Central facial emotion accelerates automatic processing of multiple facial emotions: An EEG study

Central facial emotion accelerates automatic processing of multiple facial emotions: An EEG study | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 November 2025 V1 Latest version Share on Central facial emotion accelerates automatic processing of multiple facial emotions: An EEG study Authors : Menghui Xiong , Xiaobin Ding , Liping Hu , Jianhui Liang , and Yan Huang 0000-0003-1387-3727 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176301995.50140053/v1 198 views 85 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The automatic processing of multiple facial expressions (MFE) is critical for human survival and social interactions, with behavioral studies documenting a central advantage in MFE perception—yet its neural mechanisms remain unclear. Using visual mismatch negativity (vMMN, an event-related potentials index of automatic processing) and an oddball paradigm, we investigated how central facial emotion regulates MFE automatic processing across three EEG experiments with 48 total Chinese college students (24 in Experiments 1/2, 24 in Experiment 3). In Experiment 1, participants completed a central face gender-detection task while viewing MFE stimuli—four peripheral faces and one central face. The central face was either emotional (happy or surprised, congruent with peripheral faces) or neutral. Results showed an acceleration effect of central emotion: vMMN latency was earlier for central emotional faces than neutral ones, with no amplitude difference. Experiment 2 used single central emotional faces and revealed MFE elicited shorter vMMN latency than single faces. Experiment 3 adopted a face-irrelevant color-detection task and replaced central neutral faces with scrambled faces; it replicated the acceleration effect of central emotion. A negativity priority also emerged: surprise elicited earlier vMMN than happiness across experiments. Collectively, these findings demonstrate that central facial emotion acts as a pre-attentive “catalyst” for MFE processing, while revealing two key features—rapid MFE ensemble coding and threat prioritization—that support adaptive social cognition. Central facial emotion accelerates automatic processing of multiple facial emotions: An EEG study Menghui Xiong a, b , Xiaobin Ding c , Liping Hu a , Jianhui Liang a , Yan Huang a, d, * a Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , 518055 , Shenzhen, China. b Faculty of Psychology, Southwest University, 400715, Chongqing, China. c Psychology Department, Northwest Normal University, 730070, Lanzhou, China. d University of Chinese Academy of Sciences,100049, Beijing, China. Correspondence should be addressed to: Yan Huang, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, 518055, Shenzhen, China; Email: [email protected] ; Phone: (86)755-26039158 CRediT authorship contribution statement Menghui Xiong: Conceptualization, Data curation, Formal analysis, Visualization, Writing–original draft. Xiaobin Ding: Methodology. Liping Hu: Methodology, Funding acquisition. Jianhui Liang: Data curation. Yan Huang: Conceptualization, Methodology, Formal analysis, Funding acquisition, Supervision, Writing–review and editing. Funding sources This study was supported by National Science and Technology Major Project (2025ZD0215000), National Natural Science Foundation of China (32371091), Basic and Applied Basic Research Foundation of Guangdong Province (2024A1515010529, 2023A1515012642), STI2030-Major Projects (2022ZD0209500), Guangdong Provincial Key Laboratory of Brain Connectome and Behavior (2023B1212060055), and CAS Key Laboratory of Brain Connectome and Manipulation (2019DP173024). Data statement We report how we determined our sample size, all data exclusions, all manipulations, and all measures in the study. The data and research materials corresponding to this research are available in the supplementary materials. Conflict of interests The authors declare that they have no competing interests. Running Head: Central emotions accelerate multi face processing Abstract The automatic processing of multiple facial expressions (MFE) is critical for human survival and social interactions, with behavioral studies documenting a central advantage in MFE perception—yet its neural mechanisms remain unclear. Using visual mismatch negativity (vMMN, an event-related potentials index of automatic processing) and an oddball paradigm, we investigated how central facial emotion regulates MFE automatic processing across three EEG experiments with 48 total Chinese college students (24 in Experiments 1/2, 24 in Experiment 3). In Experiment 1, participants completed a central face gender-detection task while viewing MFE stimuli—four peripheral faces and one central face. The central face was either emotional (happy or surprised, congruent with peripheral faces) or neutral. Results showed an acceleration effect of central emotion: vMMN latency was earlier for central emotional faces than neutral ones, with no amplitude difference. Experiment 2 used single central emotional faces and revealed MFE elicited shorter vMMN latency than single faces. Experiment 3 adopted a face-irrelevant color-detection task and replaced central neutral faces with scrambled faces; it replicated the acceleration effect of central emotion. A negativity priority also emerged: surprise elicited earlier vMMN than happiness across experiments. Collectively, these findings demonstrate that central facial emotion acts as a pre-attentive “catalyst” for MFE processing, while revealing two key features—rapid MFE ensemble coding and threat prioritization—that support adaptive social