The Effect of Disturbance on the Neural Mechanisms of Learning Word Formation Rules in a Novel Language

The Effect of Disturbance on the Neural Mechanisms of Learning Word Formation Rules in a Novel Language | 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 Research Article The Effect of Disturbance on the Neural Mechanisms of Learning Word Formation Rules in a Novel Language Mengjie Meng, Lanlan Ren, Xiyuan Wang, John W. Schwieter, Huanhuan Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4015255/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Individuals learn the meaning of words mainly through feedback from others at early stages, but confusing feedback may cause disturbances in establishing lexical form-to-meaning mappings. To date, little is known about how these mappings are preciously established as language learning experiences and proficiency increase. To this end, we asked participants to perform a picture-word matching task under disturbance and non-disturbance conditions during functional magnetic resonance imaging (fMRI). Brain imaging revealed that in the non-disturbance condition, more brain network connections emerged during early (naïve) learning than later (expert) learning. However, in the disturbance condition, more connections were found during expert learning compared to naïve learning. Correspondingly, the behavioral results showed that as learning experiences increase in the disturbance condition, so do accuracy rates. Together, these findings indicate that with increased experience in mapping lexical forms to meanings, individuals appear to become less sensitive to disturbances by engaging multiple brain areas. Lexical form-meaning mapping Disturbance Language learning experience fMRI Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Learning words accompanies individuals throughout their entire lives, from infancy to old age. The question is, how exactly is the mapping between words and their meanings established? Individuals often depend on feedback from parents or caregivers, but sometimes incorrect or confusing feedback in a variety of contexts can hamper learning. To overcome such disturbances, individuals generally consolidate the correct mapping through continuous practice, effectively establishing and strengthening lexical form-to-meaning mappings. To date, the process of how language learners establish these mappings in the context of disturbances is not yet clear. According to Bruner et al.’s ( 1965 ) hypothesis testing theory, the formation of concepts in initial stages may be imprecise and incomplete. This may be partly due to the fact that individuals must spend an extensive amount of time exploring the connection between appearance and essence while being faced with disturbances. As knowledge and experience increase, individuals may become aware of the limitations of their original understanding, and consequently modify their initial hypotheses to develop accurate ones. Kane and Engle ( 2003 ) demonstrated that disturbance in the Stroop task is determined by the interaction of response competition and target maintenance mechanisms. In Cohen’s (1999) two-mechanism model, the mapping from lexical form to meaning involves the dynamic interplay between goal maintenance and response competition. Goal maintenance refers to keeping the target information active in the cognitive system, whereas response competition involves in the competition between different responses in solving the task. According to the model, there should be marked differences between expert and naïve learners, specifically in their ability to ignore disturbances. Naïve learners possess less knowledge and experience, and rely more on surface structure characterization, whereas expert learners possess greater knowledge and experience, and employ deeper structure characterization. Theoretical models of the mental lexicon often capture the developmental nature of lexical form-to-meaning mapping. For instance, in the bilingual literature, the Revised Hierarchical Model (RHM) (Kroll & Stewart, 1994 ) proposes an asymmetrical strength between an integrated conceptual store and first language (L1) vs. second language (L2) words. In other words, according to the model, L1 words have a stronger association with their concepts than do L2 words. Critically, the model argues that the mappings between L2 words and concepts strengthen as L2 proficiency increases. That is, as learners are exposed to higher levels of processing, continuous processing of word semantics, and richer L2 experiences, they become increasingly familiar with the morphological rules of new words, thereby establishing stronger connections between L2 words and their corresponding meanings. Many neurocognitive models in bilingualism also suggest that L2 neural representations undergo dynamic changes due to proficiency development and increased experiences (Abutalebi, 2008 ; Green, 2003 ; Paradis, 2009 ; Ullman, 2005 , 2014 ). Zobl ( 1998 ) proposed a two-stage developmental model of language learning. In the early stage, learners are unable to access the functional, independent representations of information about affixes. As learning experience increases, learners enter a second stage in which they acquire the complex internal structure of lexical forms. These dynamic changes have been observed in the brain. For instance, Li et al. ( 2020 ) trained participants to associate meaningless shapes with high-level object features. They found that language experience led to increased activation in the left supplementary motor area, the posterior part of the middle cingulate cortex, and the posterior superior temporal gyrus. A meta-analysis by Tagarelli et al. ( 2019 ) found that the medial superior frontal gyrus was associated with semantic processing and language memory, particularly when processing abstract concepts and semantic associations (Li et al., 2013 ). As individuals engage in more language experiences and consequently become more proficient, there are observable implications for language processing, both at the behavioral and neural levels. However, disturbances, including confusing or ambiguous feedback, that naturally accompany these language experiences must be mitigated by the individual. On this backdrop, this study aims to explore the role of feedback in developing mappings between lexical forms and their meanings through extensive practice. By simulating deterministic or confusing feedback (i.e., that which is often given by caregivers and educators), our experiment enables us to compare neural activity and learning outcomes in contexts that involve either disturbed or non-disturbed feedback. In our study, we analyze behavioral performance and brain imagining to examine how disturbance in feedback affects learning word formation rules when learners are first exposed to words (i.e., when they are naïve learners) and with continuous practice (i.e., as they become expert learners). We predict that in the presence of a disturbance, accuracy rates will be lower than in non-disturbance condition. However, we anticipate that when the participants become expert learners, they will be less sensitive to disturbing feedback. On a neural level, we expect these patterns to be reflected by more active connections between the dorsolateral superior frontal gyrus and other brain networks. 2. Methods 2.1 Participants A sample size of 24 was calculated using G.power 3.1 (Faul et al., 2007 ) according to the following settings: F -tests > ANOVA: Repeated measures, within factors, effect size F = .25, α error probability = .05, correlation among repeated measures = .5, power (1 − β error probability) = .8, number of groups = 1, number of measurements = 5, and nonsphericity correct ∈ = 1. Thirty participants were recruited from Liaoning Normal University. All participants had normal or corrected vision and had no history of neurological or psychological disorders. The study excluded participants whose head movements exceeding 3 mm during the experiment and those whose accuracy rates were < 50%. A total of six participants were excluded, leaving 24 participants (22 females, mean = 21.5, SD = 1.72, right-handed) for data analysis. Ethics approval was provided by the Research Centre for Brain and Cognitive Neuroscience of Liaoning Normal University, and all participants provided their written informed consent prior to taking part in the study. Table 1 shows the mean age of L2 (English) acquisition and self-ratings of L1 (Chinese) and L2 proficiency. Participants self-rated their language proficiency on a 6-point scale, where “1” was not proficient and “6” was completely proficient. The paired sample t -test showed that the L1 was more proficient than the L2 in listening ( t (23) = -8.741, p < .001), speaking ( t (23) = -8.108, p < .001), reading ( t (23) = -6.409, p < .001) and writing ( t (23) = -8.113, p < .001). These results indicate that participants have intermediate proficiency in their L2 (see also Liu et al., 2022 , 2023 for a similar sample). Table 1 Participants’ age of language acquisition and self-ratings of proficiency. L1 L2 Age of Acquisition 7.38 ± 2.00 Listening 5.42 ± .88 3.29 ± .86 Speaking 4.79 ± .78 3.25 ± .73 Reading 4.50 ± 1.18 2.83 ± 1.01 Writing 4.88 ± .95 3.25 ± 1.15 2.2 Materials We employed a picture-word matching task to investigate how participants overcome the disturbance caused by confusing feedback and how they establish mappings between word forms and meanings. The experimental materials consisted of 128 images that represented combinations of 16 shapes (pentagram, square, triangle, circle, pentagon, rhombus, arc, trapezoid, ring, ellipse, hexagon, parallelogram, cross, rectangle, semicircle, sector) and 16 colors (deep red, dark brown, light yellow, grass green, dark blue, dark purple, sky blue, light orange, light brown, dark grey, ochre, light pink, beige, black, dark green, cyan). The names of the colors and shapes were monosyllabic pseudowords (e.g., “sa” for yellow, “da” for pentagram). To achieve a balance of color and shape, half of the time the images were presented in the order of color before shape (e.g., sada = “sa” for yellow, “da” for pentagram) and the other half in the order of shape before color (e.g., dasa = “da” for pentagram,“sa” for yellow). Because it is believed that in visual perception, humans perceive color cues before shape cues (Gong et al., 2016 ), we designed 4 experimental blocks, with each block containing 32 pictures (16 with color + shape, and 16 with shape + color). In the disturbance condition, we set different rewards according to different feedback probabilities (see Fig. 1 a). We considered the feedback to have a disturbance if it was misleading. For instance, a correct response may only have a 70% chance of receiving 9 points (high reward) and a 30% chance of receiving 1 point (low reward), while a wrong response would have the opposite reward in the learning trial. When there was no disturbance, the feedback was deterministic, with a 100% chance of receiving 9 points for a correct response and a 100% chance of receiving 1 point for a wrong response in the learning trial. 2.3 Procedure To ensure that participants were familiar with the procedure, we asked them to practice four to six trials before entering the scanner. Participants were told that on each trial, they would observe a virtual learner’s judgement about a target word (i.e., whether it was presented as color + shape vs. shape + color) and would then see feedback about that choice. Following this, participants were asked the same question and were required to make their own judgement based on the virtual learner’s feedback to maximize their own reward. Participants did not receive feedback about their own judgements. As the learning time increased and participants gained more experience with the lexical form-semantic rules, they became ‘expert learners.’ This allows us to compare their performance and brain activity as naïve learners (i.e., in the first and second blocks) and as expert learners (i.e., in the third and fourth blocks). We used a within-subject experimental task with a 2 (disturbance type: non-disturbance vs. disturbance) × 2 (learning experience: naïve vs. expert) design. The design included 4 experimental blocks: two containing feedback without disturbance and two containing feedback with disturbance. Each block contained 32 trials, and 16 compound stimuli of different colors and shapes were randomly presented. Each block lasted 8 minutes and 10 seconds, with the whole experiment lasting approximately 33 minutes. The order of the four blocks was counterbalanced across participants. As shown in Fig. 1 b, a trial started with a fixation point for 500 ms, followed by the stimulus’ image and name for 3000 ms. After this, participants observed a question about the target and viewed a response made by a virtual learner (i.e., the computer) for 2000 ms. They then saw feedback about the virtual learner’s choice for 2000 ms. Finally, the choice again appeared for 3000 ms and participants made their own choice based on the virtual learner’s feedback. If participants responded within these 3000 ms, the remaining time was filled by a blank screen. Finally, a jittered inter-trial interval appeared for 1000–4000 ms. 2.4 fMRI data acquisition In this study, a GE Discovery MR750 3-T scanner was used to obtain functional and structural brain images. Participants’ heads were immobilized during scanning to prevent artifacts caused by head movement from interfering with the experiment. Each brain volume consisted of 33 axial slices (voxel size: 3.5 × 3.5 × 4.2 mm, slice thickness: 2 mm) acquired by using a T2*-weighted gradient echo planar imaging (EPI) sequence. The scan parameters of the functional images were as follows: repetition time (TR) = 2000 ms; echo time (TE) = 30 ms; flip angle = 90°; image matrix = 64 × 64; field of view (FOV) = 224 × 224 mm. There were four runs in total and each functional scan run contained 245 time points. A structural image was acquired using a T1-weighted 3D MPRAGE sequence with 19 slices, slice thickness = 1 mm, TR = 6.652 ms, TE = 2.928 ms, rotation angle = 12°, sequential acquisition = 192 slices, slice spacing = 1 mm, image matrix = 256 × 256. The field of view and voxel size were 256 × 256 mm and 1 × 1 × 1 mm, respectively. 2.5 Behavioral data analyses To investigate how disturbance in feedback affects lexical form-meaning mapping, we used a generalized linear mixed effects model to analyze participants’ accuracy. The analyses were conducted using lme4 software, with accuracy as the dependent variable, and learning experience and disturbance/non disturbance as fixed effects. Trials in which participants did not respond were excluded from the analyses. We constructed a mixed-effects model with different random effects and evaluated the superiority of the model using Bayesian Information Criteria (BIC). According to the BIC, we selected the simplest model (i.e., the model with the lowest BIC). The final model was model = glmer (rate ~ data $ learning experience*data $ disturbance + (1|subject), family = “binomial”, data, control = glmerControl (optimizer = “bobyqa”, optCtrl = list (maxfun = 20000))). 2.6 fMRI data preprocessing analyses We analyzed the fMRI data using dpabi (Yan et al. 2016 ), a toolkit for preprocessing and analyzing brain imaging data. First, the EPI DICOM data were converted to NIFTI format, and the first 10 volumes of each run were discarded due to T1 relaxation artifacts. In addition, slice time correction was performed using the middle slice of the volume as a reference to correct for head movement. Then, each participant’s brain and structural images were registered for comparison between groups and statistical analyses. Next, we used the DARTEL tool (Ashburner, 2007 ) to map the brain structure images of different individuals into the same standard space (MNI) to improve the accuracy of normalization. Finally, all voxels were resampled to 3 × 3 × 3 mm and all functions were smoothed using 6 mm FWHM isotropic Gaussian checks. 2.7 Full factorial analyses Using SPM12 software in MATLAB R2014b (Welcome Department of Cognitive Neurology, London, UK), we performed a general linear model (GLM) analysis. At the first level analysis, to understand the influence of disturbance on word learning, we divided each trial into disturbance and learning phases to establish a multiple event-related GLM. In fMRI studies, head movements generated by each participant can interfere with the analysis of brain images, so these six head movement parameters must be treated as noise and modeled as regression factors. These head movement parameters were convolved with a typical hemodynamic response function (HRF) to account for the delay and shape of the blood oxygen level dependent (BOLD) signal, reducing the noise introduced by head movements, and more accurately reflecting brain activity. Then, the generated .mat file is divided into four conditions (disturbance-naïve, disturbance-expert, non-disturbance-naïve, and non-disturbance-expert) in the Contrast Manager. The results of the first level GLM for each participant were used in a second level group analysis, using the full factor analysis to test for significant effects between groups. In the group statistical analysis, a full factor analysis was used on the whole brain to examine main effects and interactions between disturbance and learning experience. Finally, Gaussian Random Field (GRF) theory was used to generate a statistical graph at the threshold. GRF correction is a family error rate correction method (Tillikainen et al., 2006 ; Woo et al., 2014 ) which allows for strict correction using a single voxel threshold of p < .001, cluster level threshold of p 20 voxels. 2.8 gPPT analysis To further investigate the neural circuitry elicited by disturbance vs. non-disturbance conditions, we used a generalized psychophysical interaction analysis (gPPI) to assess functional networks in the brain and reveal how functional connections are made between BOLD signals in regions of interest (ROIs). Specifically, for each participant, we used the gPPI toolbox (Cisler et al., 2013 ; McLaren et al., 2012 ) in SPM8 to extract the deconvolved times series from the seed regions as physiological variables, and convolved each experimental condition (disturbance-naïve, disturbance-expert, non-disturbance-naïve, and non-disturbance-expert) and parameter modulator with the standard oxygen level dependent response function as psychological regressors. The PPI terms were then created by multiplying the time series of the psychological regressors and the physiological variables. Next, the general linear model (GLM) was estimated separately for each participant using spm8, and statistical plots comparing the results of all 24 participants were combined to enter a group-level random effects analysis (i.e., one-sample t -test). Finally, statistical significance was examined for various parameters (GRF correction, single voxel threshold p < .001, cluster level threshold p 20 voxels) to assess functional connectivity across different brain regions. 3. Results 3.1 Behavioral Results To assess the impact of disturbance type and learning experience on word-formation rule learning, we ran a 2 (disturbance type: non-disturbance vs. disturbance) × 2 (learning experience: naïve vs. expert) generalized logistic mixed-effects model. The results indicated that the main fixed effect of learning experience was not significant ( b = − .003, SE = .128, z = − .028, p = .978). The main fixed effect of disturbance type was significant, such that participants made more accurate responses in the non-disturbance condition compared to the disturbance condition ( b = -1.610, SE = .128, z = -12.544, p < .001). More important, the interaction between disturbance type and learning experience was significant ( b = .546, SE = .257, z = 2.127, p = .033) (see Fig. 2 ). Further analyses revealed that participants were more accurate as expert learners than as naïve learners in the disturbance condition ( b = − .263, SE = .123, z = -2.132, p = .033), but these differences were not significant in the non-disturbance condition ( b = .276, SE = .225, z = 1.225, p = .221). Moreover, participants were more accurate in the non-disturbance condition than in the disturbance condition both during naïve ( b = 1.88, SE = .208, z = 9.004, p < .001) and expert learning ( b = 1.34, SE = .196, z = 6.834, p < .001). 3.2 fMRI results 3.2.1 Full-factorial results We conducted a 2 (disturbance type: non-disturbance vs. disturbance) × 2 (learning experience: naïve vs. expert) full-factorial analysis to assess brain activation. As shown in Table 2 , Table 3 , and Fig. 3 , for disturbance type, a significant interaction was found in the L MFG, R SFGdor, and L IPL (see Fig. 3 a), and for learning experience, the interaction was concentrated in the R SFGdor, L Postcentral gyrus, and R Postcentral gyrus (see Fig. 3 b). During naïve learning, the activation of the L MFG in the non-disturbance condition was higher than that of the disturbance condition ( b = 1.090, SE = .179, t = 6.11, p < .001). However, there was no significant difference during expert learning ( b = .110, SE = .179, t = .614, p = .541). Naïve learning also elicited stronger activity in the L MFG than expert learning in non-disturbance conditions ( b = .962, SE = .179, t = 5.378, p < .001), but not in disturbance conditions ( b = − .021, SE = .179, t = − .118, p = .907) (see Fig. 3 c). During naïve learning, there was stronger activation in the R SFGdor in the non-disturbance condition than in the disturbance condition ( b = .799, SE = .208, t = 3.846, p = .000), but no such difference emerged during expert learning ( b = − .247, SE = .208, t = -1.19, p = .238). Compared to expert learning, naïve learning elicited stronger R SFGdor activity in the non-disturbance condition ( b = .850, SE = .208, t = 4.09, p < .001), but not in the disturbance condition ( b = − .196, SE = .208, t = − .944, p = .349) (see Fig. 3 d). Moreover, compared to expert learning experience, naïve learning experience elicited stronger R SFGdor ( b = .931, SE = .227, t = 4.111, p < .001) (see Fig. 3 e), L Postcentral gyrus ( b = 1.686, SE = .332, t = 5.073, p < .001) (see Fig. 3 f), and R Postcentral gyrus activity ( b = 1.200, SE = .26, t = 4.603, p < .001) (see Fig. 3 g) in the non-disturbance condition, but not in the disturbance condition ( b = − .343, SE = .227, t = -1.513, p = .135; b = − .499, SE = .332, t = -1.501, p = .138; b = − .41, SE = .26, t = -1.576, p = .1196, respectively). During naïve learning, non-disturbance conditions elicited stronger activation in the R SFGdor ( b = .782, SE = .227, t = 3.452, p = .001) (see Fig. 3 e), L Postcentral gyrus ( b = 1.752, SE = .332, t = 5.273, p < .001) (see Fig. 3 f) and R Postcentral gyrus ( b = 1.311, SE = .26, t = 5.036, p < .001) (see Fig. 3 g) compared to disturbance conditions. However, during expert learning, more activity was found in the R SFGdor in disturbance conditions compared to non-disturbance conditions ( b = − .492, SE = .227, t = -2.172, p < .033) (see Fig. 3 h). The L Postcentral gyrus ( b = − .432, SE = .332, t = -1.301, p = .1977) and R Postcentral gyrus ( b = − .297, SE = .260, t = -1.143, p = .257) did not show such difference during expert learning, (see Fig. 3 f, g). Table 2 Full-factorial results. Phase Brain Regions BA Cluster Coordinates (x, y, z) F p Disturbance L MFG 6 38 -27 3 48 17.46 < .001 *** R SFGdor 6 20 27 0 63 15.61 < .001 *** L IPL 40 55 -30 -45 54 19.18 < .001 *** Learning R SFGdor 6 31 18 − 12 72 16.84 < .001 *** L Postcentral 3 690 -48 -27 60 32.48 < .001 *** R Postcentral 4 490 48 − 18 42 24.73 < .001 *** Note: L = Left, R = Right, BA = Brodmann area, medial, MFG = middle frontal gyrus, SFGdor = superior frontal gyrus. *** p Expert L MFG, R SFGdor Naïve: non-DIS > DIS Learning R SFGdor, L/R Postcentral Non-DIS: Naïve > Expert R SFGdor, L/R Postcentral Naïve: non-DIS > DIS R SFGdor Expert: DIS > non-DIS Notes: L = Left, R = Right, non-DIS = non-disturbance, DIS = disturbance, SFGmed = superior frontal gyrus, medial, MFG = middle frontal gyrus, SFGdor = superior frontal gyrus, dorsolateral. 