Inequity Aversion Makes Gains Harder: Evidence from Neural dynamics in the ultimatum game

Inequity Aversion Makes Gains Harder: Evidence from Neural dynamics in the ultimatum game | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Inequity Aversion Makes Gains Harder: Evidence from Neural dynamics in the ultimatum game Lijun Chen, Yue Zhuang, Xiang Gao, Xiaoliu Jiang, Zhihua Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6937568/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 To compare cognitive costs between shifting from “rejecting unfairness to accepting unfairness” versus “accepting fairness to accepting unfairness”, an Ultimatum Game with EEG time-frequency analysis (ERSP) was conducted. Participants either accepted a moderately unfair offer (7:3) after rejecting an extreme unfair one (9:1), requiring suppression of inequity aversion, or accepted the same after a fair offer (5:5), without prior rejection. Behaviorally, acceptance rates did not differ. Neurally, the “reject-to-accept” condition exhibited stronger alpha (7–13 Hz) and beta (13–25 Hz) event-related desynchronization (ERD) in the right temporoparietal junction (rTPJ), indicating higher inhibitory control costs. Findings elucidate adaptive mechanisms balancing emotional aversion and strategic flexibility in social inequality contexts, informing interventions for fairness-sensitive individuals and equitable policy design. Social science/Business and management Social science/Economics Social science/Politics and international relations Social science/Psychology Social science/Science technology and society Social science/Sociology Inequity aversion Ultimatum game EEG time-frequency analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction The ancient Chinese proverb, "People are concerned not with scarcity but with inequality", highlights humanity’s enduring focus on equity. Nevertheless, absolute equity in real-world resource distribution is rare, and individuals frequently encounter inequity. When faced with unfair allocations, humans do not behave as purely rational agents but instead weigh economic gains against fairness concerns (Sun et al., 2019 ). On one hand, the rational economic man hypothesis posits that individuals maximize self-interest; thus, rejecting unfair offers (resulting in zero gain) contradicts this principle. On the other hand, individuals exhibit strong equity preferences, often sacrificing personal gains to oppose inequity—a phenomenon termed inequity aversion (Fehr & Schmidt, 1999 ). Disadvantageous inequity, where one receives less than others, evokes perceptions of inferiority and triggers aversion. Extreme disadvantageous inequity provokes decisive rejection, while slight inequity induces a trade-off between equity and gains, offering insight into the psychological mechanisms underlying such decisions. Although the neural basis of disadvantageous inequity aversion has been extensively studied (Gao et al., 2018 ), the cognitive and neural processes involved in transitioning from aversion to accepting inequity for gains remain poorly understood. Examining differences in cognitive load between decision-making pathways of ‘suppressing inequity aversion to obtain gains’ versus ‘acquiring gains after accepting fair allocations’ addresses whether inequity aversion heightens the difficulty of pursuing profits. By dissecting neural mechanism disparities between high-conflict strategies (suppressing inequity aversion and emotional responses) and low-conflict strategies (adaptive adjustments), this exploration can elucidate individual psychological adaptations to social inequality. The findings can empirically support decision optimization for fairness-sensitive individuals and provide scientific foundations for policymakers to mitigate decision conflicts and foster equitable governance. 1.1 Trade-offs Between Gains and Fairness in Moderately Unfair Contexts The ultimatum game (UG) is a classic paradigm for studying inequity aversion (Güth et al., 1982 ). In this game, a proposer offers a division of resources, and a responder decides to accept or reject it. If the responder accepts the proposer’s allocation, both receive the proposed amounts; if rejected, neither receives anything. According to rational economic theory, individuals would accept all of the offers, yet empirical results showed that responders often rejected unfair proposals. However, based on inequity aversion model's view, individuals compare their payoffs with others' in unfair allocations (Takanashi 2024 ). The more unfair the distribution proposal, the higher the responder's rejection probability (Bernhard et al., 2006 ). Typically, responders reject extremely unfair proposals and accept fair ones. Thus, moderately unfair allocations provoke rich cognitive activity, making them ideal for investigating the psychological mechanisms of equity-gain trade-offs. 1.2 Cognitive Control in Suppressing Inequity Aversion Inequity aversion involves emotional and conflict-control processes (Gao et al., 2018 ; Soutschek & Schubert, 2014 ). Unfair allocation can trigger individuals' negative emotions. For example, disadvantageous inequity may lead to angry, and advantageous inequity may cause guilt. These emotions are key factors influencing individuals' cooperation choices. Disadvantageous inequity evokes negative emotions like jealous, anger, and frustration, leading to rejection (Heffner & FeldmanHall, 2022 ; McAuliffe & Dunham, 2017 ). Neuroimaging studies revealed that rejecting unfair proposals activated the prefrontal cortex (associated with emotion regulation) and anterior insula (involved in negative emotion processing) (Koenigs & Tranel, 2007 ; Sanfey et al., 2003 ). In the study by van't Wout et al. ( 2006 ), it was found that when rejecting unfair proposals, people tended to exhibit stronger skin - conductance responses and became more emotionally aroused. In another study using rats as subjects, those in a disadvantaged inequity context displayed more anxiety-like behaviors and showed increased activity in the medial prefrontal and orbitofrontal cortex (Jeong & Noh, 2024 ). Thus, individuals’ rejection of unfairness is accompanied by intense negative emotions. When facing unfair allocation, individuals have to suppress negative emotional responses to unfairness to overcome inequity aversion, involving conflict regulation and higher cognitive control(De Neys et al., 2011 ; Halali et al. 2011 ). Tabibnia et al. ( 2008 ) found that accepting unfairness activates self-control circuits. During this process, the right ventrolateral prefrontal cortex (involved in emotion regulation) shows increased activity, while the anterior cingulate cortex (linked to negative emotions) shows decreased activity. Thus, accepting unfairness involves more cognitive control, including inhibiting negative emotions. Rejecting unfair proposals indicates inequity aversion, while accepting them requires suppressing inequity aversion and regulating negative emotions, involving cognitive control and conflict resolution. Current researches on cognitive control predominantly focused on inhibitory processes in single-trial decisions. However, decision-making is inherently sequential, engaging more complex cognitive dynamics. In the Ultimatum Game (UG), individuals not only face trade-offs between fairness and gains within individual trials but also exhibit carryover effects from prior decisions (Dong et al., 2014 ). Responses to unfair offers in earlier rounds systematically shaped subsequent choices (Soutschek & Schubert, 2014 ). Confronting unfair allocations, decision-makers experience conflicts between fairness norms and economic gains. According to the conflict monitoring model, the ACC detects such conflicts and signals the dorsolateral prefrontal cortex (dlPFC) to recruit cognitive control resources—such as suppressing anger or evaluating long-term benefits—to adjust behavior (Botvinick et al., 2001 ; Zheng et al. 2018 ). In sequential UG paradigms, shifting from rejecting unfairness (Inequity Aversion, IA) to accepting it (Accepting Inequity, AI) imposes higher cognitive control costs than transitioning from accepting fairness (Accepting Equity, AE) to AI. The IA-to-AI shift requires overriding strong fairness preferences and emotional responses within a loss context (previous rejection), whereas the AE-to-AI transition involves adaptive strategy updates in a gain context (prior acceptance), demanding less suppression of intrinsic fairness norms. This suggests that accepting unfairness after prior rejection necessitates greater mobilization of cognitive resources compared to post-fairness adaptations. 