cognition. Keywords: multiple facial expressions, emotion, visual mismatch negativity (vMMN), automatic processing, central visual field, ERP Introduction The automatic processing of facial expressions is of significant social and survival importance (Ekman et al., 1983; Kragel et al., 2021; Wang et al., 2023). Facial expressions convey crucial information, particularly negative expressions such as fear and surprise, which serve as indicators of potential threats in the surrounding environment. In everyday situations, it is common to encounter scenarios that necessitate the simultaneous processing of multiple facial expressions (MFE). For example, during public speaking engagements, it is essential for speakers to rapidly perceive the overall emotional state of their audience. Previous behavioral studies have demonstrated that individuals can rapidly and automatically extract ensemble information from MFE, including both the average expression and the variance in expression among faces (Haberman & Whitney, 2007, 2009; Utochkin et al., 2024). For instance, humans exhibit the ability to swiftly extract the average emotion from MFE, processing the overall emotion of 24 faces in just 100 ms (Yang et al., 2013). This suggests that the extraction of overall emotion from MFE possesses a distinct neural mechanism that is not dependent on individual face processing (Whitney & Yamanashi Leib 2018). While behavioral studies highlight the processing uniqueness of MFE, neuroimaging approaches provide insights into the neural underpinnings of MFE processing. For instance, a functional magnetic resonance imaging (fMRI) study has found that the processing of MFE primarily occurs in the dorsal pathway, while the processing of individual faces mainly occurs in the ventral pathway (Im et al., 2017). Electroencephalogram (EEG) studies have indicated that ensemble perception of MFE can be processed automatically (Li et al., 2018; Wang et al., 2016). Moreover, behavioral studies have indicated that faces within the central visual field contribute to a greater proportion of ensemble perception in MFE compared to peripheral faces (Dandan et al., 2023; Ji et al., 2014). However, the neural mechanism underlying the higher weight of central faces in MFE perception is still unknown. Previous MFE neural mechanism studies mainly presented face stimuli to the peripheral visual field (Chen et al., 2020; Im et al., 2017; Wang et al., 2016). The present study uses the oddball paradigm and an ERP index for automatic processing to explore the impact of central facial emotion processing on overall MFE emotion processing. This will facilitate a deeper understanding of the neural mechanisms underlying the automatic integration of facial emotion information in MFE. The visual mismatch negativity (vMMN) component, widely recognized as a direct neurophysiological indicator of automatic processing, has also been used in studies exploring the automatic processing of emotions (Kovarski et al., 2017, 2021; Stefanics et al., 2012; Zeng et al., 2022). The unattended MFE presented in the peripheral visual field has been demonstrated to elicit vMMN. Moreover, the activation of vMMN can be observed even for unconscious visual stimuli (Flynn et al., 2017). The vMMN is elicited by infrequent events within a sequence of visual stimuli and is typically assessed using the passive oddball paradigm (Kovarski et al., 2021), where the infrequent oddball events (deviant stimuli) are presented as task-irrelevant stimuli embedded within a sequence of frequent stimuli (standard stimuli). To reduce potential interference resulting from physical disparities such as brightness, identity, and gender, we adopted a “reverse block” presentation approach, in which the standard and deviant stimuli swap roles in the reverse block to ensure that the vMMN solely reflects the variations in the frequency of identical stimuli, rather than the dissimilarities in other attributes (Chen et al., 2020; Li et al., 2018; Stefanics et al., 2012). Therefore, the present study adopted this reverse-block oddball paradigm to investigate the automatic MFE processing through vMMN. To address these questions, this study conducted three EEG experiments, all adopting the oddball paradigm with “forward-reverse” blocks to control for physical stimulus interference (e.g., differences in facial identity, brightness, and gender). This paradigm swaps the roles of standard and deviant stimuli across different blocks, ensuring that the vMMN reflects only differences in emotional processing rather than physical attribute differences (Chen et al., 2020; Li et al., 2018). In Experiment 1, participants performed a gender detection task on central faces while viewing MFE stimuli consisting of four peripheral faces and one central face. The central faces were divided into two conditions: emotional condition (either happy or surprised, consistent with peripheral faces) and non-emotional condition (neutral faces). By comparing the latency (reflecting processing speed) and amplitude (reflecting processing intensity) of the vMMN between the two conditions, the regulatory mode of central emotion was examined. Experiment 2 focused on single-face processing, presenting isolated happy or surprised faces. By comparing vMMN differences between single faces and MFE, this study verified whether MFE exhibits a faster automatic processing speed, which would confirm that its automatic processing possesses a distinct mechanism. Experiment 3 adjusted Experiment 1 by replacing central neutral faces with scrambled faces (to eliminate potential interference from neutral emotions) and adopting a task completely unrelated to faces (color detection of the