3.2.2 gPPT results To further investigate the circuity of brain region activation in disturbance (non-disturbance vs. disturbance) and learning (naïve vs. expert) conditions, we conducted a gPPI connectivity analysis by selecting seeds from the interactive brain regions revealed in the full-factorial analysis. As shown in Table 4 and Fig. 4 , expert learning showed higher connectivity between the L MFG and R Cerebelum_Crus1 compared to naïve learning experience in the non-disturbance condition (see Fig. 4 a). In the disturbance condition, however, naïve learning exhibited stronger connectivity between R SFGdor and L SMA, L SFGdor, L PoCG, L DGG, L LING, R LING, R CUN, L PHG, and R PoCG compared to expert learning (see Fig. 4 b). Expert learning showed stronger activation connectivity between R SFGdor and L SMA, L IPL, L STG, and L IFGtriang compared to naïve learning in the non-disturbance condition (see Fig. 4 c), while no such differences were found in the non-disturbance condition. Table 4 Functional connectivity of ROIs. Phase Seeds Comparisons Connectivity areas BA Cluster Coordinates (x, y, z) t p Disturbance L MFG non-DIS: expert > naïve R CC1 / 27 42–63 -30 5.78 expert L SMA 6 79 -3 -3 72 4.8 < .001 *** R LING 18 65 6–69 -3 4.2 < .001 *** R CUN / 55 9–72 27 4.3 < .001 *** R PHG 20 41 36 − 18 -27 5.3 < .001 *** L SFGdor 6 36 -24 -6 69 4.24 < .001 *** L DCG / 34 0–15 45 4.03 < .001 *** L LING 19 25 -18 -63 -6 4.62 < .001 *** L PoCG 2 25 -39 -30 45 4.37 < .001 *** L LING 18 22 -33 -90 15 5.26 < .001 *** R PreCG 6 22 36 − 18 69 4.12 naïve L SMA 6 54 -9 15 51 5.26 < .001 *** L IPL 40 53 -30 -48 42 4.97 < .001 *** L STG 42 37 -60 -42 18 4.86 < .001 *** L IFGtriang 48 26 -36 24 24 4.29 < .001 *** Notes : L = Left, R = Right, non-DIS = non-disturbance, DIS = disturbance, BA = Brodmann area, CC1 = Cerebelum_Crus1, SFGdor = superior frontal gyrus, dorsolateral, SMA = supplementary motor area, DCG = median cingulate and paracingulate gyri, PoCG = postcentral gyrus, PreCG = Precentral gyrus, LING = lingual gyrus, CUN = cuneus, PHG = ParaHippocampal gyrus, IFGtriang = inferior frontal gyrus, triangular part, IPL = Inferior parietal, but supramarginal and angular gyri, STG = superior temporal gyrus. *** p < .001. Discussion Learning a new language is a long and complex process that naturally entails disturbances, such as those found in feedback. In this study, we simulated the process that humans go through as they learn words and word formation rules, starting as naïve learners and with intensive practice, becoming experts in which they successfully establish lexical-form-to-meaning mapping. To examine this process, we analyzed how disturbance in feedback affects behavioral performance and brain activity during naïve and expert learning. The results of the behavioral analyses showed lower accuracy rates in disturbance conditions compared to non-disturbance conditions, suggesting that confusing feedback hampers the ability to establish a mapping between lexical forms and their meanings. However, as participants became expert leaners, their accuracy rates significantly improved when faced with disturbance conditions, suggesting that they were less sensitive to such disturbance. The fMRI results showed that during expert learning, there was more brain network connectivity in disturbance conditions than during naïve learning. Specifically, as learning experience increased, disturbance conditions elicited higher activation levels in the right dorsolateral superior frontal gyrus compared to non-disturbance conditions. This suggests that participants’ experience plays a critical role in learning the mappings between lexical forms and meanings, and that disturbances in feedback must be overcome through further experiences to establish these mappings. In non-disturbance conditions during naïve learning, participants had greater activation in brain regions typically associated with reward sensitivity (Le et al., 2020 ; Linke et al., 2010 ) and in regions associated with lexical form and semantics (Liu et al., 2021 ). This likely indicates that the mapping between lexical form and meaning was established during naïve learning in non-disturbance conditions and became more automated during expert learning. Additional functional connectivity analyses showed that during naïve learning in non-disturbance conditions, there were stronger functional connections in brain regions associated with word learning and retrieval compared to during expert learning (Gatti et al., 2020 ; Hart et al., 2000 ; Rivas-Fernandez et al., 2021; Xiang et al., 2003 ). This implies that in non-disturbance conditions, subjects quickly acquire new words during naïve learning and shift towards more automated processes that do not require excessive brain network connections during expert learning. These patterns suggest that with increased experience, learners can more effectively engage multiple brain regions to work together. These findings are consistent with the RHM model (Kroll & Stewart, 1994 ) which holds that with development, the mappings between L2 words and their meanings strengthen and become more automatic. Disturbance conditions elicited greater activation in the right dorsolateral superior frontal gyrus compared to non-disturbance conditions during expert learning, while non-disturbance conditions led to greater activation in this same region during naïve learning. This finding is consistent with the behavioral results showing that during expert learning, participants were more accurate in disturbance conditions than during naïve learning. Given that the dorsal prefrontal area and medial superior frontal gyrus are associated with semantic learning (Binney et al., 2010 ), this finding likely indicates that disturbances in feedback can disrupt the mapping between lexical form and meaning, causing participants to doubt the established mapping and requiring them to place more effort in improving the mapping. This further suggests that heightened activation of the right dorsolateral superior frontal gyrus may be related to increased learning experience, leading to qualitative changes in language acquisition. Evidence from the gPPI analyses also demonstrated these qualitative changes. Aligning with the behavioral performance, namely that participants were more accurate in disturbance conditions during expert learning compared to naïve learning, we observed stronger connections between the left supplementary motor area and the right cuneus, the bilateral lingual gyrus, the right parahippocampal gyrus, and the left postcentral gyrus in these same conditions. Among the brain regions that are associated with semantics are the cerebellum, postcentral gyrus, cuneus, supplementary motor area, left parahippocampal gyrus, and medial frontal gyrus (Jackson et al., 2016 ). The right medial posterior lingual gyrus has been implicated in general shape processing (Fink, 1996). These findings suggest that as learning experience increases and becomes less exploratory and more automatic, learners require the engagement of multiple brain regions to better establish the associations between lexical forms and meanings. Conclusion As humans acquire new words, the process of establishing an association between lexical forms and their meanings is continuously influenced by feedback. In this study, we found that disturbances in feedback complicate this process, particularly for naïve learners. However, as their learning experiences with new words increased, so did their ability to avoid the hampering effects of disturbances in feedback. These findings suggest that as individuals become more expert word learners, they also appear to become less sensitive to disturbance. Declarations Author Notes This research was supported by grants from the General Program of National Natural Science Foundation of China (32371089), Liaoning Social Science Planning Fund of China (L20AYY001), Dalian Science and Technology Star Fund of China (2020RQ055), Youth Project of Liaoning Provincial Department of Education (LJKQZ2021089), Research and Cooperation Projects on Social and Economic Development of Liaoning Province (2024lslybhzkt-17), and Liaoning Educational Science Planning Project (JG21DB306). We have no known competing interests to declare. References Abutalebi, J. (2008). Neural aspects of second language representation and language control. Acta Psychologica , 128 , 466–478. Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage , 38 (1), 95–113. Binney, R. J., Embleton, K. V., Jefferies, E., Parker, G. J., & Lambon Ralph, M. A. (2010). The ventral and inferolateral aspects of the anterior temporal lobe are crucial in semantic memory: Evidence from a novel direct comparison of distortion-corrected fMRI, rTMS, and semantic dementia. Cerebral Cortex , 20 (11), 2728–2738. Bruner, J. S., Goodnow, J. J., & Austin, G. A. (1965). A study of thinking . Wiley. Cisler, J. M., James, G. A., Tripathi, S., Mletzko, T., Heim, C., Hu, X. P., Mayberg, H., Nemeroff, C., & Kilts, C. D. (2013). Differential functional connectivity within an emotion regulation neural network among individuals resilient and susceptible to the depressogenic effects of early life stress. Psychological medicine , 43 (3), 507–518. Cohen, J. D., Barch, D. M., Carter, C., & Servan-Schreiber, D. (1999). Context-processing deficits in schizophrenia: Converging evidence from three theoretically motivated cognitive tasks. Journal of Abnormal Psychology , 108 , 120–133. Fink, G. R., Halligan, P. W., Marshall, J. C., Frith, C. D., Frackowiak, R. S. J., & Dolan, R. J. (1996). Where in the brain does visual attention select the forest and the trees? Nature , 382 (6592), 626–628. Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39 (2), 175–191. Gatti, D., Van Vugt, F., & Vecchi, T. (2020). A causal role for the cerebellum in semantic integration: A transcranial magnetic stimulation study. Scientific Reports , 10 (1), 18139. Green, D. W. (2003). The neural basis of the lexicon and the grammar in L2 acquisition. In R. van Hout, A. Hulk, F. Kuiken, & R. Towell (Eds.), The interface between syntax and the lexicon in second language acquisition (pp. 197–208). Benjamins. Gong, T., Lam, Y. W., & Shuai, L. (2016). Influence of perceptual saliency hierarchy on learning of language structures: An artificial language learning experiment. Frontiers in Psychology , 7 , 1952. Hart, A. J., Whalen, P. J., Shin, L. M., McInerney, S. C., Fischer, H., & Rauch, S. L. (2000). Differential response in the human amygdala to racial outgroup vs ingroup face stimuli. Neuroreport , 11 (11), 2351–2354. Jackson, R. L., Hoffman, P., Pobric, G., & Ralph, M. A. L. (2016). The semantic network at work and rest: Differential connectivity of anterior temporal lobe subregions. Journal of Neuroscience , 36 (5), 1490–1501. Kane, M. J., & Engle, R. W. (2003). Working-memory capacity and the control of attention: The contributions of goal neglect, response competition, and task set to Stroop interference. Journal of Experimental Psychology: General , 132 (1), 47–70. Kroll, J. F., & Stewart, E. (1994). Category interference in translation and picture naming: Evidence for asymmetric connections between bilingual memory representations. Journal of Memory and Language , 33, 149–174. Le, T. M., Wang, W., Zhornitsky, S., Dhingra, I., Zhang, S., & Li, C. S. R. (2020). Interdependent neural correlates of reward processing and response inhibition during reward and punishment sensitivity. Cortex, 30 (3), 1662–1676. Li, M., Xu, Y., Luo, X., Zeng, J., & Han, Z. (2020). Linguistic experience acquisition for novel stimuli selectively activates the neural network of the visual word form area. Neuroimage , 215 , 116838. Li, W., Qin, W., Liu, H., Fan, L., Wang, J., Jiang, T., & Yu, C. (2013). Subregions of the human superior frontal gyrus and their connections. NeuroImage, 78, 46–58. Liu, Y., Shi, G., Li, M., Xing, H., Song, Y., Xiao, L., Guan, Y., & Han, Z. (2021). Early top-down modulation in visual word form processing: evidence from an intracranial SEEG study. Journal of Neuroscience , 41 (28), 6102–6115. Liu, D., Xing, Z., Huang, J., Schwieter, J. W., & Liu, H. (2023). Genetic bases of language control in bilinguals: Evidence from an EEG study. Human Brain Mapping, 44 (9), 3624–3643. Liu, H., Li, W., Zuo, M., Wang, F., Guo, Z., & Schwieter, J. W. (2022). Cross-task adaptation effects of bilingual language control on cognitive control: a dual-brain EEG examination of simultaneous production and comprehension. Cerebral Cortex , 32 (15), 3224–3242. Linke, J., Kirsch, P., King, A. V., Gass, A., Hennerici, M. G., Bongers, A., & Wessa, M. (2010). Motivation modulates neural responses to rewards. NeuroImage, 49 (3), 2618–2625. McLaren, D. G., Ries, M. L., Xu, G., & Johnson, S. C. (2012). A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches. Neuroimage , 61 (4), 1277–1286. Paradis, M. (2009). Declarative and procedural determinants of second languages . Benjamins. Rivas-Fernández, M. Á., Varela-López, B., Cid-Fernández, S., & Galdo-Álvarez, S. (2021). Functional activation and connectivity of the left inferior frontal gyrus during lexical and phonological retrieval. Symmetry , 13 (9), 1655. Tagarelli, K. M., Shattuck, K. F., Turkeltaub, P. E., & Ullman, M. T. (2019). Language learning in the adult brain: A neuroanatomical meta-analysis of lexical and grammatical learning. NeuroImage , 193 , 178–200. Tillikainen L, Salli E, Korvenoja A, Aronen H (2006) A cluster mass permutation test with contextual enhancement for fMRI activation detection. NeuroImage, 32 (2), 654–664. Ullman, M. T. (2005). A cognitive neuroscience perspective on second language acquisition: The declarative/procedural model. In C. Sanz (Ed.), Mind and context in adult second language acquisition (pp. 141–178). Georgetown University Press. Ullman, M. T. (2014). The declarative/procedural model: A neurobiologically-motivated theory of first and second language. In B. VanPatten & J. Williams (Eds.), Theories of second language acquisition: An introduction (2nd edition) (pp. 135–158). Erlbaum. Woo C, Krishnan A, Wager T (2014) Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations. NeuroImage, 91, 412–419. Xiang, H., Lin, C., Ma, X., Zhang, Z., Bower, J. M., Weng, X., & Gao, J. H. (2003). Involvement of the cerebellum in semantic discrimination: An fMRI study. Human Brain Mapping , 18 (3), 208–214. Yan, C. G., Wang, X. D., Zuo, X. N., & Zang, Y. F. (2016). DPABI: Data processing & analysis for (resting-state) brain imaging. Neuroinformatics , 14 , 339–351. Zobl, H. (1998). Representational changes: From listed representations to independent representations of verbal affixes. In M.