1.3 The Neural Basis of Cognitive Control in Inequity Aversion Trade - offs between gains and fairness involve dynamic allocation of cognitive resources, such as shifting from emotion suppression to value calculation. Behavioral studies only capture decision - making outcomes, but EEG time - frequency analysis can track the timing of this psychological process. Event-related spectral perturbations (ERSP) is a method for time-frequency analysis of EEG, capable of examining changes in brainwave power within specific frequency bands. This analysis reveals event-related desynchronization (ERD) and event-related synchronization (ERS) across various frequency bands, offering insights into different cognitive activities (Tiesinga et al., 2008 ; Wang et al., 2025 ). Among these, the Alpha and Beta bands play a key role in cognitive control. Changes in the Alpha band are associated with top-down cognitive control and its degree (Babiloni et al., 2004 ), with Alpha-ERD reflecting an increase in brain activation (Compton et al. 2011 ) and cognitive activity (Klimesch et al, 2007 ). Beta-band ERS represents the maintenance of the current cognitive state, while Beta-band ERD signifies a change or impending change in cognitive state (Gastaldon et al., 2020 ). Additionally, Beta-band changes are closely linked to inhibitory functions (Beltrán et al., 2019 ; Muralidharan et al., 2019 ). Compared to traditional metrics such as acceptance rate and response time, ERSP offers another perspective for exploring the cognitive control mechanisms during decision-making. Spatially, the Right Temporoparietal Junction (rTPJ) is a key brain region related to inequity aversion. Obeso et al. ( 2018 ) suggested that rTPJ, as a neural hub, modulated the conflict between equity and rewards. Previous studies have shown that transcranial direct current stimulation (tDCS) targeting the rTPJ increased the likelihood of participants making fair decisions (Wu et al., 2023 ). rTPJ is central to the mentalization network and plays an important role in human reciprocal behavior. This study aimed to investigate the neurocognitive costs of strategic decision shifts when individuals trade off fairness norms against economic gains. Using the the UG paradigm, two sequential conditions were contrasted: (1) accepting moderately unfair offers (7:3) after rejecting extremely unfair allocations (9:1) ( Inequity Aversion → Accept Inequity , IAAI), and (2) accepting the same moderate unfairness following prior acceptance of fair splits (5:5) ( Accept Equity → Accept Inequity , AEAI). Condition 1 evoked strong negative emotions, requiring responders to suppress fairness-driven aversion and reconfigure conflict-resolution strategies when accepting subsequent moderate inequity. In contrast, condition 2 involved no prior suppression of fairness norms, as responders encountered fairness-gain conflicts for the first time, enabling adaptive adjustments without strategic overrides. To test this hypothesis, event-related spectral perturbation (ERSP) was employed to analyze EEG oscillations, with a focus on alpha (7–13 Hz) and beta (13–25 Hz) event-related desynchronization (ERD) (Xu & Shi 2020 ). 2 Methods 2.1 Experimental Design The Ultimatum Game (UG) served as the experimental paradigm, employing a within-subject design with a single factor: the allocation scheme (extremely unfair, moderately unfair, and fair). In this experiment, the proposer always allocated 10 Chinese yuan to the responder. The three allocation schemes were as follows: 5:5 (fair), 7:3 (moderately unfair), and 9:1 (extremely unfair). The proposer randomly selected one of the three allocation schemes, ensuring each scheme had an equal probability of being chosen. This design prevented the responder from predicting the proposal in advance, ensuring that the decision-making process occurred only after the proposal was presented. To maintain randomness and equal probability, the proposer’s behavior was simulated by a computer, though the responder was informed that the proposal originated from proposers. Each experiment consisted of 90 valid trials. As illustrated in Fig. 1 , each trial comprised five screens: Cue, Evaluation, Wait, Decision, and Break. During the Decision screen, the responder made an accept or reject decision by pressing a button. Regardless of when the button was pressed, the Decision screen remained visible for 1480 ms. If no response was recorded during this period, the trial was considered invalid. The Break screen was a black screen displayed for 1200 ms, providing a brief interval before the next trial. 2.2 Participants Ethical approval for this experiment was obtained from the institution’s Psychology Research Ethics Committee. The required sample size was estimated using G*Power v.3.1 (Faul et al., 2009), resulting in a minimum of 19 participants (effect size = 0.8, confidence level = 0.05, statistical power = 0.9, paired t -test). To ensure sufficient statistical power, 36 adult participants were recruited. Prior to participation, each individual received a full explanation of the experimental procedures and provided written informed consent. Participants’ compensation included a base payment and task earnings. Each participant received a base payment of 45 RMB, with an additional amount equivalent to 5% of the total earnings from the 90 trials. Among the 36 participants, one consistently accepted all offers, and EEG data from seven participants did not meet quality standards; thus, these data were excluded. The final analysis included 28 participants (21 males, 7 females) aged 20 to 25 years ( M = 23.52, SD = 1.43), all with normal or corrected vision. None of the participants had physical or mental illnesses, nor had they received prior exposure to the UG experiment. 2.3 EEG Recording and Preprocessing EEG signals were recorded using a 64-channel EEG cap based on the extended 10–10 international electrode system. During EEG acquisition, the AFz electrode served as the ground, with a vertex reference applied via the NeuroScan system. Data were sampled at 1,000 Hz, and all electrode impedances were maintained below 10 kΩ. EEG preprocessing involved six steps: 1) bandpass filtering (1–45Hz); 2) removing ocular and muscle artifacts using Independent Component Analysis (ICA) and the ADJUST algorithm (Mognon et al., 2011 ); 3) re-referencing the data to the average reference; 4) segmenting the EEG data based on the Decision screen (from − 400ms to 480ms relative to the Decision screen onset); 5) excluding data segments with amplitude exceeding ± 100uV; 6) constructing the EEG dataset by labeling each segment with trial sequence, proposed allocation scheme, and the responder’s decision. 2.4 EEG Data Grouping To validate the hypothesis, the data were divided into two conditions. Condition 1: previous rejection of an extremely unfair offers (9:1) and acceptance of a moderately unfair offers (7:3) in the current trial (IAAI group). Condition 2: previous acceptance of a fair splits (5:5) and acceptance of a moderately unfair offer (7:3) in the current trial ( AEAI group). EEG data segments were extracted for both conditions and analyzed accordingly. 2.5 EEG Analysis Methods 2.5.1 ERSP Calculation Event-related spectral perturbation (ERSP) analysis was performed to examine time-frequency differences between the two conditions. Wavelet transform was applied to each EEG signal, mapping it into the time-frequency domain. The time-frequency representation of each trial for a given electrode was stored in a matrix, where each element contained the average signal strength within a 1 Hz frequency band and a 5 ms time window. An electrode of each subject had two time-frequency matrices (one for IAAI and another for AEAI). The time-frequency representations of all trials within the same condition were averaged for each electrode, producing the time-frequency group average matrix. To avoid the influence of diffused frequency components on the pre-stimulus interval, the period from − 400 ms to -200 ms was used as the baseline. For each frequency row in the time-frequency matrix, the baseline mean intensity was computed. All values in that row were divided by the baseline mean, followed by a log transformation (base 10) and scaling by a factor of 10 to obtain ERSP values in decibels (dB). The final ERSP matrix for each subject’s IAAI and AEAI conditions at each electrode was derived. Only the 0–480 ms time window and 5–35 Hz frequency range were retained for further analysis. MATLAB scripts incorporating core functions from the EEGLAB toolbox (Delorme & Makeig, 2004 ) were used for these computations. 2.5.2 Cluster-Based Permutation Test To compare the ERSP differences between the IAAI and AEAI conditions across the 0-480ms and 5-35Hz time-frequency range at each electrode for 28 subjects, we faced the problem of multiple comparisons. The cluster-based permutation test (Maris & Oostenveld, 2007 ) is a statistical method specifically designed to address this issue, involving two main processes: probability distribution estimation and statistical inference. The probability distribution estimation is based on random permutation and adjacency clustering. The random permutation procedure divides the ERSP matrices of the 28 subjects at each electrode into two groups (IAAI and AEAI) randomly. A t-test is performed for each time-frequency location between the two groups. The significant time-frequency locations are classified as either showing a difference or not based on a significance level of 0.05. The adjacency clustering procedure clusters adjacent time-frequency locations that show differences, forming a significantly different time-frequency region. The total t-value for each of these clusters is calculated, and the largest total t-value is recorded as a sample from the difference region. The probability distribution is estimated by performing 1000 random permutations and adjacency clustering, and the histogram method is used to estimate the probability distribution of the sample. Once the probability distribution is estimated, the statistical inference process continues to identify the significant time-frequency regions where differences exist between the IAAI and AEAI conditions. This process consists of five steps :1) Perform a t-test for the ERSP values of the IAAI and AEAI groups at each time-frequency location for all 28 subjects and record the t-values and p-values. 2) Classify the time-frequency locations as showing a difference or not based on a significance level of 0.05. 3) Cluster adjacent time-frequency locations showing differences into significant regions. 4) Calculate the total t-value for each significant time-frequency region. 