\u003e\\n\\u003cp\\u003eCongruency Sequence Effect (CSE) Size Analysis\\u003c/p\\u003e\\n\\u003cp\\u003eA repeated-measures ANOVA with 2 (Group: monolingual, bilingual) \\u0026times; 3 (Brain Region: left, centre, right) was conducted on the amplitude of the P3 component CSE. The analysis showed a significant main effect of Brain Region, \\u003cem\\u003eF\\u003c/em\\u003e(2, 86) = 7.435, \\u003cem\\u003ep\\u003c/em\\u003e = .001, \\u0026eta;p\\u0026sup2; = 0.147. Post hoc comparisons revealed that the CSE amplitude was significantly larger in the left brain region than in the right (\\u003cem\\u003ep\\u003c/em\\u003e = .001), whereas the central region did not significantly differ from either the left or right regions. The main effect of Group was not significant, \\u003cem\\u003eF\\u003c/em\\u003e(1, 43) = 1.825, \\u003cem\\u003ep\\u003c/em\\u003e = .184, nor was the Group \\u0026times; Brain Region interaction, \\u003cem\\u003eF\\u003c/em\\u003e(2, 86) = 0.473, \\u003cem\\u003ep\\u0026nbsp;\\u003c/em\\u003e= .625. The CSE amplitude results for the P3 component are displayed in Figure 8, with topographical maps shown in Figure 9b.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003e\\u003cem\\u003eConflict monitoring-Overall response time/amplitude\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eOverall reaction time is a traditional behavioral indicator of conflict monitoring. In the present study, no significant differences in overall reaction time were found between bilinguals and monolinguals, suggesting that bilinguals did not exhibit an advantage in conflict monitoring at the behavioral level. This result aligns with previous findings[29]. Some researchers have noted that the influence of bilingual experience on conflict monitoring may not be readily observable through behavioral outcomes alone, and that neurophysiological indicators may offer deeper insights into the relationship between bilingualism and cognitive control[30].\\u003c/p\\u003e\\n\\u003cp\\u003eIndeed, our ERP findings revealed between-group differences in both the N2 and P3 components. Specifically, bilinguals exhibited smaller N2 amplitudes than monolinguals under both congruent and incongruent conditions. This finding is consistent with results reported by Kousaie et al.\\u0026nbsp;[23]. The N2 component, an event-related potential associated with conflict monitoring and cognitive control, typically reflects the extent of conflict detection, with greater N2 amplitudes indicating greater engagement of monitoring processes[31]. Thus, the smaller N2 amplitudes observed in bilinguals may reflect more efficient language processing and better regulation of cognitive resources, especially in switching between languages, resulting in reduced cognitive load during task performance.\\u003c/p\\u003e\\n\\u003cp\\u003eThis interpretation is supported by Lamm et al., who found that N2 amplitude decreases from childhood to adolescence, reflecting the maturation of cognitive control[32]. Smaller N2 amplitudes have been associated with better performance on independent executive function tasks. Additionally, bilinguals have been shown to exhibit reduced activation in the anterior cingulate cortex (ACC) compared to monolinguals, which corresponds to lower N2 amplitudes and suggests more efficient ACC engagement[33]. Yeung and Cohen also noted that increased N2 amplitude may reflect enhanced processing of task-irrelevant information[22]. Collectively, these findings support the interpretation that bilinguals\\u0026rsquo; reduced N2 amplitudes reflect more efficient conflict monitoring mechanisms at the neural level.\\u003c/p\\u003e\\n\\u003cp\\u003eRegarding the P3 component, the mean amplitude across the two groups exhibited a pattern opposite to that observed for the N2 component: bilinguals showed significantly larger P3 amplitudes than monolinguals. The P3 is considered a reliable neural marker of attentional resource allocation, with greater amplitudes reflecting increased attention engagement[25]. Thus, the enhanced P3 amplitudes observed in bilinguals suggest that they recruit more cognitive resources when performing cognitive tasks.\\u003c/p\\u003e\\n\\u003cp\\u003eThis finding is consistent with previous research. For instance, Kousaie et al., using Simon, Stroop, and Flanker tasks, also reported that bilinguals exhibited greater P3 amplitudes than monolinguals, indicating heightened attentional engagement across a range of conflict monitoring paradigms[34].\\u003c/p\\u003e\\n\\u003cp\\u003eAlthough bilinguals and monolinguals performed similarly at the behavioral level, neurophysiological data suggest distinct underlying mechanisms. Bilinguals exhibited reduced N2 amplitudes\\u0026mdash;possibly reflecting more efficient conflict monitoring and reduced need for anterior cingulate cortex (ACC) engagement\\u0026mdash;yet showed increased P3 amplitudes, indicating greater attentional allocation. Together, these findings imply that bilinguals may be more efficient in resolving conflict and mobilizing attentional resources.\\u003c/p\\u003e\\n\\u003cp\\u003eImportantly, these results also suggest that Dai-Chinese bilinguals demonstrate similar cognitive advantages and underlying mechanisms as bilinguals whose second language is English. This supports the view that the cognitive benefits of bilingualism are not restricted to specific language pairs or cultural contexts, but rather represent a universal phenomenon.