-L. Beck (Ed.), Morphology and its interfaces in second language knowledge (pp. 339–371). Benjamins. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4015255","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276406561,"identity":"b4559085-d15f-495c-856f-552e17e4f073","order_by":0,"name":"Mengjie Meng","email":"","orcid":"","institution":"Liaoning Normal University","correspondingAuthor":false,"prefix":"","firstName":"Mengjie","middleName":"","lastName":"Meng","suffix":""},{"id":276406562,"identity":"7f94ec9b-f93a-4e91-af28-25fb5dff952d","order_by":1,"name":"Lanlan Ren","email":"","orcid":"","institution":"Liaoning Normal University","correspondingAuthor":false,"prefix":"","firstName":"Lanlan","middleName":"","lastName":"Ren","suffix":""},{"id":276406563,"identity":"acb66446-81cd-4412-b757-8764e75cacd3","order_by":2,"name":"Xiyuan Wang","email":"","orcid":"","institution":"Liaoning Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiyuan","middleName":"","lastName":"Wang","suffix":""},{"id":276406564,"identity":"b59d39ee-49e0-4ba6-af14-d2d5f347ae73","order_by":3,"name":"John W. Schwieter","email":"","orcid":"","institution":"Wilfrid Laurier University","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"W.","lastName":"Schwieter","suffix":""},{"id":276406565,"identity":"f22080fb-afa2-45a2-9b5d-afdca88c41d9","order_by":4,"name":"Huanhuan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDACCRCqsKln428+cOBDBdFazqQl8EscSzw44wyxWhhbDidINuQYH+ZtIUIH/+zmhzc+NhzOMzhw5sMB3gYGeX6xAwQsuXPM2HLmjvRig8O9Gw5I7mAwnDk7gYA1N3LYpHnPWDNuOHB2wwHDMwwJBrcJaJEHafnbxgzUkvPgQGIbEVoMQFoY25wTZzbkMBw4SIwWQ5Bfes6kGQMD2eBgwxkJwn6Ruw0MsR8VNnLAqHz8+U+FjTy/NAEt6ECCNOWjYBSMglEwCrADANn2UC3EDw+YAAAAAElFTkSuQmCC","orcid":"","institution":"Liaoning Normal University","correspondingAuthor":true,"prefix":"","firstName":"Huanhuan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-03-05 05:04:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4015255/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4015255/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52185642,"identity":"411296b8-5c22-4a2e-9761-d615082af5ef","added_by":"auto","created_at":"2024-03-07 18:24:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111489,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental design including variations in the feedback probability (a) and an example of a single trial translated into English (for the original Chinese version, see Figure A2 in the Appendix).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4015255/v1/613634008eacc9c67fc3a46c.png"},{"id":52185643,"identity":"888b3545-ad3d-4584-935b-233757165114","added_by":"auto","created_at":"2024-03-07 18:24:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32474,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of disturbance and learning experience on the accuracy of word formation rule learning. White circles represent the mean, white lines represent the median. Box plots represent the 75% and 25% quartiles, black dots represent the data distribution.\u003c/p\u003e\n\u003cp\u003e*** \u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4015255/v1/05dae9776538d15301d987cc.png"},{"id":52185645,"identity":"430e3131-3608-4cad-981e-1deff3748ded","added_by":"auto","created_at":"2024-03-07 18:24:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":351780,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the full factorial analysis and ROI analysis. \u003cem\u003eNotes: L = \u003c/em\u003eLeft,\u003cem\u003e R\u003c/em\u003e = Right, \u003cem\u003eSFGmed = \u003c/em\u003esuperior frontal gyrus, medial, \u003cem\u003eMFG\u003c/em\u003e = middle frontal gyrus, \u003cem\u003eSFGdor = \u003c/em\u003esuperior frontal gyrus, dorsolateral, \u003cem\u003eIPL\u003c/em\u003e = Inferior parietal, but supramarginal and angular gyri. \u003cem\u003enon-DIS = \u003c/em\u003enon-disturbance, \u003cem\u003eDIS\u003c/em\u003e = disturbance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e* p \u003c/em\u003e\u0026lt; .05, ** \u003cem\u003ep \u003c/em\u003e\u0026lt; .01, *** \u003cem\u003ep \u003c/em\u003e\u0026lt; .001.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4015255/v1/1b3e54c421f6582da8992b2d.png"},{"id":52185646,"identity":"05b9cc5c-8401-448a-a2a0-6fd6daf01b22","added_by":"auto","created_at":"2024-03-07 18:24:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":440929,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the gPPI analysis. Notes: The red dots represent the ROI used as seed regions, the other dots represent brain regions with significant functional connections. \u003cem\u003eL =\u003c/em\u003e Left,\u003cem\u003e R\u003c/em\u003e = Right, \u003cem\u003enon-DIS = \u003c/em\u003enon-disturbance, \u003cem\u003eDIS\u003c/em\u003e = disturbance, \u003cem\u003eCC1 = \u003c/em\u003eCerebelum_Crus1, \u003cem\u003eSFGdor\u003c/em\u003e = superior frontal gyrus, dorsolateral, \u003cem\u003eSMA\u003c/em\u003e = supplementary motor area, \u003cem\u003eDCG\u003c/em\u003e = median cingulate and paracingulate gyri, \u003cem\u003ePoCG\u003c/em\u003e = postcentral gyrus, \u003cem\u003eLING\u003c/em\u003e = lingual gyrus, \u003cem\u003eCUN = \u003c/em\u003ecuneus, \u003cem\u003ePHG\u003c/em\u003e = ParaHippocampal gyrus, \u003cem\u003eIFGtriang = \u003c/em\u003einferior frontal gyrus, triangular part, \u003cem\u003eIPL\u003c/em\u003e = Inferior parietal, but supramarginal and angular gyri, \u003cem\u003eSTG\u003c/em\u003e = superior temporal gyrus.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4015255/v1/4ac83913ff45e5dec40f5ddf.png"},{"id":52972509,"identity":"6d292065-174d-4c01-bc7f-71036b08e0c5","added_by":"auto","created_at":"2024-03-19 08:37:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1267556,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4015255/v1/49d1f079-9ad4-4e6a-a0c9-dfb526e00d6f.pdf"},{"id":52185644,"identity":"72e117cd-87ff-4f0f-b253-ec4545feed78","added_by":"auto","created_at":"2024-03-07 18:24:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":384282,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4015255/v1/59ef01c0109c064035819cbe.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effect of Disturbance on the Neural Mechanisms of Learning Word Formation Rules in a Novel Language","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLearning words accompanies individuals throughout their entire lives, from infancy to old age. The question is, how exactly is the mapping between words and their meanings established? Individuals often depend on feedback from parents or caregivers, but sometimes incorrect or confusing feedback in a variety of contexts can hamper learning. To overcome such disturbances, individuals generally consolidate the correct mapping through continuous practice, effectively establishing and strengthening lexical form-to-meaning mappings. To date, the process of how language learners establish these mappings in the context of disturbances is not yet clear.\u003c/p\u003e \u003cp\u003eAccording to Bruner et al.\u0026rsquo;s (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1965\u003c/span\u003e) hypothesis testing theory, the formation of concepts in initial stages may be imprecise and incomplete. This may be partly due to the fact that individuals must spend an extensive amount of time exploring the connection between appearance and essence while being faced with disturbances. As knowledge and experience increase, individuals may become aware of the limitations of their original understanding, and consequently modify their initial hypotheses to develop accurate ones. Kane and Engle (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) demonstrated that disturbance in the Stroop task is determined by the interaction of response competition and target maintenance mechanisms. In Cohen\u0026rsquo;s (1999) two-mechanism model, the mapping from lexical form to meaning involves the dynamic interplay between goal maintenance and response competition. Goal maintenance refers to keeping the target information active in the cognitive system, whereas response competition involves in the competition between different responses in solving the task. According to the model, there should be marked differences between expert and na\u0026iuml;ve learners, specifically in their ability to ignore disturbances. Na\u0026iuml;ve learners possess less knowledge and experience, and rely more on surface structure characterization, whereas expert learners possess greater knowledge and experience, and employ deeper structure characterization.\u003c/p\u003e \u003cp\u003eTheoretical models of the mental lexicon often capture the developmental nature of lexical form-to-meaning mapping. For instance, in the bilingual literature, the Revised Hierarchical Model (RHM) (Kroll \u0026amp; Stewart, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) proposes an asymmetrical strength between an integrated conceptual store and first language (L1) vs. second language (L2) words. In other words, according to the model, L1 words have a stronger association with their concepts than do L2 words. Critically, the model argues that the mappings between L2 words and concepts strengthen as L2 proficiency increases. That is, as learners are exposed to higher levels of processing, continuous processing of word semantics, and richer L2 experiences, they become increasingly familiar with the morphological rules of new words, thereby establishing stronger connections between L2 words and their corresponding meanings.\u003c/p\u003e \u003cp\u003eMany neurocognitive models in bilingualism also suggest that L2 neural representations undergo dynamic changes due to proficiency development and increased experiences (Abutalebi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Green, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Paradis, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ullman, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Zobl (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) proposed a two-stage developmental model of language learning. In the early stage, learners are unable to access the functional, independent representations of information about affixes. As learning experience increases, learners enter a second stage in which they acquire the complex internal structure of lexical forms. These dynamic changes have been observed in the brain. For instance, Li et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) trained participants to associate meaningless shapes with high-level object features. They found that language experience led to increased activation in the left supplementary motor area, the posterior part of the middle cingulate cortex, and the posterior superior temporal gyrus. A meta-analysis by Tagarelli et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that the medial superior frontal gyrus was associated with semantic processing and language memory, particularly when processing abstract concepts and semantic associations (Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs individuals engage in more language experiences and consequently become more proficient, there are observable implications for language processing, both at the behavioral and neural levels. However, disturbances, including confusing or ambiguous feedback, that naturally accompany these language experiences must be mitigated by the individual. On this backdrop, this study aims to explore the role of feedback in developing mappings between lexical forms and their meanings through extensive practice. By simulating deterministic or confusing feedback (i.e., that which is often given by caregivers and educators), our experiment enables us to compare neural activity and learning outcomes in contexts that involve either disturbed or non-disturbed feedback. In our study, we analyze behavioral performance and brain imagining to examine how disturbance in feedback affects learning word formation rules when learners are first exposed to words (i.e., when they are na\u0026iuml;ve learners) and with continuous practice (i.e., as they become expert learners). We predict that in the presence of a disturbance, accuracy rates will be lower than in non-disturbance condition. However, we anticipate that when the participants become expert learners, they will be less sensitive to disturbing feedback. On a neural level, we expect these patterns to be reflected by more active connections between the dorsolateral superior frontal gyrus and other brain networks.