5) Use the estimated probability distribution to perform a hypothesis test on the total t-value of each significant region, using a significance level of 0.05. The time-frequency regions that pass the hypothesis test are considered the regions with significant differences between the two conditions. This procedure involved extensive calculations, which were carried out using MATLAB programs. The core algorithms for the cluster-based permutation test were implemented using FieldTrip functions (Oostenveld et al., 2011 ). 3 Results 3.1 Behavioral Data Under the condition of accepting the 5:5 offers (fair condition) in the previous round, participants exhibited mixed responses to the 7:3 offers (moderately unfair condition) in the current round, with an average acceptance rate of 0.72 ( SD = 0.24). Similarly, under the condition of rejecting the 9:1 offers (extremely unfair condition) in the previous round, participants also showed mixed responses to the 7:3 proposal, with an average acceptance rate of 0.68 ( SD = 0.29). A paired t -test comparing the two acceptance rates yielded a t-value of 0.85 and a p-value of 0.40, indicating no significant difference. In this study, the time elapsed between the appearance of a proposal on the screen and the participant's key press response was defined as reaction time. The average reaction time across all valid trials for each participant was calculated, with a minimum value of 512 ms. Given that participants require additional time to convert their decision into a key press, it is assumed that the decision-making process was completed before 480 ms. Therefore, EEG data analysis within the 0–480 ms window can provide insights into participants' cognitive decision-making processes. Furthermore, the average reaction times in the AEAI and IAAI conditions were computed for each participant. A paired t -test yielded a t-value of 0.13 and a p-value of 0.90, indicating no significant difference between the two conditions. 3.2 ERSP Differences Based on ERSP analysis, differences between the AEAI and IAAI conditions were observed at eight electrodes: CP2, CP4, PZ, P2, P4, P6, PO4, and POZ (see Fig. 2 ). The differences between the two conditions were most prominent in the 300–400 ms time window and the 7–25 Hz frequency range. Since EEG analysis often focuses on the Alpha (7–13 Hz) and Beta (13–25 Hz) frequency bands, further analysis concentrated on the 300–400 ms time window with the 7–13 Hz (referred to as the Alpha band) and 13–25 Hz (referred to as the Beta band) frequency ranges. For each electrode, the average ERSP values for each time-frequency location in the Alpha and Beta bands were calculated for each participant under both the AEAI and IAAI conditions. Paired t-tests were then performed on the Alpha and Beta band ERSP values for each electrode between the AEAI and IAAI conditions. The results revealed significant ERSP differences at the CP2, CP4, PZ, P2, P4, P6, PO4, and POZ electrodes (see Table 1 ). Table 1 ERSP Values and Differences Across Electrodes Under AEAI and IAAI Conditions Electrodes Alpha Band Beta Band AEAI M ± SD IAAI M ± SD p AEAI M ± SD IAAI M ± SD p CP2 -0.41 ± 3.03 -0.68 ± 3.45 0.652 -1.30 ± 2.32 -2.81 ± 2.26 0.002** CP4 0.04 ± 2.65 -1.18 ± 2.64 0.029* -1.40 ± 2.17 -3.24 ± 2.00 < 0.001*** Pz 0.51 ± 2.79 -0.55 ± 3.49 0.075+ -1.47 ± 2.80 -2.80 ± 2.58 0.018* P2 0.41 ± 2.59 -0.74 ± 3.65 0.035* -1.39 ± 2.46 -3.20 ± 2.71 < 0.001*** P4 -0.04 ± 3.32 -1.38 ± 3.20 0.015* -2.59 ± 2.82 -3.96 ± 2.44 0.003** P6 0.09 ± 3.64 -1.36 ± 3.10 0.022* -2.57 ± 2.72 -4.00 ± 2.26 0.002** POz 0.61 ± 3.41 -0.75 ± 3.60 0.048* -2.13 ± 2.26 -3.35 ± 2.42 0.006** PO4 0.44 ± 4.05 -1.08 ± 3.56 0.020* -2.79 ± 2.50 -4.50 ± 2.52 0.001** Note : Each row corresponds to an electrode where significant differences were found by paired t -tests. The 'AEAI' and 'IAAI' columns show the mean and standard deviation of ERSP values for all subjects, with units in dB. +p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001 3.2.1 Alpha Band In the AEAI condition, the average Alpha band ERSP values for all participants at each electrode were used to plot the AEAI topography. Similarly, the IAAI topography was plotted (see Fig. 3 A). The AEAI topography indicates that Alpha band ERSP values across electrodes are close to zero, suggesting the absence of prominent Alpha event-related desynchronization (ERD) or event-related synchronization (ERS). In contrast, the IAAI topography reveals strong negative ERSP values in the right parietal-occipital and central frontal regions, indicating prominent Alpha ERD under the IAAI condition. A paired t-test on the Alpha band ERSP values revealed significant differences at the P4 and PO4 electrodes, as indicated by the red and orange boxes in Fig. 3 A. At the P4 electrode (see Fig. 3 B), the mean dB value under the IAAI condition (-1.38 ± 3.20 dB) was significantly lower than under the AEAI condition (-0.04 ± 3.32 dB), with a t(27) = 2.60, p = 0.015, 95% CI = [0.28, 2.40]. At the PO4 electrode (see Fig. 4 C), the mean dB value under the IAAI condition (-1.08 ± 3.56 dB) was significantly lower than under the AEAI condition (0.44 ± 4.05 dB), with a t (27) = 2.46, p = 0.0204, 95% CI = [0.26, 2.79]. These results indicate that, compared to the AEAI condition, the IAAI condition exhibits stronger Alpha ERD at the P4 and PO4 electrodes. 3.2.2 Beta Band The same comparison method was used for the Beta band, and the results are shown in Fig. 4 . The AEAI topography in Fig. 4 A shows a blue color in the central frontal region, with the parietal-occipital regions on both sides also showing blue. This indicates some Beta ERD in these regions under the AEAI condition. The IAAI topography in Fig. 4 A shows a large area of deep blue in the central frontal, right central, and parietal-occipital regions, indicating a more prominent Beta ERD in these regions under the IAAI condition. Visually, both the AEAI and IAAI conditions show Beta ERD, but there is a larger and more widespread Beta ERD under the IAAI condition. Paired t-tests on the Beta band ERSP values revealed significant differences at the CP4 and P2 electrodes (see Table 1 ), with the red and orange boxes in Fig. 4 A indicating these two electrodes. Figure 4 B and 4 C visually demonstrate the ERSP differences at the CP4 and P2 electrodes. At both CP4 and P2 electrodes, the IAAI condition exhibited stronger Beta ERD compared to the AEAI condition. 4 Discussion 4.1 Alpha Band ERSP analysis revealed that the IAAI condition induced a stronger Alpha-ERD than the AEAI condition. Specifically, under the IAAI condition (rejecting extremely unfair offers in the preceding trial followed by accepting moderately unfair ones), the brain engages in monitoring fairness-gain conflicts across both preceding and current trials. In contrast, the AEAI condition (transitioning from accepting fair to moderately unfair offers) requires only a single conflict detection and resolution process during the current trial, thereby incurring lower cognitive control costs. In the UG, responders must balance inequity aversion against personal gain. Inequity aversion typically engages emotion- and conflict-control-related cognitive processes (Gao et al., 2018 ). To prioritize profit-driven decisions in the current trial, responders must suppress intrinsic fairness preferences and override strong social norms and emotional reactions (e.g., anger). This cognitive demand necessitates suppression in relevant cortical regions (Hunter, 2020 ; Zhao et al., 2014 ) and heightens attentional resource allocation (Yang et al., 2020 ). Alpha-ERD serves as a key indicator of cognitive control, reflecting heightened attention (Pfurtscheller & Da Silva, 1999), increased brain activation (Carp & Compton, 2009 ), and enhanced cognitive processing (Klimesch, 1999 ). The stronger Alpha-ERD observed in the IAAI condition suggests that more cognitive resources were engaged, and neural activation was higher, to overcome inequity aversion and facilitate conflict resolution. Spatially, the Alpha-ERD difference was primarily observed in the right temporo-parietal junction (rTPJ), with the most significant differences at the P4 and PO4 electrodes. The right temporoparietal junction (rTPJ) is a critical brain region implicated in inequity aversion. It mediates conflicts between gains-driven motivations and fairness norms (Obeso et al., 2018 ; Knoch et al. 2006 ) and plays a pivotal role in guiding human reciprocal behavior (Wu et al., 2023 ). Alpha-ERD is closely linked to cognitive inhibition (Knyazev, 2007 ) and is a sensitive marker of volitional control during cognitive tasks (Klimesch, 1999 ). The pronounced Alpha-ERD in the rTPJ region further suggests that, compared to encountering a fair offer in the previous round, accepting a moderately unfair offer after rejecting an extremely unfair one demands greater cognitive resources to inhibit inequity aversion. 