central fixation point) to ensure the fully automatic processing of MFE. By systematically addressing these questions, this study aimed to clarify the neural mechanism of the central advantage in automatic MFE processing, to enrich our understanding of the vMMN’s role in complex emotional processing, and to provide a physiological basis for explaining how humans efficiently navigate social environments via rapid, automatic processing of collective emotion. Materials and methods Participants Forty-eight college students were recruited for the study, with 24 participants in Experiments 1 and 2 (12 males), and 24 in Experiment 3 (11 males). All participants reported normal or corrected-to-normal vision, normal color vision, and right-handedness. Participants were recruited via campus posters. There were no significant differences in gender (χ 2 = 0.08, p = 0.77), age, anxiety levels, or depression levels between the participants in Experiments 1/2 and 3 (see Table 1 ). Previous research has indicated that elevated levels of anxiety or depression may be linked to negative emotional bias (LeMoult & Gotlib, 2019; Wieser & Keil, 2020), potentially impacting the automatic processing of emotions. State anxiety levels and depression levels were respectively assessed using the State Anxiety Inventory (S-AI) (Spielberger et al., 1971), and Beck Depression Scale-II (Beck et al., 1961). As depicted in Table 1 , our participants’ levels of anxiety and depression were in the low to moderate range for anxiety and do not indicate any presence of depressive symptoms (Bailen et al., 2019; Spielberger et al., 1971). A statistical power analysis was conducted using G*Power 3.1.9.4 (Faul et al., 2007) for a within-subject design with the following parameters: effect size f = 0.25, alpha level (α) = 0.05, and statistical power = 0.95. The results indicated that a minimum of 23 participants is necessary to achieve adequate power for the study. To account for potential invalid data, we enrolled 24 participants in each experiment. The study received approval from the Human Research Ethics Committee of the Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences, and adhered to the principles outlined in the Helsinki Declaration. Informed consent was obtained from all participants prior to their involvement in the experiment. Participants were given monetary compensation for their time. Table 1 Demographic and psychological measure comparisons (age, anxiety, depression) across experiments Experiment 1/2 Experiment 3 t p Age 22.46 ± 2.92 23.29 ± 2.18 -1.12 0.268 Anxiety 30.00 ± 6.76 32.38 ± 6.55 -1.24 0.222 Depression 3.46 ± 2.92 4.83 ± 4.53 -1.25 0.217 Note. Values are presented as M ± SD . Anxiety: State Anxiety scale; Depression: Beck Depression scale-II. Stimuli Stimuli included happy (positive), surprised (negative), and neutral faces, selected from the emotional face database (Ma et al., 2020). A total of 32 facial stimuli were used (16 males, 16 females), comprising 14 happy, 14 surprised, and 4 neutral faces. Compared with other negative emotions, surprised and happy faces show better matching in perceptual similarity and emotional intensity (Ma et al., 2020), which is why we selected surprise faces as negative stimuli. Importantly, surprise—as a basic emotion with dual valence (An et al., 2017)—encompasses both positive manifestations (e.g., pleasant reactions to unexpected rewards) and negative ones (e.g., startle responses to sudden threats). For the current study, we specifically chose negatively valenced surprise faces. To validate their emotional properties, 30 naive participants rated valence (1 = most negative, 7 = most positive) and intensity (1 = weakest, 7 = strongest) of the selected faces (Bailen et al., 2019). As shown in Fig. 1 , valence differed significantly across the three emotion types: happy (5.61 ± 0.34) > neutral (3.50 ± 0.17) > surprised (2.32 ± 0.21; all p s < 0.001), confirming the negative valence of surprised faces. Emotional intensity was matched between happy (5.01 ± 0.50) and surprised (4.77 ± 0.31) faces ( p = 0.352), with both being higher than neutral faces (3.07 ± 0.19; both p s < 0.001). All grayscale faces were standardized for luminance and spatial frequency using the SHINE toolbox (Willenbockel et al., 2010). Fig. 1 Valence and intensity ratings of happy, surprised and neutral faces ( M ± SD ). *** p < 0.001 In Experiment 1, we used MFE stimuli comprised five distinct faces to investigate how central emotional signals influence automatic MFE processing via vMMN. The four peripheral faces (2 males, 2 females) displayed either happy (positive) or surprised (negative) emotions, while the central face (male or female) showed either a matching emotion to the periphery or a neutral expression. Notably, the overall emotion of MFE displays is determined by their dominant expressions (Haberman & Whitney, 2010; Whitney & Yamanashi Leib, 2018), so the overall emotion of current MFE stimuli was consistent with peripheral emotions. This resulted in four conditions ( Fig. 2A ): overall happy-central happy (HH), overall surprised-central surprised (SS), overall happy-central neutral (Hn), and overall surprised-central neutral (Sn). All faces were presented on a gray background, viewed from 60 cm, with each face subtending 4.9° × 6.9°; peripheral faces had an eccentricity of 8°. In Experiment 2, only a single happy (H) or surprised (S) face was presented at the screen center, with all other settings identical to Experiment 1. In Experiment 3, based on Experiment 1, central neutral faces were replaced with scrambled faces (generated by randomly shuffling neutral facial components from Experiment 1), resulting in two conditions: overall happy-central scrambled (Hs) and overall surprised-central scrambled (Ss). The HH and SS conditions remained unchanged, and all other stimulus settings matched Experiment 1. Fig. 2 Examples of stimuli and experimental procedures. (A) Examples of MFE stimuli used in Experiments 1, 2 and 3. (B) Experimental procedures for the three Experiments. The task of Experiments 1 and 2 was gender-specific detection, while that in Experiment 3 was red fixation detection. (C) Schematic of the oddball sequence of “forward-reverse” blocks in Experiment 1. Procedure In all three experiments, an oddball paradigm with a ”reverse block” design was used to present facial stimuli. The vMMN component captures the automatic processing of faces by detecting the differences in brain electrical activity associated with the same stimulus under varying probabilities. Each trial began with the presentation of face stimuli for 300 ms, followed by a gray screen with a central fixation cross, which was displayed for 450–650 ms ( Fig. 1B ). In Experiment 1, participants were instructed to focus on the central facial stimulus and complete a gender discrimination task for this stimulus. Half of the participants were required to press the ”J” key promptly when a male central face appeared, while the other half pressed this key in response to a female central face. The central faces were predominantly of the same gender, with a minority of target faces (5% probability) of the opposite gender. Furthermore, it was ensured that the two consecutive MFE stimuli did not share the same facial identities. Prior to the formal experiments, participants completed a practice session to ensure sustained fixation to the central face; eye movement was monitored via electrooculography. Experiment 1 consisted of four blocks, each containing 360 trials (standard stimulus [std], probability = 80%; deviation stimulus [dev], probability = 20%). Trials with the target stimulus, which were embedded within the standard (probability = 4%) and deviant stimuli (probability = 1%), were excluded from subsequent analyses (Kovarski et al., 2017). The order of the four blocks (2 forward and reverse × 2 emotion and non-emotion) was randomized among participants. The standard stimulus in the emotional forward block was the HH stimulus [StdHH], while the deviation stimulus was the SS stimulus [DevSS]. Conversely, in the corresponding reverse block, the standard stimulus [StdSS] was paired with the deviation stimulus [DevHH]. In the non-emotional forward block, the standard stimulus was the Hn stimulus [StdHn], and the deviation stimulus was the Sn stimulus [DevSn]. Similarly, in the reverse block, the standard stimulus [StdSn] was paired with the deviation stimulus [DevSn]. We investigated automatic MFE processing using the vMMN component, as indicated by the difference waves between deviant and standard stimuli. For instance, the vMMN induced by MFE under the condition of central happy emotion was obtained by subtracting StdHH from DevHH. In Experiment 2, only two central emotional blocks were incorporated. The experimental procedure was identical to that of Experiment 1 ( Fig. 1B ). Regarding Experiment 3, its procedure was largely the same as that of Experiment 1, except that neutral faces were substituted with scrambled facial stimuli, and the task was adjusted to a fixation color detection task at the center. Specifically, when the fixation color shifted from black to red, participants were directed to press the “J” key to render a judgment. EEG recording and preprocessing EEG signals were recorded using a 64-channel Hydrocel Geodesic Sensor Net EEG system (EGI, Eugene, USA). Horizontal and vertical electrooculograms (EOGs) were recorded from the outer canthi of the eyes and from above and below the left eye. The sampling rate was 500 Hz, the online reference electrode was Cz, and the impedance of all electrodes was kept below 50 kΩ (Ferree et al., 2001). We preprocessed EEG data using the EEGLAB13.0.0b toolbox in MATLAB (MathWorks, Natick, MA), using a whole brain average reference and a bandpass filter set from 0.1 to 30 Hz. Data were then segmented from -100 ms pre-stimulus to 600 ms post-stimulus onset, with baseline correction applied from -100 ms pre-stimulus to 0 ms. To control for horizontal eye movements, we rejected epochs with signals exceeding ± 30 μV at the difference waves of electrodes F9/10 (Hu et al., 2023). The proportion of exclusions accounted for approximately 3% of the total trials. And to further avoid other potential artifacts, we rejected epochs with values exceeding ±100 μV on any channel (Stefanics et al., 2018). Independent component analysis (ICA) was utilized to identify and remove artifacts associated with vertical eye movements and blinks. In Experiment 1, the number of accepted trials ( M ± SD ) was 172 ± 35 (StdHH), 37 ± 9 (DevSS), 175 ± 38 (StdSS), 40 ± 10 (DevHH), 174 ± 36 (StdHn), 39 ± 10 (DevSn), 169 ± 39 (StdSn), 36 ± 10 (DevHn). In Experiment 2, the number of accepted trials was 179 ± 29 (StdH), 41 ± 8 (DevS), 188 ± 28 (StdS), 43 ± 7 (DevH). In Experiment 3, the number of accepted trials was 188 ± 31 (StdHH), 47 ± 8 (DevSS), 190 ± 30 (StdSS), 48 ± 8 (DevHH), 184 ± 32 (StdHs), 46 ± 8 (DevSs), 197 ± 20 (StdSs), 49 ± 7 (DevHs). The number of valid trials for all conditions exceeded 30, allowing for a stable ERP component to be averaged across trials (Huffmeijer et al., 2014). Data analyses The ERP was calculated by averaging epochs for the conditions in Experiment 1 (SS, HH, Sn, and Hn), in Experiment 2 (S, H) and in Experiment 3 (SS, HH, Ss, Hs). vMMN was defined as the negative difference waves obtained by subtracting the standard ERPs from