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eA sample size of 24 was calculated using G.power 3.1 (Faul et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) according to the following settings: \u003cem\u003eF\u003c/em\u003e-tests\u0026thinsp;\u0026gt;\u0026thinsp;ANOVA: Repeated measures, within factors, effect size \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.25, α error probability\u0026thinsp;=\u0026thinsp;.05, correlation among repeated measures\u0026thinsp;=\u0026thinsp;.5, power (1\u0026thinsp;\u0026minus;\u0026thinsp;β error probability)\u0026thinsp;=\u0026thinsp;.8, number of groups\u0026thinsp;=\u0026thinsp;1, number of measurements\u0026thinsp;=\u0026thinsp;5, and nonsphericity correct \u0026isin; = 1. Thirty participants were recruited from Liaoning Normal University. All participants had normal or corrected vision and had no history of neurological or psychological disorders. The study excluded participants whose head movements exceeding 3 mm during the experiment and those whose accuracy rates were \u0026lt;\u0026thinsp;50%. A total of six participants were excluded, leaving 24 participants (22 females, mean\u0026thinsp;=\u0026thinsp;21.5, SD\u0026thinsp;=\u0026thinsp;1.72, right-handed) for data analysis. Ethics approval was provided by the Research Centre for Brain and Cognitive Neuroscience of Liaoning Normal University, and all participants provided their written informed consent prior to taking part in the study.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the mean age of L2 (English) acquisition and self-ratings of L1 (Chinese) and L2 proficiency. Participants self-rated their language proficiency on a 6-point scale, where \u0026ldquo;1\u0026rdquo; was not proficient and \u0026ldquo;6\u0026rdquo; was completely proficient. The paired sample \u003cem\u003et\u003c/em\u003e-test showed that the L1 was more proficient than the L2 in listening (\u003cem\u003et\u003c/em\u003e(23) = -8.741, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), speaking (\u003cem\u003et\u003c/em\u003e(23) = -8.108, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), reading (\u003cem\u003et\u003c/em\u003e(23) = -6.409, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and writing (\u003cem\u003et\u003c/em\u003e(23) = -8.113, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). These results indicate that participants have intermediate proficiency in their L2 (see also Liu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e for a similar sample).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants\u0026rsquo; age of language acquisition and self-ratings of proficiency.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of Acquisition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eListening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.42\u0026thinsp;\u0026plusmn;\u0026thinsp;.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeaking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWriting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.88\u0026thinsp;\u0026plusmn;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Materials\u003c/h2\u003e \u003cp\u003e We employed a picture-word matching task to investigate how participants overcome the disturbance caused by confusing feedback and how they establish mappings between word forms and meanings. The experimental materials consisted of 128 images that represented combinations of 16 shapes (pentagram, square, triangle, circle, pentagon, rhombus, arc, trapezoid, ring, ellipse, hexagon, parallelogram, cross, rectangle, semicircle, sector) and 16 colors (deep red, dark brown, light yellow, grass green, dark blue, dark purple, sky blue, light orange, light brown, dark grey, ochre, light pink, beige, black, dark green, cyan).\u003c/p\u003e \u003cp\u003eThe names of the colors and shapes were monosyllabic pseudowords (e.g., \u0026ldquo;sa\u0026rdquo; for yellow, \u0026ldquo;da\u0026rdquo; for pentagram). To achieve a balance of color and shape, half of the time the images were presented in the order of color before shape (e.g., sada = \u0026ldquo;sa\u0026rdquo; for yellow, \u0026ldquo;da\u0026rdquo; for pentagram) and the other half in the order of shape before color (e.g., dasa = \u0026ldquo;da\u0026rdquo; for pentagram,\u0026ldquo;sa\u0026rdquo; for yellow). Because it is believed that in visual perception, humans perceive color cues before shape cues (Gong et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), we designed 4 experimental blocks, with each block containing 32 pictures (16 with color\u0026thinsp;+\u0026thinsp;shape, and 16 with shape\u0026thinsp;+\u0026thinsp;color).\u003c/p\u003e \u003cp\u003eIn the disturbance condition, we set different rewards according to different feedback probabilities (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). We considered the feedback to have a disturbance if it was misleading. For instance, a correct response may only have a 70% chance of receiving 9 points (high reward) and a 30% chance of receiving 1 point (low reward), while a wrong response would have the opposite reward in the learning trial. When there was no disturbance, the feedback was deterministic, with a 100% chance of receiving 9 points for a correct response and a 100% chance of receiving 1 point for a wrong response in the learning trial.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Procedure\u003c/h2\u003e \u003cp\u003eTo ensure that participants were familiar with the procedure, we asked them to practice four to six trials before entering the scanner. Participants were told that on each trial, they would observe a virtual learner\u0026rsquo;s judgement about a target word (i.e., whether it was presented as color\u0026thinsp;+\u0026thinsp;shape vs. shape\u0026thinsp;+\u0026thinsp;color) and would then see feedback about that choice. Following this, participants were asked the same question and were required to make their own judgement based on the virtual learner\u0026rsquo;s feedback to maximize their own reward. Participants did not receive feedback about their own judgements. As the learning time increased and participants gained more experience with the lexical form-semantic rules, they became \u0026lsquo;expert learners.\u0026rsquo; This allows us to compare their performance and brain activity as na\u0026iuml;ve learners (i.e., in the first and second blocks) and as expert learners (i.e., in the third and fourth blocks).\u003c/p\u003e \u003cp\u003eWe used a within-subject experimental task with a 2 (disturbance type: non-disturbance vs. disturbance) \u0026times; 2 (learning experience: na\u0026iuml;ve vs. expert) design. The design included 4 experimental blocks: two containing feedback without disturbance and two containing feedback with disturbance. Each block contained 32 trials, and 16 compound stimuli of different colors and shapes were randomly presented. Each block lasted 8 minutes and 10 seconds, with the whole experiment lasting approximately 33 minutes. The order of the four blocks was counterbalanced across participants. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, a trial started with a fixation point for 500 ms, followed by the stimulus\u0026rsquo; image and name for 3000 ms. After this, participants observed a question about the target and viewed a response made by a virtual learner (i.e., the computer) for 2000 ms. They then saw feedback about the virtual learner\u0026rsquo;s choice for 2000 ms. Finally, the choice again appeared for 3000 ms and participants made their own choice based on the virtual learner\u0026rsquo;s feedback. If participants responded within these 3000 ms, the remaining time was filled by a blank screen. Finally, a jittered inter-trial interval appeared for 1000\u0026ndash;4000 ms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 fMRI data acquisition\u003c/h2\u003e \u003cp\u003eIn this study, a GE Discovery MR750 3-T scanner was used to obtain functional and structural brain images. Participants\u0026rsquo; heads were immobilized during scanning to prevent artifacts caused by head movement from interfering with the experiment. Each brain volume consisted of 33 axial slices (voxel size: 3.5 \u0026times; 3.5 \u0026times; 4.2 mm, slice thickness: 2 mm) acquired by using a T2*-weighted gradient echo planar imaging (EPI) sequence. The scan parameters of the functional images were as follows: repetition time (TR)\u0026thinsp;=\u0026thinsp;2000 ms; echo time (TE)\u0026thinsp;=\u0026thinsp;30 ms; flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;; image matrix\u0026thinsp;=\u0026thinsp;64 \u0026times; 64; field of view (FOV)\u0026thinsp;=\u0026thinsp;224 \u0026times; 224 mm. There were four runs in total and each functional scan run contained 245 time points. A structural image was acquired using a T1-weighted 3D MPRAGE sequence with 19 slices, slice thickness\u0026thinsp;=\u0026thinsp;1 mm, TR\u0026thinsp;=\u0026thinsp;6.652 ms, TE\u0026thinsp;=\u0026thinsp;2.928 ms, rotation angle\u0026thinsp;=\u0026thinsp;12\u0026deg;, sequential acquisition\u0026thinsp;=\u0026thinsp;192 slices, slice spacing\u0026thinsp;=\u0026thinsp;1 mm, image matrix\u0026thinsp;=\u0026thinsp;256 \u0026times; 256. The field of view and voxel size were 256 \u0026times; 256 mm and 1 \u0026times; 1 \u0026times; 1 mm, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Behavioral data analyses\u003c/h2\u003e \u003cp\u003e To investigate how disturbance in feedback affects lexical form-meaning mapping, we used a generalized linear mixed effects model to analyze participants\u0026rsquo; accuracy. The analyses were conducted using lme4 software, with accuracy as the dependent variable, and learning experience and disturbance/non disturbance as fixed effects. Trials in which participants did not respond were excluded from the analyses. We constructed a mixed-effects model with different random effects and evaluated the superiority of the model using Bayesian Information Criteria (BIC). According to the BIC, we selected the simplest model (i.e., the model with the lowest BIC). The final model was model\u0026thinsp;=\u0026thinsp;glmer (rate\u0026thinsp;~\u0026thinsp;data\u003cspan\u003e$\u003c/span\u003elearning experience*data\u003cspan\u003e$\u003c/span\u003edisturbance + (1|subject), family = \u0026ldquo;binomial\u0026rdquo;, data, control\u0026thinsp;=\u0026thinsp;glmerControl (optimizer = \u0026ldquo;bobyqa\u0026rdquo;, optCtrl\u0026thinsp;=\u0026thinsp;list (maxfun\u0026thinsp;=\u0026thinsp;20000))).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 fMRI data preprocessing analyses\u003c/h2\u003e \u003cp\u003eWe analyzed the fMRI data using dpabi (Yan et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), a toolkit for preprocessing and analyzing brain imaging data. First, the EPI DICOM data were converted to NIFTI format, and the first 10 volumes of each run were discarded due to T1 relaxation artifacts. In addition, slice time correction was performed using the middle slice of the volume as a reference to correct for head movement. Then, each participant\u0026rsquo;s brain and structural images were registered for comparison between groups and statistical analyses. Next, we used the DARTEL tool (Ashburner, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) to map the brain structure images of different individuals into the same standard space (MNI) to improve the accuracy of normalization. Finally, all voxels were resampled to 3 \u0026times; 3 \u0026times; 3 mm and all functions were smoothed using 6 mm FWHM isotropic Gaussian checks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Full factorial analyses\u003c/h2\u003e \u003cp\u003eUsing SPM12 software in MATLAB R2014b (Welcome Department of Cognitive Neurology, London, UK), we performed a general linear model (GLM) analysis. At the first level analysis, to understand the influence of disturbance on word learning, we divided each trial into disturbance and learning phases to establish a multiple event-related GLM. In fMRI studies, head movements generated by each participant can interfere with the analysis of brain images, so these six head movement parameters must be treated as noise and modeled as regression factors. These head movement parameters were convolved with a typical hemodynamic response function (HRF) to account for the delay and shape of the blood oxygen level dependent (BOLD) signal, reducing the noise introduced by head movements, and more accurately reflecting brain activity. Then, the generated .mat file is divided into four conditions (disturbance-na\u0026iuml;ve, disturbance-expert, non-disturbance-na\u0026iuml;ve, and non-disturbance-expert) in the Contrast Manager.