4.2 Beta Band Similarly, ERSP analysis founded that IAAI induced a stronger Beta-ERD compared to AEAI. In this study, rejecting extremely unfair offers in prior trials reflects fairness preferences (inequity aversion), and processing such aversion involves regulating negative emotions. As evidenced by Nayak and Tsai ( 2022 ), Beta-ERD indexes neural mechanisms underlying both emotion regulation and strategic behavioral adjustments. In the IAAI condition where extremely unfair offers were rejected in the previous round but moderate - inequity offers were accepted in the current round, responders not only suppressed negative emotions towards unfairness but also changed their decision - making strategy. They used a loss - based strategy to resolve the conflict between fairness and gains in the previous round's unfair allocation, but switched to a gain - based strategy in the current round. In contrast, when fair offers were accepted in the previous round, no inequity aversion was triggered. The current - round acceptance of unfair offers, like the previous - round acceptance of fair ones, involved a gain - based conflict - resolution strategy. So, there were no obvious cognitive - state or strategy changes in this case. Beta oscillations (13–25 Hz) are implicated in control processing (Huster et al., 2013 ) and inhibitory functions (Alegre et al., 2004 ), with Beta-ERD marking ongoing or imminent shift in cognitive states (Gastaldon et al., 2020 ). The stronger Beta-ERD observed under the IAAI condition suggests that responders mobilize heightened cognitive resources during this transition, facilitating a strategic shift from fairness-driven rejection to profit-driven acceptance. Spatially, the Beta-ERD difference was primarily observed in the right parietal and right parieto-occipital regions, with the most significant differences at the CP4 and P2 electrodes. Previous research has shown that Beta activity in these regions was associated with negative emotion processing and cognition (Güntekin & Başar, 2010 ). In this study, the IAAI condition elicited inequity aversion accompanied by negative emotions(i.e, anger, jealous). The spatial distribution of Beta-ERD differences supports the hypothesis that cognitive strategy shifts occurred in the IAAI condition (Lundqvist et al., 2024 ), as indicated by its neurophysiological basis. 4.3 Limitations and Future Research Directions This study explored the neurophysiological responses during continuous decision-making in the UG task using ERSP analysis. The study found that accepting a medium-unfair offer under the condition of inhibiting inequity aversion consumed more cognitive resources than accepting it without inhibition. Despite these findings, there are still some limitations in the study. First, the sample size was relatively small, limiting the investigation of gender differences and group heterogeneity (Friedl et al. 2020 ). Participants were university students, a population that exhibits variability in inequity aversion. For example, younger individuals and those with higher education levels tend to show lower levels of inequity aversion (Bellemare et al., 2008 ). Future research should include a larger sample size to explore group differences and enhance the generalizability of the findings. Second, this study primarily examined EEG frequency-band activity, which has relatively low spatial resolution. Inequity aversion is a complex cognitive process involving both cortical and subcortical brain regions, such as the insula (Nitsch et al., 2022 ) and striatum (Li et al., 2022 ). Future studies could integrate fMRI techniques to provide a more comprehensive understanding of the neural mechanisms involved. Third, in real-world decision-making, the modulation of inequity aversion is influenced by various social factors, including social support (Wei et al., 2018 ), social status (Hu et al., 2016 ), and social comparison (Dvash et al., 2010 ; Szolnoki et al. 2013 ). Future research could incorporate experimental paradigms that consider these factors to further investigate their impact on decision-making. Declarations Funding . This study was supported by the Science and Technology Plan Project of Fujian Province, China (Grant No. 2023Y0009). Declaration of conflicts of interest. The authors declare no competing interests. Ethical statement . The experiment was conducted in accordance with the Declaration of Helsinki and was approved by the Psychology Research Ethics Committee of Fuzhou University (Approval No. FZU-PSY2023-0065) on October 8, 2023. Written informed consent was obtained from all participants prior to the study. The consent procedure was carried out between May 25, 2024, and September 19, 2024. Supplemental data. The data supporting this study are available in the Science Data Bank and can be accessed via application at the following link: https://psych.scidb.cn/detail?dataSetId=4a965078aa744d8faaee867a95ef4d25&version=V1&code=o00115. Author Contribution C.L. was responsible for conceptualization, methodology design, and original draft writing. Y.Z. handled data curation, visualization, and software implementation. X.G. contributed to visualization, investigation, and software development. X.J. provided critical revisions and contributed to review and editing of the manuscript. Z.H. supervised the project, contributed to methodology and software design, and participated in review, editing, and validation. All authors reviewed and approved the final manuscript. 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1","display":"","copyAsset":false,"role":"figure","size":3293889,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the Ultimatum Game Task\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6937568/v1/12a8c9d60f29a7e1367ae15d.png"},{"id":96454017,"identity":"96693ce1-9d7f-449d-81cd-4ee0a029f509","added_by":"auto","created_at":"2025-11-21 10:02:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3011176,"visible":true,"origin":"","legend":"\u003cp\u003eERSP and Differences Under AEAI and IAAI Conditions\u003c/p\u003e\n\u003cp\u003eNote: The horizontal axis of the ERSP images represents the time period from 0 to 480 ms, and the vertical axis represents the frequency range from 5 to 35 Hz. The color of each pixel represents the ERSP value at the corresponding time-frequency location, with the color scale in each row indicating the correspondence between the color and the ERSP value. The first and second columns show the average ERSP for all subjects at each electrode under the AEAI and IAAI conditions, respectively. The third column shows the significant time-frequency regions between AEAI and IAAI conditions revealed by the cluster-based permutation test. The deep red areas indicate significant differences, while the light green areas represent regions with no significant differences.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6937568/v1/c08552cb0121bcd923b66d74.png"},{"id":96377521,"identity":"0913016d-c5ea-4b38-ab45-28e374b2ec64","added_by":"auto","created_at":"2025-11-20 11:34:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1120415,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of AEAI and IAAI Conditions in the Alpha Frequency Band\u003c/p\u003e\n\u003cp\u003eNote: Subfigure A shows the brain topography of the mean ERSP values in the Alpha frequency band for AEAI and IAAI conditions. The color scale bar indicates the correspondence between color and ERSP values. In subfigure A, the red and orange boxes highlight P4 and PO4, which show the most significant differences in Alpha frequency band ERSP.\u003c/p\u003e\n\u003cp\u003eSubfigures B and C show histograms comparing the Alpha frequency band ERSP differences at P4 and PO4. The upper boundary of the histograms represents the mean ERSP value for each group, and the length of the error bars represents the standard deviation of the ERSP values.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6937568/v1/35d3222fdb93343182a1e73b.png"},{"id":96377524,"identity":"58b3c935-0e57-4be5-93de-540915aa3249","added_by":"auto","created_at":"2025-11-20 11:34:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1027273,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of AEAI and IAAI Conditions in the Beta Frequency Band\u003c/p\u003e\n\u003cp\u003eNote: Subfigure A shows the brain topography of the mean ERSP values in the Beta frequency band for AEAI and IAAI conditions. The color scale bar indicates the correspondence between color and ERSP values. The red and orange rectangles highlight CP4 and P2, which show the most significant differences in Beta frequency band ERSP. Subfigures B and C show histograms comparing the Beta frequency band ERSP differences at CP4 and P2. The upper boundary of the histograms represents the mean ERSP value for each group, and the length of the error bars represents the standard deviation of the ERSP values.\u003c/p\u003e\n\u003cp\u003e***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6937568/v1/59c0fb452002876bad999335.png"},{"id":98439181,"identity":"004914db-85e2-4a99-b42f-536c83f018fd","added_by":"auto","created_at":"2025-12-17 17:01:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8334245,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6937568/v1/888ce001-c401-4496-8bd7-82219e055aa0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inequity Aversion Makes Gains Harder: Evidence from Neural dynamics in the ultimatum game","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe ancient Chinese proverb, \"People are concerned not with scarcity but with inequality\", highlights humanity\u0026rsquo;s enduring focus on equity. Nevertheless, absolute equity in real-world resource distribution is rare, and individuals frequently encounter inequity. When faced with unfair allocations, humans do not behave as purely rational agents but instead weigh economic gains against fairness concerns (Sun et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On one hand, the rational economic man hypothesis posits that individuals maximize self-interest; thus, rejecting unfair offers (resulting in zero gain) contradicts this principle. On the other hand, individuals exhibit strong equity preferences, often sacrificing personal gains to oppose inequity\u0026mdash;a phenomenon termed inequity aversion (Fehr \u0026amp; Schmidt, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Disadvantageous inequity, where one receives less than others, evokes perceptions of inferiority and triggers aversion. Extreme disadvantageous inequity provokes decisive rejection, while slight inequity induces a trade-off between equity and gains, offering insight into the psychological mechanisms underlying such decisions. Although the neural basis of disadvantageous inequity aversion has been extensively studied (Gao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the cognitive and neural processes involved in transitioning from aversion to accepting inequity for gains remain poorly understood. Examining differences in cognitive load between decision-making pathways of \u0026lsquo;suppressing inequity aversion to obtain gains\u0026rsquo; versus \u0026lsquo;acquiring gains after accepting fair allocations\u0026rsquo; addresses whether inequity aversion heightens the difficulty of pursuing profits. By dissecting neural mechanism disparities between high-conflict strategies (suppressing inequity aversion and emotional responses) and low-conflict strategies (adaptive adjustments), this exploration can elucidate individual psychological adaptations to social inequality. The findings can empirically support decision optimization for fairness-sensitive individuals and provide scientific foundations for policymakers to mitigate decision conflicts and foster equitable governance.