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eConflict monitoring-\\u003c/em\\u003e \\u003cem\\u003eCongruency Sequence Effects\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe congruency sequence effect (CSE) is another important index of conflict monitoring. In the present study, analysis of conflict effect size revealed that the magnitude of conflict effects was greater following congruent trials than after incongruent trials. This behavioral pattern reflects a serial consistency effect, commonly referred to as the conflict adaptation effect. It suggests that individuals dynamically adjust their attentional strategies based on the congruency of the previous trial. Specifically, when the preceding trial is incongruent, individuals are more likely to engage in conflict monitoring, which reduces the conflict effect on the subsequent trial.\\u003c/p\\u003e\\n\\u003cp\\u003eThe conflict adaptation effect reflects experience-driven modulation of attention in response to cognitive conflict. In EEG results, this pattern was observed on the N2 component: the conflict effect was significantly larger following congruent than incongruent trials, indicating that the neural mechanism for conflict monitoring is sensitive to sequential congruency. However, this pattern was not observed on the P3 component, where the conflict effect did not differ significantly between post-congruent and post-incongruent trials. This suggests that while conflict adaptation affects monitoring processes (as indexed by the N2), it may not influence attentional resource allocation (as indexed by the P3).\\u003c/p\\u003e\\n\\u003cp\\u003eThe CSE is thought to reflect an individual\\u0026apos;s ability to adaptively regulate attention and cognitive control in response to previous conflict. Grundy et al. reported that bilinguals exhibited a smaller conflict adaptation effect than monolinguals, despite no group differences in overall reaction times[15]. They interpreted this as evidence that bilinguals are better at dissociating their attentional responses to congruent versus incongruent stimuli. However, the present study found no significant group differences in CSE magnitude, either in behavioral measures or in EEG components. This aligns with previous research by Goldsmith and Morton, who also found no differences between bilingual and monolingual adults using the Flanker task[35]. Similarly, studies using the size congruency task, Simon task, and Attention Network Task (ANT) have reported no significant group differences in conflict adaptation effects[16,36]\\u0026nbsp;.\\u003c/p\\u003e\\n\\u003cp\\u003eTaken together, our results suggest that bilinguals do not show an advantage over monolinguals in terms of conflict adaptation or in the dynamic adjustment of cognitive resources. Although bilinguals demonstrated a monitoring advantage\\u0026mdash;as evidenced by differences in N2 and P3 amplitudes\\u0026mdash;this advantage appears limited to the conflict detection process itself and does not extend to the adaptive modulation of conflict monitoring.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSuppression Control\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe conflict effect refers to the negative influence of conflicting information on task performance during cognitive processing. Behaviorally, it manifests as increased reaction times in incongruent conditions compared to congruent ones. In the present study, participants responded significantly faster in congruent trials than in incongruent trials, indicating the presence of a classic conflict effect.\\u003c/p\\u003e\\n\\u003cp\\u003eWhen considering the congruency relationship between the preceding and current trials, response times in the cC condition (congruent preceded by congruent) were significantly shorter than in the cI condition (incongruent preceded by congruent), and response times in the iC condition (congruent preceded by incongruent) were also shorter than those in the iI condition (incongruent preceded by incongruent). These results indicate that conflict effects were observed after both congruent and incongruent trials, demonstrating the stability of this effect.\\u003c/p\\u003e\\n\\u003cp\\u003eConsistent with the behavioral findings, the EEG results showed that N2 amplitudes were significantly larger in incongruent trials compared to congruent trials, suggesting the presence of conflict effects at the neural level as well. Specifically, when the preceding trial was congruent, a significant conflict effect was observed on the N2 component of the current trial. When the preceding trial was incongruent, although the difference did not reach statistical significance, the N2 amplitude in the incongruent condition was still numerically larger than in the congruent condition, reflecting a trend consistent with conflict processing.\\u003c/p\\u003e\\n\\u003cp\\u003eConflict effect size\\u0026mdash;defined as the difference in mean reaction times or EEG amplitudes between incongruent and congruent trials\\u0026mdash;is commonly used as an index of inhibitory control. A smaller conflict effect size typically indicates stronger inhibitory control. Some studieshave reported that bilinguals exhibit smaller conflict effects, suggesting a greater ability to inhibit irrelevant or conflicting information compared to monolinguals[3,37,38]\\u0026nbsp;.