\u003c/p\u003e \u003cp\u003eThe results of the first level GLM for each participant were used in a second level group analysis, using the full factor analysis to test for significant effects between groups. In the group statistical analysis, a full factor analysis was used on the whole brain to examine main effects and interactions between disturbance and learning experience. Finally, Gaussian Random Field (GRF) theory was used to generate a statistical graph at the threshold. GRF correction is a family error rate correction method (Tillikainen et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Woo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) which allows for strict correction using a single voxel threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, cluster level threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, and a cluster size of \u0026gt;\u0026thinsp;20 voxels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 gPPT analysis\u003c/h2\u003e \u003cp\u003eTo further investigate the neural circuitry elicited by disturbance vs. non-disturbance conditions, we used a generalized psychophysical interaction analysis (gPPI) to assess functional networks in the brain and reveal how functional connections are made between BOLD signals in regions of interest (ROIs). Specifically, for each participant, we used the gPPI toolbox (Cisler et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; McLaren et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) in SPM8 to extract the deconvolved times series from the seed regions as physiological variables, and convolved each experimental condition (disturbance-na\u0026iuml;ve, disturbance-expert, non-disturbance-na\u0026iuml;ve, and non-disturbance-expert) and parameter modulator with the standard oxygen level dependent response function as psychological regressors. The PPI terms were then created by multiplying the time series of the psychological regressors and the physiological variables. Next, the general linear model (GLM) was estimated separately for each participant using spm8, and statistical plots comparing the results of all 24 participants were combined to enter a group-level random effects analysis (i.e., one-sample \u003cem\u003et\u003c/em\u003e-test). Finally, statistical significance was examined for various parameters (GRF correction, single voxel threshold \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, cluster level threshold \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, cluster size\u0026thinsp;\u0026gt;\u0026thinsp;20 voxels) to assess functional connectivity across different brain regions.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.1 Behavioral Results\u003c/h2\u003e\n \u003cp\u003eTo assess the impact of disturbance type and learning experience on word-formation rule learning, we ran a 2 (disturbance type: non-disturbance vs. disturbance) × 2 (learning experience: naïve vs. expert) generalized logistic mixed-effects model. The results indicated that the main fixed effect of learning experience was not significant (\u003cem\u003eb\u003c/em\u003e = − .003, \u003cem\u003eSE\u003c/em\u003e = .128, \u003cem\u003ez\u003c/em\u003e = − .028, \u003cem\u003ep\u003c/em\u003e = .978). The main fixed effect of disturbance type was significant, such that participants made more accurate responses in the non-disturbance condition compared to the disturbance condition (\u003cem\u003eb\u003c/em\u003e = -1.610, \u003cem\u003eSE\u003c/em\u003e = .128, \u003cem\u003ez\u003c/em\u003e = -12.544, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). More important, the interaction between disturbance type and learning experience was significant (\u003cem\u003eb\u003c/em\u003e = .546, \u003cem\u003eSE\u003c/em\u003e = .257, \u003cem\u003ez\u003c/em\u003e = 2.127, \u003cem\u003ep\u003c/em\u003e = .033) (see Fig. \u003cspan\u003e2\u003c/span\u003e). Further analyses revealed that participants were more accurate as expert learners than as naïve learners in the disturbance condition (\u003cem\u003eb\u003c/em\u003e = − .263, \u003cem\u003eSE\u003c/em\u003e = .123, \u003cem\u003ez\u003c/em\u003e = -2.132, \u003cem\u003ep\u003c/em\u003e = .033), but these differences were not significant in the non-disturbance condition (\u003cem\u003eb\u003c/em\u003e = .276, \u003cem\u003eSE\u003c/em\u003e = .225, \u003cem\u003ez\u003c/em\u003e = 1.225, \u003cem\u003ep\u003c/em\u003e = .221). Moreover, participants were more accurate in the non-disturbance condition than in the disturbance condition both during naïve (\u003cem\u003eb\u003c/em\u003e = 1.88, \u003cem\u003eSE\u003c/em\u003e = .208, \u003cem\u003ez\u003c/em\u003e = 9.004, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and expert learning (\u003cem\u003eb\u003c/em\u003e = 1.34, \u003cem\u003eSE\u003c/em\u003e = .196, \u003cem\u003ez\u003c/em\u003e = 6.834, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.2 fMRI results\u003c/h2\u003e\n \u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.2.1 Full-factorial results\u003c/h2\u003e\n \u003cp\u003eWe conducted a 2 (disturbance type: non-disturbance vs. disturbance) × 2 (learning experience: naïve vs. expert) full-factorial analysis to assess brain activation. As shown in Table \u003cspan\u003e2\u003c/span\u003e, Table \u003cspan\u003e3\u003c/span\u003e, and Fig. \u003cspan\u003e3\u003c/span\u003e, for disturbance type, a significant interaction was found in the L MFG, R SFGdor, and L IPL (see Fig. \u003cspan\u003e3\u003c/span\u003ea), and for learning experience, the interaction was concentrated in the R SFGdor, L Postcentral gyrus, and R Postcentral gyrus (see Fig. \u003cspan\u003e3\u003c/span\u003eb).\u003c/p\u003e\n \u003cp\u003eDuring naïve learning, the activation of the L MFG in the non-disturbance condition was higher than that of the disturbance condition (\u003cem\u003eb\u003c/em\u003e = 1.090, \u003cem\u003eSE\u003c/em\u003e = .179, \u003cem\u003et\u003c/em\u003e = 6.11, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). However, there was no significant difference during expert learning (\u003cem\u003eb\u003c/em\u003e = .110, \u003cem\u003eSE\u003c/em\u003e = .179, \u003cem\u003et\u003c/em\u003e = .614, \u003cem\u003ep\u003c/em\u003e = .541). Naïve learning also elicited stronger activity in the L MFG than expert learning in non-disturbance conditions (\u003cem\u003eb\u003c/em\u003e = .962, \u003cem\u003eSE\u003c/em\u003e = .179, \u003cem\u003et\u003c/em\u003e = 5.378, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), but not in disturbance conditions (\u003cem\u003eb\u003c/em\u003e = − .021, \u003cem\u003eSE\u003c/em\u003e = .179, \u003cem\u003et\u003c/em\u003e = − .118, \u003cem\u003ep\u003c/em\u003e = .907) (see Fig. \u003cspan\u003e3\u003c/span\u003ec). During naïve learning, there was stronger activation in the R SFGdor in the non-disturbance condition than in the disturbance condition (\u003cem\u003eb\u003c/em\u003e = .799, \u003cem\u003eSE\u003c/em\u003e = .208, \u003cem\u003et\u003c/em\u003e = 3.846, \u003cem\u003ep\u003c/em\u003e = .000), but no such difference emerged during expert learning (\u003cem\u003eb\u003c/em\u003e = − .247, \u003cem\u003eSE\u003c/em\u003e = .208, \u003cem\u003et\u003c/em\u003e = -1.19, \u003cem\u003ep\u003c/em\u003e = .238). Compared to expert learning, naïve learning elicited stronger R SFGdor activity in the non-disturbance condition (\u003cem\u003eb\u003c/em\u003e = .850, \u003cem\u003eSE\u003c/em\u003e = .208, \u003cem\u003et\u003c/em\u003e = 4.09, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), but not in the disturbance condition (\u003cem\u003eb\u003c/em\u003e = − .196, \u003cem\u003eSE\u003c/em\u003e = .208, \u003cem\u003et\u003c/em\u003e = − .944, \u003cem\u003ep\u003c/em\u003e = .349) (see Fig. \u003cspan\u003e3\u003c/span\u003ed). Moreover, compared to expert learning experience, naïve learning experience elicited stronger R SFGdor (\u003cem\u003eb\u003c/em\u003e = .931, \u003cem\u003eSE\u003c/em\u003e = .227, \u003cem\u003et\u003c/em\u003e = 4.111, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) (see Fig. \u003cspan\u003e3\u003c/span\u003ee), L Postcentral gyrus (\u003cem\u003eb\u003c/em\u003e = 1.686, \u003cem\u003eSE\u003c/em\u003e = .332, \u003cem\u003et\u003c/em\u003e = 5.073, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) (see Fig. \u003cspan\u003e3\u003c/span\u003ef), and R Postcentral gyrus activity (\u003cem\u003eb\u003c/em\u003e = 1.200, \u003cem\u003eSE\u003c/em\u003e = .26, \u003cem\u003et\u003c/em\u003e = 4.603, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) (see Fig. \u003cspan\u003e3\u003c/span\u003eg) in the non-disturbance condition, but not in the disturbance condition (\u003cem\u003eb\u003c/em\u003e = − .343, \u003cem\u003eSE\u003c/em\u003e = .227, \u003cem\u003et\u003c/em\u003e = -1.513, \u003cem\u003ep\u003c/em\u003e = .135; \u003cem\u003eb\u003c/em\u003e = − .499, \u003cem\u003eSE\u003c/em\u003e = .332, \u003cem\u003et\u003c/em\u003e = -1.501, \u003cem\u003ep\u003c/em\u003e = .138; \u003cem\u003eb\u003c/em\u003e = − .41, \u003cem\u003eSE\u003c/em\u003e = .26, \u003cem\u003et\u003c/em\u003e = -1.576, \u003cem\u003ep\u003c/em\u003e = .1196, respectively). During naïve learning, non-disturbance conditions elicited stronger activation in the R SFGdor (\u003cem\u003eb\u003c/em\u003e = .782, \u003cem\u003eSE\u003c/em\u003e = .227, \u003cem\u003et\u003c/em\u003e = 3.452, \u003cem\u003ep\u003c/em\u003e = .001) (see Fig. \u003cspan\u003e3\u003c/span\u003ee), L Postcentral gyrus (\u003cem\u003eb\u003c/em\u003e = 1.752, \u003cem\u003eSE\u003c/em\u003e = .332, \u003cem\u003et\u003c/em\u003e = 5.273, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) (see Fig. \u003cspan\u003e3\u003c/span\u003ef) and R Postcentral gyrus (\u003cem\u003eb\u003c/em\u003e = 1.311, \u003cem\u003eSE\u003c/em\u003e = .26, \u003cem\u003et\u003c/em\u003e = 5.036, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) (see Fig. \u003cspan\u003e3\u003c/span\u003eg) compared to disturbance conditions. However, during expert learning, more activity was found in the R SFGdor in disturbance conditions compared to non-disturbance conditions (\u003cem\u003eb\u003c/em\u003e = − .492, \u003cem\u003eSE\u003c/em\u003e = .227, \u003cem\u003et\u003c/em\u003e = -2.172, \u003cem\u003ep\u003c/em\u003e \u0026lt; .033) (see Fig. \u003cspan\u003e3\u003c/span\u003eh). The L Postcentral gyrus (\u003cem\u003eb\u003c/em\u003e = − .432, \u003cem\u003eSE\u003c/em\u003e = .332, \u003cem\u003et\u003c/em\u003e = -1.301, \u003cem\u003ep\u003c/em\u003e = .1977) and R Postcentral gyrus (\u003cem\u003eb\u003c/em\u003e = − .297, \u003cem\u003eSE\u003c/em\u003e = .260, \u003cem\u003et\u003c/em\u003e = -1.143, \u003cem\u003ep\u003c/em\u003e = .257) did not show such difference during expert learning, (see Fig. \u003cspan\u003e3\u003c/span\u003ef, g).\u003c/p\u003e \u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFull-factorial results.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhase\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBrain Regions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoordinates\u003c/p\u003e\n \u003cp\u003e(x, y, z)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL MFG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-27 3 48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR SFGdor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 0 63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL IPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-30 -45 54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLearning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR SFGdor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 − 12 72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL Postcentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-48 -27 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR Postcentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 − 18 42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003eNote: L =\u003c/em\u003e Left, \u003cem\u003eR\u003c/em\u003e = Right, \u003cem\u003eBA\u003c/em\u003e = Brodmann area, medial, \u003cem\u003eMFG\u003c/em\u003e = middle frontal gyrus, \u003cem\u003eSFGdor =\u003c/em\u003e superior frontal gyrus.