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Trade-offs Between Gains and Fairness in Moderately Unfair Contexts\u003c/h2\u003e\u003cp\u003eThe ultimatum game (UG) is a classic paradigm for studying inequity aversion (G\u0026uuml;th et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). In this game, a proposer offers a division of resources, and a responder decides to accept or reject it. If the responder accepts the proposer\u0026rsquo;s allocation, both receive the proposed amounts; if rejected, neither receives anything. According to rational economic theory, individuals would accept all of the offers, yet empirical results showed that responders often rejected unfair proposals. However, based on inequity aversion model's view, individuals compare their payoffs with others' in unfair allocations (Takanashi \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The more unfair the distribution proposal, the higher the responder's rejection probability (Bernhard et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Typically, responders reject extremely unfair proposals and accept fair ones. Thus, moderately unfair allocations provoke rich cognitive activity, making them ideal for investigating the psychological mechanisms of equity-gain trade-offs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Cognitive Control in Suppressing Inequity Aversion\u003c/h2\u003e\u003cp\u003eInequity aversion involves emotional and conflict-control processes (Gao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Soutschek \u0026amp; Schubert, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Unfair allocation can trigger individuals' negative emotions. For example, disadvantageous inequity may lead to angry, and advantageous inequity may cause guilt. These emotions are key factors influencing individuals' cooperation choices. Disadvantageous inequity evokes negative emotions like jealous, anger, and frustration, leading to rejection (Heffner \u0026amp; FeldmanHall, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; McAuliffe \u0026amp; Dunham, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Neuroimaging studies revealed that rejecting unfair proposals activated the prefrontal cortex (associated with emotion regulation) and anterior insula (involved in negative emotion processing) (Koenigs \u0026amp; Tranel, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sanfey et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In the study by van't Wout et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), it was found that when rejecting unfair proposals, people tended to exhibit stronger skin - conductance responses and became more emotionally aroused. In another study using rats as subjects, those in a disadvantaged inequity context displayed more anxiety-like behaviors and showed increased activity in the medial prefrontal and orbitofrontal cortex (Jeong \u0026amp; Noh, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, individuals\u0026rsquo; rejection of unfairness is accompanied by intense negative emotions.\u003c/p\u003e\u003cp\u003eWhen facing unfair allocation, individuals have to suppress negative emotional responses to unfairness to overcome inequity aversion, involving conflict regulation and higher cognitive control(De Neys et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Halali et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Tabibnia et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) found that accepting unfairness activates self-control circuits. During this process, the right ventrolateral prefrontal cortex (involved in emotion regulation) shows increased activity, while the anterior cingulate cortex (linked to negative emotions) shows decreased activity. Thus, accepting unfairness involves more cognitive control, including inhibiting negative emotions. Rejecting unfair proposals indicates inequity aversion, while accepting them requires suppressing inequity aversion and regulating negative emotions, involving cognitive control and conflict resolution.\u003c/p\u003e\u003cp\u003eCurrent researches on cognitive control predominantly focused on inhibitory processes in single-trial decisions. However, decision-making is inherently sequential, engaging more complex cognitive dynamics. In the Ultimatum Game (UG), individuals not only face trade-offs between fairness and gains within individual trials but also exhibit carryover effects from prior decisions (Dong et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Responses to unfair offers in earlier rounds systematically shaped subsequent choices (Soutschek \u0026amp; Schubert, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Confronting unfair allocations, decision-makers experience conflicts between fairness norms and economic gains. According to the conflict monitoring model, the ACC detects such conflicts and signals the dorsolateral prefrontal cortex (dlPFC) to recruit cognitive control resources\u0026mdash;such as suppressing anger or evaluating long-term benefits\u0026mdash;to adjust behavior (Botvinick et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In sequential UG paradigms, shifting from rejecting unfairness (Inequity Aversion, IA) to accepting it (Accepting Inequity, AI) imposes higher cognitive control costs than transitioning from accepting fairness (Accepting Equity, AE) to AI. The IA-to-AI shift requires overriding strong fairness preferences and emotional responses within a loss context (previous rejection), whereas the AE-to-AI transition involves adaptive strategy updates in a gain context (prior acceptance), demanding less suppression of intrinsic fairness norms. This suggests that accepting unfairness after prior rejection necessitates greater mobilization of cognitive resources compared to post-fairness adaptations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3 The Neural Basis of Cognitive Control in Inequity Aversion\u003c/h2\u003e\u003cp\u003eTrade - offs between gains and fairness involve dynamic allocation of cognitive resources, such as shifting from emotion suppression to value calculation. Behavioral studies only capture decision - making outcomes, but EEG time - frequency analysis can track the timing of this psychological process. Event-related spectral perturbations (ERSP) is a method for time-frequency analysis of EEG, capable of examining changes in brainwave power within specific frequency bands. This analysis reveals event-related desynchronization (ERD) and event-related synchronization (ERS) across various frequency bands, offering insights into different cognitive activities (Tiesinga et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Among these, the Alpha and Beta bands play a key role in cognitive control. Changes in the Alpha band are associated with top-down cognitive control and its degree (Babiloni et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), with Alpha-ERD reflecting an increase in brain activation (Compton et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and cognitive activity (Klimesch et al, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Beta-band ERS represents the maintenance of the current cognitive state, while Beta-band ERD signifies a change or impending change in cognitive state (Gastaldon et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, Beta-band changes are closely linked to inhibitory functions (Beltr\u0026aacute;n et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Muralidharan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Compared to traditional metrics such as acceptance rate and response time, ERSP offers another perspective for exploring the cognitive control mechanisms during decision-making.\u003c/p\u003e\u003cp\u003eSpatially, the Right Temporoparietal Junction (rTPJ) is a key brain region related to inequity aversion. Obeso et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggested that rTPJ, as a neural hub, modulated the conflict between equity and rewards. Previous studies have shown that transcranial direct current stimulation (tDCS) targeting the rTPJ increased the likelihood of participants making fair decisions (Wu et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). rTPJ is central to the mentalization network and plays an important role in human reciprocal behavior.