\\u003c/p\\u003e\\n\\u003cp\\u003eHowever, the present study found no significant differences in conflict effect size between bilinguals and monolinguals in behavioral outcomes. This finding is consistent with previous studies, all of which reported no significant association between bilingual experience and conflict effect sizes in reaction time measures using the Flanker or Stroop tasks[13,15,39]. Similarly, EEG results showed no significant differences between groups in conflict effect size on either the N2 or P3 components. These converging findings suggest that, contrary to some prior claims, bilinguals may not exhibit a general advantage in inhibitory control as measured by conflict effect size.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eDifferences in Brain Regions\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIt is well known that for right-handed individuals, the dominant hemisphere is typically the left hemisphere of the brain. Language functions are predominantly controlled by this dominant hemisphere, which is why the left hemisphere is often referred to as the\\u0026nbsp;\\u0026ldquo;language brain\\u0026rdquo;. Because bilinguals receive more extensive training in language use than monolinguals, Rodriguez et al. proposed that bilinguals engage more language-related control areas in the left hemisphere, such as the left caudate nucleus and the left inferior frontal gyrus (LIFG)[40]. Other studies have also found structural differences, with bilinguals showing a thicker cortex in the LIFG[41]\\u0026nbsp;and the left middle frontal gyrus, compared to monolinguals[36]. These findings support the notion that the bilingual advantage is primarily associated with left hemisphere regions.\\u003c/p\\u003e\\n\\u003cp\\u003eHowever, in the present study, no significant differences between bilinguals and monolinguals were observed in any of the EEG components of interest\\u0026mdash;including waveform amplitudes, conflict effects, or congruency sequence effects (CSE)\\u0026mdash;with respect to brain region activation. This suggests that bilinguals did not exhibit greater activation in left hemisphere regions compared to monolinguals.\\u003c/p\\u003e\\n\\u003cp\\u003eFurthermore, the current study found no significant differences between the left and right hemispheres in the amplitudes of either the N2 or P3 components. The largest amplitudes were observed in the midline electrode sites, indicating that both monolingual and bilingual participants may engage bilateral brain regions cooperatively during cognitive processing.\\u003c/p\\u003e\\n\\u003cp\\u003eAn interesting additional finding was that, for the P3 component, the magnitude of the CSE was significantly greater in the left hemisphere compared to the right. This could suggest that the right hemisphere may exhibit better conflict adaptation in terms of attentional resource allocation. However, this interpretation remains speculative and should be verified through more precise neuroimaging techniques and further empirical research.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis study used the Flanker task and ERP techniques to compare cognitive control between Dai\\u0026ndash;Han bilinguals and monolinguals. Results showed a bilingual advantage in conflict monitoring, reflected in reduced N2 and increased P3 amplitudes, suggesting more efficient conflict detection and attentional allocation. However, no significant group differences were found in conflict adaptation (CSE) or inhibitory control (conflict effect size), indicating that the advantage is specific to monitoring processes.No clear left-hemisphere dominance was observed; instead, both groups showed bilateral activation, though the left hemisphere exhibited a stronger CSE in P3 amplitude. This may imply lateralized attentional adaptation, warranting further research. Overall, findings support a selective bilingual advantage in monitoring efficiency. These results, based on Dai\\u0026ndash;Han bilinguals, expand current understanding by showing that such advantages are not limited to Indo-European language contexts. Future studies should employ neuroimaging tools like fMRI or MEG to further explore the neural basis and developmental trajectory of bilingual cognitive advantages.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eAcknowledgements\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors wish to thank all of the participants involved in this study.\\u003c/p\\u003e\\n\\u003cp\\u003eAuthor Contributions\\u003c/p\\u003e\\n\\u003cp\\u003eConceptualization, R.Y. and F.Y.; methodology, J.L.; validation, J.Z.; formal analysis, R.Y. and J.L.; investigation, R.Y. and J.Z.; resources, F.Y.; data curation, J.Z.; writing\\u0026mdash;original draft preparation, R.Y. and J.Z.; writing\\u0026mdash;review and editing, R.Y. and F.Y.; visualization, J.L. supervision, F.Y. and J.L.; project administration, J.L.; funding acquisition, F.Y.. All authors have read and agreed to the published version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u003c/p\\u003e\\n\\u003cp\\u003eThis research was funded by the Yunnan Provincial Department of Education Scientific Research Fund, grant number 2024J0412, and the Joint Special Key Project of Yunnan Federation of Social Sciences and Yunnan University of Chinese Medicine, grant number LHZX202403.