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e*** \u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e\n \u003cdiv\u003e \u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparisons of the interaction between learning experience and disturbance type.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhase\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eROI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCondition\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL MFG, R SFGdor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-DIS: Naïve \u0026gt; Expert\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL MFG, R SFGdor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNaïve: non-DIS \u0026gt; DIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLearning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR SFGdor, L/R Postcentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-DIS: Naïve \u0026gt; Expert\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR SFGdor, L/R Postcentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNaïve: non-DIS \u0026gt; DIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR SFGdor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpert: DIS \u0026gt; non-DIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cem\u003eNotes: L =\u003c/em\u003e Left, \u003cem\u003eR\u003c/em\u003e = Right, \u003cem\u003enon-DIS =\u003c/em\u003e non-disturbance, \u003cem\u003eDIS\u003c/em\u003e = disturbance, \u003cem\u003eSFGmed =\u003c/em\u003e superior frontal gyrus, medial, \u003cem\u003eMFG\u003c/em\u003e = middle frontal gyrus, \u003cem\u003eSFGdor =\u003c/em\u003e superior frontal gyrus, dorsolateral.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.2.2 gPPT results\u003c/h2\u003e\n \u003cp\u003eTo further investigate the circuity of brain region activation in disturbance (non-disturbance vs. disturbance) and learning (naïve vs. expert) conditions, we conducted a gPPI connectivity analysis by selecting seeds from the interactive brain regions revealed in the full-factorial analysis. As shown in Table \u003cspan\u003e4\u003c/span\u003e and Fig. \u003cspan\u003e4\u003c/span\u003e, expert learning showed higher connectivity between the L MFG and R Cerebelum_Crus1 compared to naïve learning experience in the non-disturbance condition (see Fig. \u003cspan\u003e4\u003c/span\u003ea). In the disturbance condition, however, naïve learning exhibited stronger connectivity between R SFGdor and L SMA, L SFGdor, L PoCG, L DGG, L LING, R LING, R CUN, L PHG, and R PoCG compared to expert learning (see Fig. \u003cspan\u003e4\u003c/span\u003eb). Expert learning showed stronger activation connectivity between R SFGdor and L SMA, L IPL, L STG, and L IFGtriang compared to naïve learning in the non-disturbance condition (see Fig. \u003cspan\u003e4\u003c/span\u003ec), while no such differences were found in the non-disturbance condition.\u003c/p\u003e\n \u003cdiv\u003e \u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFunctional connectivity of ROIs.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhase\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeeds\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComparisons\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConnectivity\u003c/p\u003e\n \u003cp\u003eareas\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoordinates\u003c/p\u003e\n \u003cp\u003e(x, y, z)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL MFG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-DIS: expert \u0026gt; naïve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR CC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42–63 -30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR SFGdor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDIS: naïve \u0026gt; expert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL SMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3 -3 72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR LING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6–69 -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR CUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9–72 27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR PHG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 − 18 -27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL SFGdor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24 -6 69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL DCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0–15 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL LING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18 -63 -6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL PoCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-39 -30 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL LING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-33 -90 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR PreCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 − 18 69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLearning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL SFGdor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enon-DIS: expert \u0026gt; naïve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL SMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9 15 51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL IPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-30 -48 42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL STG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-60 -42 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL IFGtriang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-36 24 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; .001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cem\u003eNotes\u003c/em\u003e: \u003cem\u003eL =\u003c/em\u003e Left, \u003cem\u003eR\u003c/em\u003e = Right, \u003cem\u003enon-DIS =\u003c/em\u003e non-disturbance, \u003cem\u003eDIS\u003c/em\u003e = disturbance, \u003cem\u003eBA\u003c/em\u003e = Brodmann area, \u003cem\u003eCC1 =\u003c/em\u003e Cerebelum_Crus1, \u003cem\u003eSFGdor\u003c/em\u003e = superior frontal gyrus, dorsolateral, \u003cem\u003eSMA\u003c/em\u003e = supplementary motor area, \u003cem\u003eDCG\u003c/em\u003e = median cingulate and paracingulate gyri, \u003cem\u003ePoCG\u003c/em\u003e = postcentral gyrus, \u003cem\u003ePreCG\u003c/em\u003e = Precentral gyrus, \u003cem\u003eLING\u003c/em\u003e = lingual gyrus, \u003cem\u003eCUN =\u003c/em\u003e cuneus, \u003cem\u003ePHG\u003c/em\u003e = ParaHippocampal gyrus, \u003cem\u003eIFGtriang =\u003c/em\u003e inferior frontal gyrus, triangular part, \u003cem\u003eIPL\u003c/em\u003e = Inferior parietal, but supramarginal and angular gyri, \u003cem\u003eSTG\u003c/em\u003e = superior temporal gyrus.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*** \u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLearning a new language is a long and complex process that naturally entails disturbances, such as those found in feedback. In this study, we simulated the process that humans go through as they learn words and word formation rules, starting as naïve learners and with intensive practice, becoming experts in which they successfully establish lexical-form-to-meaning mapping. To examine this process, we analyzed how disturbance in feedback affects behavioral performance and brain activity during naïve and expert learning. The results of the behavioral analyses showed lower accuracy rates in disturbance conditions compared to non-disturbance conditions, suggesting that confusing feedback hampers the ability to establish a mapping between lexical forms and their meanings. However, as participants became expert leaners, their accuracy rates significantly improved when faced with disturbance conditions, suggesting that they were less sensitive to such disturbance.\u003c/p\u003e\u003cp\u003eThe fMRI results showed that during expert learning, there was more brain network connectivity in disturbance conditions than during naïve learning. Specifically, as learning experience increased, disturbance conditions elicited higher activation levels in the right dorsolateral superior frontal gyrus compared to non-disturbance conditions. This suggests that participants’ experience plays a critical role in learning the mappings between lexical forms and meanings, and that disturbances in feedback must be overcome through further experiences to establish these mappings.\u003c/p\u003e\u003cp\u003eIn non-disturbance conditions during naïve learning, participants had greater activation in brain regions typically associated with reward sensitivity (Le et al., \u003cspan\u003e2020\u003c/span\u003e; Linke et al., \u003cspan\u003e2010\u003c/span\u003e) and in regions associated with lexical form and semantics (Liu et al., \u003cspan\u003e2021\u003c/span\u003e). This likely indicates that the mapping between lexical form and meaning was established during naïve learning in non-disturbance conditions and became more automated during expert learning. Additional functional connectivity analyses showed that during naïve learning in non-disturbance conditions, there were stronger functional connections in brain regions associated with word learning and retrieval compared to during expert learning (Gatti et al., \u003cspan\u003e2020\u003c/span\u003e; Hart et al., \u003cspan\u003e2000\u003c/span\u003e; Rivas-Fernandez et al., 2021; Xiang et al., \u003cspan\u003e2003\u003c/span\u003e). This implies that in non-disturbance conditions, subjects quickly acquire new words during naïve learning and shift towards more automated processes that do not require excessive brain network connections during expert learning. These patterns suggest that with increased experience, learners can more effectively engage multiple brain regions to work together. These findings are consistent with the RHM model (Kroll \u0026amp; Stewart, \u003cspan\u003e1994\u003c/span\u003e) which holds that with development, the mappings between L2 words and their meanings strengthen and become more automatic.\u003c/p\u003e\u003cp\u003eDisturbance conditions elicited greater activation in the right dorsolateral superior frontal gyrus compared to non-disturbance conditions during expert learning, while non-disturbance conditions led to greater activation in this same region during naïve learning. This finding is consistent with the behavioral results showing that during expert learning, participants were more accurate in disturbance conditions than during naïve learning. Given that the dorsal prefrontal area and medial superior frontal gyrus are associated with semantic learning (Binney et al., \u003cspan\u003e2010\u003c/span\u003e), this finding likely indicates that disturbances in feedback can disrupt the mapping between lexical form and meaning, causing participants to doubt the established mapping and requiring them to place more effort in improving the mapping. This further suggests that heightened activation of the right dorsolateral superior frontal gyrus may be related to increased learning experience, leading to qualitative changes in language acquisition.\u003c/p\u003e\u003cp\u003eEvidence from the gPPI analyses also demonstrated these qualitative changes. Aligning with the behavioral performance, namely that participants were more accurate in disturbance conditions during expert learning compared to naïve learning, we observed stronger connections between the left supplementary motor area and the right cuneus, the bilateral lingual gyrus, the right parahippocampal gyrus, and the left postcentral gyrus in these same conditions. Among the brain regions that are associated with semantics are the cerebellum, postcentral gyrus, cuneus, supplementary motor area, left parahippocampal gyrus, and medial frontal gyrus (Jackson et al., \u003cspan\u003e2016\u003c/span\u003e). The right medial posterior lingual gyrus has been implicated in general shape processing (Fink, 1996). These findings suggest that as learning experience increases and becomes less exploratory and more automatic, learners require the engagement of multiple brain regions to better establish the associations between lexical forms and meanings.