\u003c/p\u003e\u003cp\u003eThis study aimed to investigate the neurocognitive costs of strategic decision shifts when individuals trade off fairness norms against economic gains. Using the the UG paradigm, two sequential conditions were contrasted: (1) accepting moderately unfair offers (7:3) after rejecting extremely unfair allocations (9:1) (\u003cem\u003eInequity Aversion \u0026rarr; Accept Inequity\u003c/em\u003e, IAAI), and (2) accepting the same moderate unfairness following prior acceptance of fair splits (5:5) (\u003cem\u003eAccept Equity \u0026rarr; Accept Inequity\u003c/em\u003e, AEAI). Condition 1 evoked strong negative emotions, requiring responders to suppress fairness-driven aversion and reconfigure conflict-resolution strategies when accepting subsequent moderate inequity. In contrast, condition 2 involved no prior suppression of fairness norms, as responders encountered fairness-gain conflicts for the first time, enabling adaptive adjustments without strategic overrides. To test this hypothesis, event-related spectral perturbation (ERSP) was employed to analyze EEG oscillations, with a focus on alpha (7\u0026ndash;13 Hz) and beta (13\u0026ndash;25 Hz) event-related desynchronization (ERD) (Xu \u0026amp; Shi \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Experimental Design\u003c/h2\u003e\u003cp\u003eThe Ultimatum Game (UG) served as the experimental paradigm, employing a within-subject design with a single factor: the allocation scheme (extremely unfair, moderately unfair, and fair). In this experiment, the proposer always allocated 10 Chinese yuan to the responder. The three allocation schemes were as follows: 5:5 (fair), 7:3 (moderately unfair), and 9:1 (extremely unfair). The proposer randomly selected one of the three allocation schemes, ensuring each scheme had an equal probability of being chosen. This design prevented the responder from predicting the proposal in advance, ensuring that the decision-making process occurred only after the proposal was presented. To maintain randomness and equal probability, the proposer\u0026rsquo;s behavior was simulated by a computer, though the responder was informed that the proposal originated from proposers.\u003c/p\u003e\u003cp\u003eEach experiment consisted of 90 valid trials. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, each trial comprised five screens: Cue, Evaluation, Wait, Decision, and Break. During the Decision screen, the responder made an accept or reject decision by pressing a button. Regardless of when the button was pressed, the Decision screen remained visible for 1480 ms. If no response was recorded during this period, the trial was considered invalid. The Break screen was a black screen displayed for 1200 ms, providing a brief interval before the next trial.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Participants\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003cp\u003efor this experiment was obtained from the institution\u0026rsquo;s Psychology Research Ethics Committee. The required sample size was estimated using G*Power v.3.1 (Faul et al., 2009), resulting in a minimum of 19 participants (effect size\u0026thinsp;=\u0026thinsp;0.8, confidence level\u0026thinsp;=\u0026thinsp;0.05, statistical power\u0026thinsp;=\u0026thinsp;0.9, paired \u003cem\u003et\u003c/em\u003e-test). To ensure sufficient statistical power, 36 adult participants were recruited. Prior to participation, each individual received a full explanation of the experimental procedures and provided written informed consent. Participants\u0026rsquo; compensation included a base payment and task earnings. Each participant received a base payment of 45 RMB, with an additional amount equivalent to 5% of the total earnings from the 90 trials.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eAmong the 36 participants, one consistently accepted all offers, and EEG data from seven participants did not meet quality standards; thus, these data were excluded. The final analysis included 28 participants (21 males, 7 females) aged 20 to 25 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23.52, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.43), all with normal or corrected vision. None of the participants had physical or mental illnesses, nor had they received prior exposure to the UG experiment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 EEG Recording and Preprocessing\u003c/h2\u003e\u003cp\u003eEEG signals were recorded using a 64-channel EEG cap based on the extended 10\u0026ndash;10 international electrode system. During EEG acquisition, the AFz electrode served as the ground, with a vertex reference applied via the NeuroScan system. Data were sampled at 1,000 Hz, and all electrode impedances were maintained below 10 kΩ.\u003c/p\u003e\u003cp\u003eEEG preprocessing involved six steps: 1) bandpass filtering (1\u0026ndash;45Hz); 2) removing ocular and muscle artifacts using Independent Component Analysis (ICA) and the ADJUST algorithm (Mognon et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e); 3) re-referencing the data to the average reference; 4) segmenting the EEG data based on the Decision screen (from \u0026minus;\u0026thinsp;400ms to 480ms relative to the Decision screen onset); 5) excluding data segments with amplitude exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;100uV; 6) constructing the EEG dataset by labeling each segment with trial sequence, proposed allocation scheme, and the responder\u0026rsquo;s decision.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4 EEG Data Grouping\u003c/h2\u003e\u003cp\u003eTo validate the hypothesis, the data were divided into two conditions. Condition 1: previous rejection of an extremely unfair offers (9:1) and acceptance of a moderately unfair offers (7:3) in the current trial (IAAI group). Condition 2: previous acceptance of a fair splits (5:5) and acceptance of a moderately unfair offer (7:3) in the current trial ( AEAI group). EEG data segments were extracted for both conditions and analyzed accordingly.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5 EEG Analysis Methods\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.5.1 ERSP Calculation\u003c/h2\u003e\u003cp\u003eEvent-related spectral perturbation (ERSP) analysis was performed to examine time-frequency differences between the two conditions. Wavelet transform was applied to each EEG signal, mapping it into the time-frequency domain. The time-frequency representation of each trial for a given electrode was stored in a matrix, where each element contained the average signal strength within a 1 Hz frequency band and a 5 ms time window. An electrode of each subject had two time-frequency matrices (one for IAAI and another for AEAI). The time-frequency representations of all trials within the same condition were averaged for each electrode, producing the time-frequency group average matrix.\u003c/p\u003e\u003cp\u003eTo avoid the influence of diffused frequency components on the pre-stimulus interval, the period from \u0026minus;\u0026thinsp;400 ms to -200 ms was used as the baseline. For each frequency row in the time-frequency matrix, the baseline mean intensity was computed. All values in that row were divided by the baseline mean, followed by a log transformation (base 10) and scaling by a factor of 10 to obtain ERSP values in decibels (dB). The final ERSP matrix for each subject\u0026rsquo;s IAAI and AEAI conditions at each electrode was derived. Only the 0\u0026ndash;480 ms time window and 5\u0026ndash;35 Hz frequency range were retained for further analysis. MATLAB scripts incorporating core functions from the EEGLAB toolbox (Delorme \u0026amp; Makeig, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) were used for these computations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.5.2 Cluster-Based Permutation Test\u003c/h2\u003e\u003cp\u003eTo compare the ERSP differences between the IAAI and AEAI conditions across the 0-480ms and 5-35Hz time-frequency range at each electrode for 28 subjects, we faced the problem of multiple comparisons. The cluster-based permutation test (Maris \u0026amp; Oostenveld, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) is a statistical method specifically designed to address this issue, involving two main processes: probability distribution estimation and statistical inference.\u003c/p\u003e\u003cp\u003eThe probability distribution estimation is based on random permutation and adjacency clustering. The random permutation procedure divides the ERSP matrices of the 28 subjects at each electrode into two groups (IAAI and AEAI) randomly. A t-test is performed for each time-frequency location between the two groups. The significant time-frequency locations are classified as either showing a difference or not based on a significance level of 0.05. The adjacency clustering procedure clusters adjacent time-frequency locations that show differences, forming a significantly different time-frequency region. The total t-value for each of these clusters is calculated, and the largest total t-value is recorded as a sample from the difference region. The probability distribution is estimated by performing 1000 random permutations and adjacency clustering, and the histogram method is used to estimate the probability distribution of the sample.\u003c/p\u003e\u003cp\u003eOnce the probability distribution is estimated, the statistical inference process continues to identify the significant time-frequency regions where differences exist between the IAAI and AEAI conditions. This process consists of five steps :1) Perform a t-test for the ERSP values of the IAAI and AEAI groups at each time-frequency location for all 28 subjects and record the t-values and p-values. 2) Classify the time-frequency locations as showing a difference or not based on a significance level of 0.05. 3) Cluster adjacent time-frequency locations showing differences into significant regions. 