the corresponding deviation ERPs for each condition. Expression-related vMMN typically manifests in the temporal-occipital region within the time window of 100–400 ms (Chen et al., 2020; Li et al., 2018; Li et al., 2012; Stefanics et al., 2012), with variations in latent period and waveform characteristics observed across different studies (Kimura et al., 2012; Kovarski et al., 2017). Due to the inconsistent time window of vMMN and the varying spatial and temporal distribution characteristics, a data-driven approach utilizing a cluster-based permutation test (Maris & Oostenveld, 2007; Xiong et al., 2022) was employed to determine significant time windows of vMMN. Consistent with the prior investigations of emotion-related vMMN (Chen et al., 2020; Ding et al., 2022), and upon visual examination of the vMMN topographic maps, Experiments 1 and 3 revealed that the vMMN component exhibited its greatest significance and stability at electrodes P7/8 located on the temporal-occipital lobe. In Experiment 2, the vMMN component displayed the greatest significance and stability solely at electrode P8. Hence these specific electrodes were chosen for the analysis of vMMN. Permutation tests (5000 times) were performed to determine the significant vMMN window during 100–400 ms. We presented the time window, cumulative t -values, and level of significance within significant clusters of vMMN activity. Given our focus on early automatic processing, we analyzed the first vMMN peak. For this peak, peak latency and peak amplitude were defined as the time point and amplitude corresponding to the maximum deflection (i.e., the most negative value) within the first effective time window of the vMMN. Mean amplitude was calculated as the average value across this same time window. Where necessary, P-values were corrected for multiple comparisons using the Bonferroni method. Statistical analyses were performed using SPSS 21.0 (IBM Corp., Armonk, NY, USA) for ANOVAs and t-tests, EEG data preprocessing was conducted via EEGLAB 13.0.0b (MathWorks, Natick, MA, USA), and power analysis was completed using G*Power 3.1.9.4 (Faul et al., 2007). Results The average hit rates (M ± SD) were 95.72 ± 4.30 (Experiment 1), 95.49 ± 5.21 (Experiment 2), and 96.53 ± 4.05 (Experiment 3). Across three experiments, no significant differences in hit rates were observed among the blocks (all p s > 0.128). These high hit rates indicated that participants were fully engaged in each task. Experiment 1 3.1.1. MFE evoked significant vMMN activity The four conditions evoked significant vMMN activity ( Fig. 3A ). Cluster-based permutation tests indicated significant vMMN windows: for the SS condition, between 164–196 ms ( t = -46.21, p = 0.041); for the Sn condition, between 262–318 ms ( t = -82.02, p = 0.004); for the HH condition, between 244–284 ms ( t = -48.09, p = 0.039); and for the Hn condition, between 318–380 ms ( t = -83.82, p = 0.011). To test how central emotional faces modulate MFE automatic processing, we analyzed vMMN latency and amplitude in Experiment 1. 3.1.2. MFE Triggers Earlier vMMN in Central Emotional vs. Neutral Conditions The peak latencies of vMMN were analyzed using a two-way ANOVA, with the central face (emotional and neutral) and overall emotion (surprised and happy) as factors. The detailed results are presented in Table 2 . A significant main effect of the central face was detected, indicating that automatic MFE processing triggered vMMN activity earlier in response to the central emotion condition than in the central non-emotion condition ( M ± SE : 223 ± 2 ms vs. 318 ± 3 ms, p < 0.001). This finding suggests an “acceleration effect”, whereby the central facial emotion accelerated automatic MFE processing ( Fig. 3B ). Moreover, a significant main effect of overall emotion was noted, with the surprised expression evoking vMMN activity earlier than the happy expression (236 ± 3 ms vs. 305 ± 3 ms, p < 0 .001), suggesting a “negativity bias”. Additionally, a significant interaction between the central face and overall emotion was observed, suggesting that the acceleration effect was more pronounced when presented with a central surprised face compared to a central happy face (106 ± 5 ms vs. 82 ± 5 ms, p = 0.003) ( Fig. 3B ). Subsequent post hoc analyses with Bonferroni correction revealed statistically significant differences between SS and Sn, as well as between HH and Hn ( p s < 0.001), indicating a significant acceleration effect for both happy and surprised central faces. Fig. 3 ERPs across four different conditions of Experiment 1. (A) ERPs evoked by deviant stimuli (Dev, black solid line), standard stimuli (Std, black dotted line), and vMMN (red solid line)—derived by subtracting the standard ERPs from their corresponding deviation ERPs. Significant vMMN time windows are marked with blue solid lines, and their peak latencies are indicated by red vertical dotted lines; topographic maps of vMMN in each significant time window are presented. (B) vMMN peak latency. Left: vMMN peak latency in the central emotional condition was earlier than that in the central non-emotional condition. Right: vMMN peak latency for the SS, Sn, HH, and Hn conditions. Note: SS = overall surprised-central surprised; Sn = overall surprised-central neutral; HH = overall happy-central happy; Hn = overall happy-central neutral. *** p < 0.001, ** p < 0.01 Table 2 Statistical results of ANOVAs for peak latency in Experiments 1 and 3 ANOVA factors Experiment 1 Experiment 3 F p η 2 p F p η 2 p central face 639.91 <0.001 0.965 87.16 <0.001 0.791 overall emotion 297.83 <0.001 0.928 872.85 <0.001 0.974 central face × overall emotion 10.87 0.003 0.321 0.33 0.574 0.014 3.1.3. Central