\u003c/p\u003e"},{"header":" Conclusion","content":"\u003cp\u003eAs humans acquire new words, the process of establishing an association between lexical forms and their meanings is continuously influenced by feedback. In this study, we found that disturbances in feedback complicate this process, particularly for na\u0026iuml;ve learners. However, as their learning experiences with new words increased, so did their ability to avoid the hampering effects of disturbances in feedback. These findings suggest that as individuals become more expert word learners, they also appear to become less sensitive to disturbance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Notes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by grants from the General Program of National Natural Science Foundation of China (32371089), Liaoning Social Science Planning Fund of China (L20AYY001), Dalian Science and Technology Star Fund of China (2020RQ055), Youth Project of Liaoning Provincial Department of Education (LJKQZ2021089), Research and Cooperation Projects on Social and Economic Development of Liaoning Province (2024lslybhzkt-17), and Liaoning Educational Science Planning Project (JG21DB306).\u003c/p\u003e\n\u003cp\u003eWe have no known competing interests to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbutalebi, J. (2008). Neural aspects of second language representation and language control.\u003cem\u003e Acta Psychologica\u003c/em\u003e,\u003cem\u003e 128\u003c/em\u003e, 466\u0026ndash;478.\u003c/li\u003e\n\u003cli\u003eAshburner, J. (2007). A fast diffeomorphic image registration algorithm. \u003cem\u003eNeuroimage\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(1), 95\u0026ndash;113.\u003c/li\u003e\n\u003cli\u003eBinney, R. J., Embleton, K. V., Jefferies, E., Parker, G. J., \u0026amp; Lambon Ralph, M. A. (2010). The ventral and inferolateral aspects of the anterior temporal lobe are crucial in semantic memory: Evidence from a novel direct comparison of distortion-corrected fMRI, rTMS, and semantic dementia. \u003cem\u003eCerebral Cortex\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(11), 2728\u0026ndash;2738.\u003c/li\u003e\n\u003cli\u003eBruner, J. S., Goodnow, J. J., \u0026amp; Austin, G. A. (1965). \u003cem\u003eA study of thinking\u003c/em\u003e. Wiley.\u003c/li\u003e\n\u003cli\u003eCisler, J. M., James, G. A., Tripathi, S., Mletzko, T., Heim, C., Hu, X. P., Mayberg, H., Nemeroff, C., \u0026amp; Kilts, C. D. (2013). Differential functional connectivity within an emotion regulation neural network among individuals resilient and susceptible to the depressogenic effects of early life stress. \u003cem\u003ePsychological medicine\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(3), 507\u0026ndash;518.\u003c/li\u003e\n\u003cli\u003eCohen, J. D., Barch, D. M., Carter, C., \u0026amp; Servan-Schreiber, D. (1999). Context-processing deficits in schizophrenia: Converging evidence from three theoretically motivated cognitive tasks. \u003cem\u003eJournal of Abnormal Psychology\u003c/em\u003e, \u003cem\u003e108\u003c/em\u003e, 120\u0026ndash;133.\u003c/li\u003e\n\u003cli\u003eFink, G. R., Halligan, P. W., Marshall, J. C., Frith, C. D., Frackowiak, R. S. J., \u0026amp; Dolan, R. J. (1996). Where in the brain does visual attention select the forest and the trees? \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e382\u003c/em\u003e(6592), 626\u0026ndash;628.\u003c/li\u003e\n\u003cli\u003eFaul, F., Erdfelder, E., Lang, A. G., \u0026amp; Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. \u003cem\u003eBehavior Research Methods, 39\u003c/em\u003e(2), 175\u0026ndash;191.\u003c/li\u003e\n\u003cli\u003eGatti, D., Van Vugt, F., \u0026amp; Vecchi, T. (2020). A causal role for the cerebellum in semantic integration: A transcranial magnetic stimulation study. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 18139.\u003c/li\u003e\n\u003cli\u003eGreen, D. W. (2003). The neural basis of the lexicon and the grammar in L2 acquisition. In R. van Hout, A. Hulk, F. Kuiken, \u0026amp; R. Towell (Eds.), \u003cem\u003eThe interface between syntax and the lexicon in second language acquisition\u003c/em\u003e (pp. 197\u0026ndash;208). Benjamins. \u003c/li\u003e\n\u003cli\u003eGong, T., Lam, Y. W., \u0026amp; Shuai, L. (2016). Influence of perceptual saliency hierarchy on learning of language structures: An artificial language learning experiment. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 1952.\u003c/li\u003e\n\u003cli\u003eHart, A. J., Whalen, P. J., Shin, L. M., McInerney, S. C., Fischer, H., \u0026amp; Rauch, S. L. (2000). Differential response in the human amygdala to racial outgroup vs ingroup face stimuli. \u003cem\u003eNeuroreport\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(11), 2351\u0026ndash;2354.\u003c/li\u003e\n\u003cli\u003eJackson, R. L., Hoffman, P., Pobric, G., \u0026amp; Ralph, M. A. L. (2016). The semantic network at work and rest: Differential connectivity of anterior temporal lobe subregions. \u003cem\u003eJournal of Neuroscience\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(5), 1490\u0026ndash;1501.\u003c/li\u003e\n\u003cli\u003eKane, M. J., \u0026amp; Engle, R. W. (2003). Working-memory capacity and the control of attention: The contributions of goal neglect, response competition, and task set to Stroop interference. \u003cem\u003eJournal of Experimental Psychology: General\u003c/em\u003e, \u003cem\u003e132\u003c/em\u003e(1), 47\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eKroll, J. F., \u0026amp; Stewart, E. (1994). Category interference in translation and picture naming: Evidence for asymmetric connections between bilingual memory representations.\u003cem\u003e Journal of Memory and Language\u003c/em\u003e, 33, 149\u0026ndash;174.\u003c/li\u003e\n\u003cli\u003eLe, T. M., Wang, W., Zhornitsky, S., Dhingra, I., Zhang, S., \u0026amp; Li, C. S. R. (2020). Interdependent neural correlates of reward processing and response inhibition during reward and punishment sensitivity.\u003cem\u003e \u003c/em\u003e\u003cem\u003eCortex, 30\u003c/em\u003e(3), 1662\u0026ndash;1676. \u003c/li\u003e\n\u003cli\u003eLi, M., Xu, Y., Luo, X., Zeng, J., \u0026amp; Han, Z. (2020). Linguistic experience acquisition for novel stimuli selectively activates the neural network of the visual word form area. \u003cem\u003eNeuroimage\u003c/em\u003e, \u003cem\u003e215\u003c/em\u003e, 116838.\u003c/li\u003e\n\u003cli\u003eLi, W., Qin, W., Liu, H., Fan, L., Wang, J., Jiang, T., \u0026amp; Yu, C. (2013). Subregions of the human superior frontal gyrus and their connections. \u003cem\u003eNeuroImage, 78, \u003c/em\u003e46\u0026ndash;58.\u003c/li\u003e\n\u003cli\u003eLiu, Y., Shi, G., Li, M., Xing, H., Song, Y., Xiao, L., Guan, Y., \u0026amp; Han, Z. (2021). Early top-down modulation in visual word form processing: evidence from an intracranial SEEG study. \u003cem\u003eJournal of Neuroscience\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(28), 6102\u0026ndash;6115.\u003c/li\u003e\n\u003cli\u003eLiu, D., Xing, Z., Huang, J., Schwieter, J. W., \u0026amp; Liu, H. (2023). Genetic bases of language control in bilinguals: Evidence from an EEG study. \u003cem\u003eHuman Brain Mapping,\u003c/em\u003e \u003cem\u003e44\u003c/em\u003e(9), 3624\u0026ndash;3643.\u003c/li\u003e\n\u003cli\u003eLiu, H., Li, W., Zuo, M., Wang, F., Guo, Z., \u0026amp; Schwieter, J. W. (2022). Cross-task adaptation effects of bilingual language control on cognitive control: a dual-brain EEG examination of simultaneous production and comprehension. \u003cem\u003eCerebral Cortex\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(15), 3224\u0026ndash;3242.\u003c/li\u003e\n\u003cli\u003eLinke, J., Kirsch, P., King, A. V., Gass, A., Hennerici, M. G., Bongers, A., \u0026amp; Wessa, M. (2010). Motivation modulates neural responses to rewards. \u003cem\u003eNeuroImage, 49\u003c/em\u003e(3), 2618\u0026ndash;2625.\u003c/li\u003e\n\u003cli\u003eMcLaren, D. G., Ries, M. L., Xu, G., \u0026amp; Johnson, S. C. (2012). A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches. \u003cem\u003eNeuroimage\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(4), 1277\u0026ndash;1286.\u003c/li\u003e\n\u003cli\u003eParadis, M. (2009). \u003cem\u003eDeclarative and procedural determinants of second languages\u003c/em\u003e. Benjamins.\u003c/li\u003e\n\u003cli\u003eRivas-Fern\u0026aacute;ndez, M. \u0026Aacute;., Varela-L\u0026oacute;pez, B., Cid-Fern\u0026aacute;ndez, S., \u0026amp; Galdo-\u0026Aacute;lvarez, S. (2021). Functional activation and connectivity of the left inferior frontal gyrus during lexical and phonological retrieval. \u003cem\u003eSymmetry\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(9), 1655.\u003c/li\u003e\n\u003cli\u003eTagarelli, K. M., Shattuck, K. F., Turkeltaub, P. E., \u0026amp; Ullman, M. T. (2019). Language learning in the adult brain: A neuroanatomical meta-analysis of lexical and grammatical learning. \u003cem\u003eNeuroImage\u003c/em\u003e, \u003cem\u003e193\u003c/em\u003e, 178\u0026ndash;200.\u003c/li\u003e\n\u003cli\u003eTillikainen L, Salli E, Korvenoja A, Aronen H (2006) A cluster mass permutation test with contextual enhancement for fMRI activation detection. \u003cem\u003eNeuroImage, 32\u003c/em\u003e(2), 654\u0026ndash;664.\u003c/li\u003e\n\u003cli\u003eUllman, M. T. (2005). A cognitive neuroscience perspective on second language acquisition: The declarative/procedural model. In C. Sanz (Ed.), \u003cem\u003eMind and context in adult second language acquisition \u003c/em\u003e(pp. 141\u0026ndash;178). Georgetown University Press.\u003c/li\u003e\n\u003cli\u003eUllman, M. T. (2014). The declarative/procedural model: A neurobiologically-motivated theory of first and second language. In B. VanPatten \u0026amp; J. Williams (Eds.), \u003cem\u003eTheories of second language acquisition: An introduction\u003c/em\u003e (2nd edition) (pp. 135\u0026ndash;158). Erlbaum.\u003c/li\u003e\n\u003cli\u003eWoo C, Krishnan A, Wager T (2014) Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations.\u003cem\u003e NeuroImage, 91, \u003c/em\u003e412\u0026ndash;419.\u003c/li\u003e\n\u003cli\u003eXiang, H., Lin, C., Ma, X., Zhang, Z., Bower, J. M., Weng, X., \u0026amp; Gao, J. H. (2003). Involvement of the cerebellum in semantic discrimination: An fMRI study. \u003cem\u003eHuman Brain Mapping\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(3), 208\u0026ndash;214.\u003c/li\u003e\n\u003cli\u003eYan, C. G., Wang, X. D., Zuo, X. N., \u0026amp; Zang, Y. F. (2016). DPABI: Data processing \u0026amp; analysis for (resting-state) brain imaging. \u003cem\u003eNeuroinformatics\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 339\u0026ndash;351.\u003c/li\u003e\n\u003cli\u003eZobl, H. (1998). Representational changes: From listed representations to independent representations of verbal affixes. In M.-L. Beck (Ed.), \u003cem\u003eMorphology and its interfaces in second language knowledge \u003c/em\u003e(pp. 339\u0026ndash;371). Benjamins.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lexical form-meaning mapping, Disturbance, Language learning experience, fMRI","lastPublishedDoi":"10.21203/rs.3.rs-4015255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4015255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndividuals learn the meaning of words mainly through feedback from others at early stages, but confusing feedback may cause disturbances in establishing lexical form-to-meaning mappings. To date, little is known about how these mappings are preciously established as language learning experiences and proficiency increase. To this end, we asked participants to perform a picture-word matching task under disturbance and non-disturbance conditions during functional magnetic resonance imaging (fMRI). Brain imaging revealed that in the non-disturbance condition, more brain network connections emerged during early (na\u0026iuml;ve) learning than later (expert) learning. However, in the disturbance condition, more connections were found during expert learning compared to na\u0026iuml;ve learning. Correspondingly, the behavioral results showed that as learning experiences increase in the disturbance condition, so do accuracy rates. Together, these findings indicate that with increased experience in mapping lexical forms to meanings, individuals appear to become less sensitive to disturbances by engaging multiple brain areas.\u003c/p\u003e","manuscriptTitle":"The Effect of Disturbance on the Neural Mechanisms of Learning Word Formation Rules in a Novel Language","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-07 18:23:55","doi":"10.21203/rs.3.rs-4015255/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"463a464b-d0a9-4c95-aefe-4d019dfe8fe0","owner":[],"postedDate":"March 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-19T08:29:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-07 18:23:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4015255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4015255","identity":"rs-4015255","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}