4) Calculate the total t-value for each significant time-frequency region. 5) Use the estimated probability distribution to perform a hypothesis test on the total t-value of each significant region, using a significance level of 0.05. The time-frequency regions that pass the hypothesis test are considered the regions with significant differences between the two conditions. This procedure involved extensive calculations, which were carried out using MATLAB programs. The core algorithms for the cluster-based permutation test were implemented using FieldTrip functions (Oostenveld et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Behavioral Data\u003c/h2\u003e\u003cp\u003eUnder the condition of accepting the 5:5 offers (fair condition) in the previous round, participants exhibited mixed responses to the 7:3 offers (moderately unfair condition) in the current round, with an average acceptance rate of 0.72 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24). Similarly, under the condition of rejecting the 9:1 offers (extremely unfair condition) in the previous round, participants also showed mixed responses to the 7:3 proposal, with an average acceptance rate of 0.68 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29). A paired \u003cem\u003et\u003c/em\u003e-test comparing the two acceptance rates yielded a t-value of 0.85 and a p-value of 0.40, indicating no significant difference.\u003c/p\u003e\u003cp\u003eIn this study, the time elapsed between the appearance of a proposal on the screen and the participant's key press response was defined as reaction time. The average reaction time across all valid trials for each participant was calculated, with a minimum value of 512 ms. Given that participants require additional time to convert their decision into a key press, it is assumed that the decision-making process was completed before 480 ms. Therefore, EEG data analysis within the 0\u0026ndash;480 ms window can provide insights into participants' cognitive decision-making processes. Furthermore, the average reaction times in the AEAI and IAAI conditions were computed for each participant. A paired \u003cem\u003et\u003c/em\u003e-test yielded a t-value of 0.13 and a p-value of 0.90, indicating no significant difference between the two conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2 ERSP Differences\u003c/h2\u003e\u003cp\u003eBased on ERSP analysis, differences between the AEAI and IAAI conditions were observed at eight electrodes: CP2, CP4, PZ, P2, P4, P6, PO4, and POZ (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The differences between the two conditions were most prominent in the 300\u0026ndash;400 ms time window and the 7\u0026ndash;25 Hz frequency range. Since EEG analysis often focuses on the Alpha (7\u0026ndash;13 Hz) and Beta (13\u0026ndash;25 Hz) frequency bands, further analysis concentrated on the 300\u0026ndash;400 ms time window with the 7\u0026ndash;13 Hz (referred to as the Alpha band) and 13\u0026ndash;25 Hz (referred to as the Beta band) frequency ranges.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor each electrode, the average ERSP values for each time-frequency location in the Alpha and Beta bands were calculated for each participant under both the AEAI and IAAI conditions. Paired t-tests were then performed on the Alpha and Beta band ERSP values for each electrode between the AEAI and IAAI conditions. The results revealed significant ERSP differences at the CP2, CP4, PZ, P2, P4, P6, PO4, and POZ electrodes (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eERSP Values and Differences Across Electrodes Under AEAI and IAAI Conditions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eElectrodes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eAlpha Band\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eBeta Band\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAEAI\u003c/p\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIAAI\u003c/p\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAEAI\u003c/p\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIAAI\u003c/p\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.029*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.075+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.018*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.035*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.022*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePOz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;3.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.048*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePO4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.020*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Each row corresponds to an electrode where significant differences were found by paired \u003cem\u003et\u003c/em\u003e-tests. The 'AEAI' and 'IAAI' columns show the mean and standard deviation of ERSP values for all subjects, with units in dB.\u003c/p\u003e\u003cp\u003e+p\u0026thinsp;\u0026lt;\u0026thinsp;0.1; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Alpha Band\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the AEAI condition, the average Alpha band ERSP values for all participants at each electrode were used to plot the AEAI topography. Similarly, the IAAI topography was plotted (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The AEAI topography indicates that Alpha band ERSP values across electrodes are close to zero, suggesting the absence of prominent Alpha event-related desynchronization (ERD) or event-related synchronization (ERS). In contrast, the IAAI topography reveals strong negative ERSP values in the right parietal-occipital and central frontal regions, indicating prominent Alpha ERD under the IAAI condition.\u003c/p\u003e\u003cp\u003eA paired t-test on the Alpha band ERSP values revealed significant differences at the P4 and PO4 electrodes, as indicated by the red and orange boxes in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. At the P4 electrode (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), the mean dB value under the IAAI condition (-1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20 dB) was significantly lower than under the AEAI condition (-0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32 dB), with a t(27)\u0026thinsp;=\u0026thinsp;2.60, p\u0026thinsp;=\u0026thinsp;0.015, 95% CI = [0.28, 2.40]. At the PO4 electrode (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), the mean dB value under the IAAI condition (-1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56 dB) was significantly lower than under the AEAI condition (0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05 dB), with a \u003cem\u003et\u003c/em\u003e(27)\u0026thinsp;=\u0026thinsp;2.46, p\u0026thinsp;=\u0026thinsp;0.0204, 95% CI = [0.26, 2.79]. These results indicate that, compared to the AEAI condition, the IAAI condition exhibits stronger Alpha ERD at the P4 and PO4 electrodes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Beta Band\u003c/h2\u003e\u003cp\u003eThe same comparison method was used for the Beta band, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The AEAI topography in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA shows a blue color in the central frontal region, with the parietal-occipital regions on both sides also showing blue. This indicates some Beta ERD in these regions under the AEAI condition. The IAAI topography in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA shows a large area of deep blue in the central frontal, right central, and parietal-occipital regions, indicating a more prominent Beta ERD in these regions under the IAAI condition. Visually, both the AEAI and IAAI conditions show Beta ERD, but there is a larger and more widespread Beta ERD under the IAAI condition.\u003c/p\u003e\u003cp\u003ePaired t-tests on the Beta band ERSP values revealed significant differences at the CP4 and P2 electrodes (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with the red and orange boxes in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA indicating these two electrodes. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC visually demonstrate the ERSP differences at the CP4 and P2 electrodes. At both CP4 and P2 electrodes, the IAAI condition exhibited stronger Beta ERD compared to the AEAI condition.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Alpha Band\u003c/h2\u003e\u003cp\u003eERSP analysis revealed that the IAAI condition induced a stronger Alpha-ERD than the AEAI condition. Specifically, under the IAAI condition (rejecting extremely unfair offers in the preceding trial followed by accepting moderately unfair ones), the brain engages in monitoring fairness-gain conflicts across both preceding and current trials. In contrast, the AEAI condition (transitioning from accepting fair to moderately unfair offers) requires only a single conflict detection and resolution process during the current trial, thereby incurring lower cognitive control costs. In the UG, responders must balance inequity aversion against personal gain. Inequity aversion typically engages emotion- and conflict-control-related cognitive processes (Gao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To prioritize profit-driven decisions in the current trial, responders must suppress intrinsic fairness preferences and override strong social norms and emotional reactions (e.g., anger). This cognitive demand necessitates suppression in relevant cortical regions (Hunter, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and heightens attentional resource allocation (Yang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Alpha-ERD serves as a key indicator of cognitive control, reflecting heightened attention (Pfurtscheller \u0026amp; Da Silva, 1999), increased brain activation (Carp \u0026amp; Compton, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and enhanced cognitive processing (Klimesch, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The stronger Alpha-ERD observed in the IAAI condition suggests that more cognitive resources were engaged, and neural activation was higher, to overcome inequity aversion and facilitate conflict resolution.