\\u003c/p\\u003e\\n\\u003cp\\u003eEthics approval and consent to participate\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was conducted in accordance with the Declaration of Helsinki. Approval was granted by the Institutional Ethics Committee of Yunnan University of Chinese Medicine (Project Code: XXLW-2025-002; Date of Approval: 16 January 2025).\\u003c/p\\u003e\\n\\u003cp\\u003eConsent for publication\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003eAvailability of data and materials\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003eConflicts of Interest \\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no conflict of interest.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eBhatia, T. K. \\u0026amp; Ritchie, W. C. The Handbook of Bilingualism and Multilingualism; John Wiley \\u0026amp; Sons, ; ISBN 1-118-33241-5. (2012).\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePeal, E. \\u0026amp; Lambert, W. E. 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Bilinguals Use Language-Control Brain Areas More than Monolinguals to Perform Non-Linguistic Switching Tasks. \\u003cem\\u003ePLoS One\\u003c/em\\u003e. \\u003cb\\u003e8\\u003c/b\\u003e, e73028. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1371/journal.pone.0073028\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pone.0073028\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2013).\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKlein, D., Mok, K., Chen, J. K. \\u0026amp; Watkins, K. E. Age of Language Learning Shapes Brain Structure: A Cortical Thickness Study of Bilingual and Monolingual Individuals. \\u003cem\\u003eBrain Lang.\\u003c/em\\u003e \\u003cb\\u003e131\\u003c/b\\u003e, 20\\u0026ndash;24. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.bandl.2013.05.014\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.bandl.2013.05.014\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2014).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Bilingual advantage, Conflict monitoring, Inhibitory control, Conflict effect, Congruency sequence effect\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7008196/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7008196/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eResearch on the bilingual advantage in cognitive control has yielded mixed results, particularly across diverse populations. This study examined whether Dai bilinguals in China demonstrate enhanced cognitive control compared to monolinguals. Participants completed a classic Eriksen Flanker task while both behavioral responses and EEG data were recorded. Analyses focused on reaction times, conflict effects, and the ERP components N2 (reflecting conflict monitoring) and P3 (reflecting attentional allocation). Although no significant group differences emerged in behavioral performance, bilinguals showed reduced N2 and increased P3 amplitudes relative to monolinguals, indicating more efficient neural conflict monitoring. No differences were observed in conflict or congruency sequence effects between the groups. These findings suggest a bilingual advantage in neural conflict monitoring, even in the absence of behavioral differences. This advantage was not lateralized and highlights the value of integrating behavioral and ERP measures to better understand bilingual cognitive processing in diverse cultural contexts.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Neural Evidence for Conflict Monitoring Advantage in Dai-Han Bilinguals: An ERP Study with the Flanker Task\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-07-29 12:36:50\",\"doi\":\"10.21203/rs.3.rs-7008196/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-09-05T06:05:53+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-09-03T09:33:36+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"61929906452151894426048503788391895277\",\"date\":\"2025-08-12T08:58:06+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-08-04T20:08:30+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"237899065846046931563260517906166309775\",\"date\":\"2025-07-25T15:34:31+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-07-24T22:40:39+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-07-24T22:39:07+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2025-06-30T18:08:43+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-06-30T09:15:45+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Scientific Reports\",\"date\":\"2025-06-30T08:25:18+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"2c540713-0959-4a4e-aa07-e6ac7b957244\",\"owner\":[],\"postedDate\":\"July 29th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":52133260,\"name\":\"Biological sciences/Neuroscience\"},{\"id\":52133262,\"name\":\"Biological sciences/Psychology\"},{\"id\":52133264,\"name\":\"Social science/Psychology\"}],\"tags\":[],\"updatedAt\":\"2025-12-22T16:03:34+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7008196\",\"link\":\"https://doi.org/10.1038/s41598-025-31944-9\",\"journal\":{\"identity\":\"scientific-reports\",\"isVorOnly\":false,\"title\":\"Scientific Reports\"},\"publishedOn\":\"2025-12-15 15:58:31\",\"publishedOnDateReadable\":\"December 15th, 2025\"},\"versionCreatedAt\":\"2025-07-29 12:36:50\",\"video\":\"\",\"vorDoi\":\"10.1038/s41598-025-31944-9\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41598-025-31944-9\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7008196\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7008196\",\"identity\":\"rs-7008196\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}