Facial Emotion Exerts No Effect on vMMN Amplitude The same ANOVA was also used to analyze the peak amplitude and average amplitude of vMMN. The results showed that neither of the main effects or the interaction reached statistical significance (all p s > 0.2), indicating that central facial emotion did not have an impact on the intensity of automatic MFE processing. Experiment 2 Significant vMMN evoked by single faces Permutation tests revealed that the single face evoked significant vMMN activity ( Fig. 4A ). Specifically, significant vMMN responses were observed for surprised (S) faces during two time windows: 248–284 ms ( t = -56.45, p = 0.022) and 336–400 ms ( t = -88.76, p = 0.002). For happy (H) faces, significant vMMN activity was detected between 288–336 ms ( t = -67.86, p = 0.011). Subsequently, paired samples t-tests were conducted to compare the vMMN parameters—including peak latency, peak amplitude, and average amplitude—across the two facial emotion conditions (surprised vs. happy). Regarding peak latency, surprised faces elicited vMMN responses earlier than happy faces (267 ± 3 ms vs. 311 ± 5 ms; t (23) = -11.03, p 0.1). Fig. 4 ERPs in Experiment 2 and comparisons between Experiment 1 and Experiment 2. (A) ERPs evoked by deviant stimuli (Dev, black solid line), standard stimuli (Std, black dotted line), and vMMN (red solid line). Significant vMMN time windows are marked with blue solid lines, and their peak latencies are indicated by red vertical dotted lines; topographic maps of vMMN in each significant time window are presented. (B) vMMN peak latency elicited by emotional faces in Experiment 1 vs. 2. Note: MFE = average vMMN from central emotional conditions (SS, HH) in Experiment 1; Single face = average vMMN from single faces (S, H) in Experiment 2. SS = overall surprised-central surprised; HH = overall happy-central happy; S = single surprised; H = single happy. *** p < 0.001 Faster Automatic Emotional Processing for MFE Than Single Facial Expression To compare the automatic emotional processing of MFE and single face, we analyze HH, SS (Experiment 1), H and S (Experiment 2) conditions. For peak latency, peak amplitude, and average amplitude, we performed two-way ANOVAs with emotion (surprise, happiness), face number (multiple, single) as factors. For the peak latency, a significant main effect of emotion emerged, F (1, 23) = 431.75, p < 0.001, η 2 p = 0.949, indicating that surprise induced vMMN activity earlier than happiness (225 ± 2 ms vs. 288 ± 2 ms). Critically, MFE evoked vMMN earlier than single faces (223 ± 2 ms vs. 289 ± 3 ms, Fig. 4B ), F (1, 23) = 264.81, p < 0.001, η 2 p = 0.920—supporting that the “MFE advantage” reflects a distinct neural mechanism for automatic MFE processing. The emotion × face number interaction was also significant, F (1, 23) = 52.85, p < 0.001, η 2 p = 0.697. Simple effect analysis confirmed the MFE advantage was significant for both emotions, but larger for surprise than happiness (84 ± 5 ms vs. 47 ± 5 ms, p s 0.1). Experiment 3 Central Facial Emotion Accelerates MFE Processing in a Face-Unrelated Task In Experiment 1, a gender detection task was employed. To render the task fully face-irrelevant, Experiment 3 adopted a fixation color detection task. Furthermore, Experiment 1 used neutral faces as the non-emotional condition; however, this may potentially confound the overall emotion of MFE due to the neutral emotional valence. In Experiment 3, scrambled faces were substituted to rule out this confounding effect. All four MFE conditions elicited significant vMMN activity ( Fig. 5A ). Permutation tests identified significant vMMN time windows: 198–238 ms ( t = -58.90, p = 0.024) and 314–350 ms ( t = -46.99, p = 0.037) for the SS condition; 226–262 ms ( t = -55.12, p = 0.025) for the Ss condition; 280–310 ms ( t = -46.63, p = 0.036) for the HH condition; and 306–346 ms ( t = -68.32, p = 0.012) for the Hs condition. VMMN peak latency, peak amplitudes, and average amplitudes were analyzed via two-way ANOVA, with factors of central face (emotional and scrambled) × overall emotion (surprised and happy); detailed results are in Table 2 . As shown in Fig. 5B , consistent with Experiment 1, significant main effects of central face and overall emotion were observed for peak latency (emotional vs. scrambled : 256 ± 2 ms vs. 283 ± 2 ms, p < 0.001; surprised vs. happy: 232 ± 1 ms vs. 307 ± 2 ms, p < 0.001). This confirms both the acceleration effect of central facial emotion and a negativity bias towards surprise. However, the central face × overall emotion interaction was non-significant ( p = 0.574), suggesting that controlling potential variance may reduce the difference in the acceleration effect between central surprised and happy emotions. Additionally, consistent with Experiment 1, no significant main effects or interactions were observed for vMMN peak amplitudes or average amplitudes in Experiment 3 (all p s > 0.1). Fig. 5 ERPs under four different conditions in Experiment 3. (A) Deviant stimuli (Dev, black solid line), standard stimuli (Std, black dotted line), and vMMN (red solid line). Significant vMMN time windows and their peak latency are marked with blue solid lines and red dotted lines, respectively; topographic maps of vMMN in each significant time window are also depicted. (B) vMMN peak latency. Left: Experiment 3 reconfirmed the acceleration effect of central facial emotion. Right: No significant difference was observed in the acceleration effect between central surprised and happy faces. Note: SS = overall surprised-central surprised; Ss = overall surprised-central scrambled; HH = overall happy-central happy; Hs = overall happy-central scrambled. *** p 0.05. More Pronounced and Earlier vMMN in Experiment 1 Than in Experiment 3 Compared with Experiment 1’s face gender detection task, Experiment 3 used a color detection task entirely unrelated to face processing. To determine whether task relevance to faces affects automatic MFE processing, we compared shared SS and HH conditions across the two experiments. For vMMN peak latency, peak amplitude, and average amplitude, we conducted two-way ANOVAs with task type (gender detection, color detection) × emotion (SS, HH) as factors. Analysis revealed a significant main effect of task type: MFE processing elicited earlier and larger vMMN peaks during gender detection than color detection [ Table 3 , peak latency: F (1, 46) = 111.66, p < 0.001, η 2 p = 0.708; peak amplitude: F (1, 46) = 12.96, p = 0.001, η 2 p = 0.22; average amplitude: F (1, 46) = 2.37, p = 0.13, η 2 p = 0.049]. A significant main effect of emotion emerged for peak latency ( F (1, 46) = 898.10, p 0.1). Smaller Acceleration Effect in Experiment 3 vs. Experiment 1 The acceleration effect of central emotional faces was approximately 95 ms in Experiment 1 and 27 ms in Experiment 3. To better compare whether the acceleration effect differed statistically between the two experiments, we analyzed its relative indices: e.g., (Sn-SS)/SS for Experiment 1 and (Ss-SS)/SS for Experiment 3. An independent-samples t-test showed that the relative acceleration effect was smaller in Experiment 3 than in Experiment 1 [see Table 3 , t (46) = 11.35, p < 0.001, Cohen’s d = 3.29]. The same pattern was observed for the Hn and Hs conditions [ t (46) = 8.99, p < 0.001, Cohen’s d = 2.60]. These findings confirm that the acceleration effect of central facial emotion on MFE was weaker in Experiment 3 than in Experiment 1. Table 3 Comparisons of vMMN peak latency and relative acceleration effect between Experiments 1 and 3 ( M ± SE ). SS (ms) ( M ± SE ) HH (ms) ( M ± SE ) Relative acceleration effect (surprised) Relative acceleration effect (happy) Exp. 1 183 ± 3 264 ± 3 0.59 ± 0.03 0.32 ± 0.02 Exp. 3 218 ± 3 294 ± 3 0.14 ± 0.02 0.09 ± 0.01 Discussion Using three EEG experiments with vMMN (an index of automatic processing), this study explored how emotional signals of central visual faces regulate MFE automatic processing. Key results are as follows: (1) In Experiments 1 and 3, central emotional faces induced a significant vMMN acceleration effect compared to central neutral/scrambled faces, with no significant amplitude difference—indicating central emotional signals modulate MFE automatic processing primarily by accelerating response speed, rather than enhancing processing intensity. (2) Experiment 2 revealed MFE elicited shorter vMMN latency than single emotional faces, indicating MFE processing has a faster automatic response advantage. (3) A negative bias emerged: the automatic processing of negative emotions (whether from multiple or single faces) proceeded more rapidly than that of positive emotions. Central emotion regulation: the acceleration effect at the pre-attentive stage The vMMN acceleration effect induced by central facial emotion (consistent with MFE emotion) extends prior findings on “central advantage” in MFE processing. While behavioral studies (Dandan et al., 2023; Ji et al., 2014) established that central faces dominate explicit MFE judgments, our vMMN results—an index of pre-attentive processing (Kovarski et al., 2017, 2021)—reveal this influence operates at an earlier, automatic stage. Notably, the effect is characterized by shortened latency without amplitude differences, indicating central emotional signals modulate MFE processing exclusively by accelerating response speed rather than enhancing intensity—distinct from intensity-based emotional modulations reported in previous research (e.g., Kovarski et al., 2021). This specificity aligns with ensemble coding theory (Haberman & Whitney, 2009; Whitney & Yamanashi Leib, 2018), suggesting that the presence of emotion in the central face facilitates the pre-attentive integration of MFE signals. By clarifying this pre-attentive “catalytic” role of central emotions, our study addresses a key gap in prior MFE research, which focused largely on peripheral processing or explicit tasks (Chen et al., 2020; Wang et al., 2016). The acceleration effect was weaker in Experiment 3 (face-irrelevant color task) than Experiment 1 (face-related gender task; role of two factors: (1) reduced attentional allocation to faces in the color task (Petro et al., 2023)—with the effect of attention modulation reflected in the weaker amplitude and longer latency of vMMN in the HH/SS conditions of Experiment 3 compared to Experiment 1; (2) neutral faces in Experiment 1 introducing emotional variance that slowed MFE processing (Goldenberg et al., 2020; Liu et al., 2023), amplifying the contrast between emotional and non-emotional conditions. Regardless of these moderators, the persistence of the effect in Experiment 3 confirms it is not dependent on face-related attention, reinforcing its automatic nature. MFE exhibits an automatic processing advantage over single faces Our finding that MFE elicits shorter vMMN latency than single faces extends the ensemble coding theory of MFE processing, with key distinctions from prior work by Im and colleagues. Im et al. (2017, 2021) documented MFE processing advantages primarily under active emotional tasks (e.g., explicit emotion judgment or intensity rating). In contrast, our study is the first to demonstrate such an advantage in automatic processing (via vMMN) under a non-emotional task. This highlights that MFE’s processing efficiency is not limited to deliberate, task-dependent attention to emotional information but constitutes an inherent feature of pre-attentive visual processing. Negativity bias in automatic processing Our observation of a negative bias—faster automatic processing of negative surprise than happy emotions (via vMMN)—aligns with prior work documenting threat prioritization in pre-attentive emotional processing (Kovarski et al., 2021; Li et al., 2012; Haselton et al., 2015). Kovarski et al. (2021) showed that negative emotional signals elicit stronger and faster automatic neural responses than positive ones, which mirrors our finding that threat-related negative surprise (valence: 2.32 ± 0.21) induced a more pronounced acceleration effect. Notably, this negative bias does not contradict evidence of positive bias in other research contexts. For instance, Calvo et al. (2014) documented a positive bias in active emotion categorization tasks, where happy faces were recognized more efficiently than other expressions. Similarly, Svärd et al. (2012) observed positive bias in tasks requiring explicit emotional memory recall. These inconsistencies may stem from a core moderator: processing stage. Our vMMN indexes pre-attentive automatic processing—where threat prioritization (negative surprise) may drive the bias—whereas positive bias typically emerges in explicit, attention-dependent tasks. This suggests emotional biases are context-adaptive, not universal, tied to processing stage. Neural mechanism hypothesis for MFE processing We propose a tentative neural pathway underlying our findings: (1) the central visual field contributes more to ensemble emotion perception due to its high spatial resolution (Ji et al., 2014); (2) peripheral facial information is integrated into central visual cortex, forming an ensemble representation of MFE (Hasson et al., 2002; Levy et al., 2001); (3) this ensemble signal is routed to subcortical nuclei (e.g., amygdala) for rapid threat evaluation, then to the dorsal visual stream—identified by Im et al. (2017) as critical for MFE processing—to guide adaptive behavior. This pathway aligns with subcortical roles in fast automatic processing (Huang et al., 2020; Kragel et al., 2021; Wang et al., 2023; Pessoa & Adolphs, 2010) and requires validation via neuroimaging (e.g., fMRI) to map cortical-subcortical connectivity. Evolutionary implications Collectively, our findings highlight that MFE’s automatic processing mechanisms are shaped by three key evolutionary pressures: (1) the need to prioritize central social cues (via emotional central faces) for fast collective emotion assessment, as foveal processing of central faces ensures high-fidelity capture of critical social signals (Poletti et al., 2023); (2) the efficiency of ensemble coding over individual face processing in group settings—reducing cognitive load while enabling rapid collective threat/safety judgments (Whitney & Yamanashi Leib, 2018); (3) the urgency of threat detection for survival, reflected in the negativity bias that prioritizes negative MFE processing (Miller et al., 2012). These mechanisms likely facilitated ancestral humans’ success in complex social groups, where collective emotional states directly impacted survival and cooperation, and remain foundational to modern social cognition—from “group emotional contagion” (Barsade, 2002) to rapid interpersonal judgments in crowds. Limitations and Future Directions Three limitations of the current study and corresponding future directions are as follows: First, without explicit modulation of attentional load or direct measurement of attentional engagement, the role of attention on the central emotion acceleration effect remains speculative—future work should incorporate attentional manipulation and measurement to clarify this interaction. Second, the use of natural faces with fixed emotional valence/intensity prevented exploration of whether the acceleration effect scales with emotional salience; adopting data-driven face models (e.g., Holzleitner et al., 2019) for quantitative emotional attribute modulation (e.g., continuous adjustment of intensity) would address this and enhance ecological validity. Third, relying on Chinese participants and stimuli limits cross-cultural generalization—testing diverse cultural groups with matched stimuli is needed to validate the effect’s evolutionary universality. In conclusion, the present study advances understanding of the neural mechanisms of automatic MFE processing, filling gaps via two key innovations: first, it clarifies central emotion specifically accelerates (not intensifies) automatic MFE processing, defining its “catalyst” modulatory role; second, it shows automatic MFE processing precedes single-face processing—underscoring MFE’s unique mechanism. 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Authors Affiliations Menghui Xiong Shenzhen-Hong Kong Institute of Brain Science Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences View all articles by this author Xiaobin Ding Northwest Normal University School of Psychology View all articles by this author Liping Hu Shenzhen-Hong Kong Institute of Brain Science Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences View all articles by this author Jianhui Liang Shenzhen-Hong Kong Institute of Brain Science Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences View all articles by this author Yan Huang 0000-0003-1387-3727 [email protected] Shenzhen-Hong Kong Institute of Brain Science Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences View all articles by this author Metrics & Citations Metrics Article Usage 198 views 85 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Menghui Xiong, Xiaobin Ding, Liping Hu, et al. 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