\u003c/p\u003e\u003cp\u003eSpatially, the Alpha-ERD difference was primarily observed in the right temporo-parietal junction (rTPJ), with the most significant differences at the P4 and PO4 electrodes. The right temporoparietal junction (rTPJ) is a critical brain region implicated in inequity aversion. It mediates conflicts between gains-driven motivations and fairness norms (Obeso et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Knoch et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and plays a pivotal role in guiding human reciprocal behavior (Wu et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Alpha-ERD is closely linked to cognitive inhibition (Knyazev, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and is a sensitive marker of volitional control during cognitive tasks (Klimesch, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The pronounced Alpha-ERD in the rTPJ region further suggests that, compared to encountering a fair offer in the previous round, accepting a moderately unfair offer after rejecting an extremely unfair one demands greater cognitive resources to inhibit inequity aversion.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Beta Band\u003c/h2\u003e\u003cp\u003eSimilarly, ERSP analysis founded that IAAI induced a stronger Beta-ERD compared to AEAI. In this study, rejecting extremely unfair offers in prior trials reflects fairness preferences (inequity aversion), and processing such aversion involves regulating negative emotions. As evidenced by Nayak and Tsai (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Beta-ERD indexes neural mechanisms underlying both emotion regulation and strategic behavioral adjustments. In the IAAI condition where extremely unfair offers were rejected in the previous round but moderate - inequity offers were accepted in the current round, responders not only suppressed negative emotions towards unfairness but also changed their decision - making strategy. They used a loss - based strategy to resolve the conflict between fairness and gains in the previous round's unfair allocation, but switched to a gain - based strategy in the current round. In contrast, when fair offers were accepted in the previous round, no inequity aversion was triggered. The current - round acceptance of unfair offers, like the previous - round acceptance of fair ones, involved a gain - based conflict - resolution strategy. So, there were no obvious cognitive - state or strategy changes in this case. Beta oscillations (13\u0026ndash;25 Hz) are implicated in control processing (Huster et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and inhibitory functions (Alegre et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), with Beta-ERD marking ongoing or imminent shift in cognitive states (Gastaldon et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The stronger Beta-ERD observed under the IAAI condition suggests that responders mobilize heightened cognitive resources during this transition, facilitating a strategic shift from fairness-driven rejection to profit-driven acceptance.\u003c/p\u003e\u003cp\u003eSpatially, the Beta-ERD difference was primarily observed in the right parietal and right parieto-occipital regions, with the most significant differences at the CP4 and P2 electrodes. Previous research has shown that Beta activity in these regions was associated with negative emotion processing and cognition (G\u0026uuml;ntekin \u0026amp; Başar, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In this study, the IAAI condition elicited inequity aversion accompanied by negative emotions(i.e, anger, jealous). The spatial distribution of Beta-ERD differences supports the hypothesis that cognitive strategy shifts occurred in the IAAI condition (Lundqvist et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), as indicated by its neurophysiological basis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Limitations and Future Research Directions\u003c/h2\u003e\u003cp\u003eThis study explored the neurophysiological responses during continuous decision-making in the UG task using ERSP analysis. The study found that accepting a medium-unfair offer under the condition of inhibiting inequity aversion consumed more cognitive resources than accepting it without inhibition. Despite these findings, there are still some limitations in the study.\u003c/p\u003e\u003cp\u003eFirst, the sample size was relatively small, limiting the investigation of gender differences and group heterogeneity (Friedl et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Participants were university students, a population that exhibits variability in inequity aversion. For example, younger individuals and those with higher education levels tend to show lower levels of inequity aversion (Bellemare et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Future research should include a larger sample size to explore group differences and enhance the generalizability of the findings.\u003c/p\u003e\u003cp\u003eSecond, this study primarily examined EEG frequency-band activity, which has relatively low spatial resolution. Inequity aversion is a complex cognitive process involving both cortical and subcortical brain regions, such as the insula (Nitsch et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and striatum (Li et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Future studies could integrate fMRI techniques to provide a more comprehensive understanding of the neural mechanisms involved.\u003c/p\u003e\u003cp\u003eThird, in real-world decision-making, the modulation of inequity aversion is influenced by various social factors, including social support (Wei et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), social status (Hu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and social comparison (Dvash et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Szolnoki et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Future research could incorporate experimental paradigms that consider these factors to further investigate their impact on decision-making.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e. \u003c/em\u003e\u003c/strong\u003eThis study was supported by the Science and Technology Plan Project of Fujian Province, China (Grant No. 2023Y0009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeclaration of conflicts of interest. \u003c/em\u003e\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical statement\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e. \u003c/em\u003e\u003c/strong\u003eThe experiment was conducted in accordance with the Declaration of Helsinki and was approved by the Psychology Research Ethics Committee of Fuzhou University (Approval No. FZU-PSY2023-0065) on October 8, 2023. Written informed consent was obtained from all participants prior to the study. The consent procedure was carried out between May 25, 2024, and September 19, 2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSupplemental data. \u003c/em\u003e\u003c/strong\u003eThe data supporting this study are available in the Science Data Bank and can be accessed via application at the following link:\u003c/p\u003e\n\u003cp\u003ehttps://psych.scidb.cn/detail?dataSetId=4a965078aa744d8faaee867a95ef4d25\u0026amp;version=V1\u0026amp;code=o00115.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.L. was responsible for conceptualization, methodology design, and original draft writing. Y.Z. handled data curation, visualization, and software implementation. X.G. contributed to visualization, investigation, and software development. X.J. provided critical revisions and contributed to review and editing of the manuscript. Z.H. supervised the project, contributed to methodology and software design, and participated in review, editing, and validation. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting this study are available in the Science Data Bank and can be accessed via application at the following link:https://psych.scidb.cn/detail?dataSetId=4a965078aa744d8faaee867a95ef4d25\u0026amp;amp;version=V1\u0026amp;amp;code=o00115.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlegre M, Gurtubay IG, Labarga A, Iriarte J, Valencia M, Artieda J (2004) Frontal and central oscillatory changes related to different aspects of the motor process: A study in go/no-go paradigms. 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Neuroscience 262:92\u0026ndash;106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroscience.2013.12.052\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroscience.2013.12.052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng H, Lu X, Huang D (2018) tDCS Over DLPFC Leads to Less Utilitarian Response in Moral-Personal Judgment. \u003cem\u003eFrontiers in Neuroscience\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnins.2018.00193\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2018.00193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Inequity aversion, Ultimatum game, EEG time-frequency analysis","lastPublishedDoi":"10.21203/rs.3.rs-6937568/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6937568/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo compare cognitive costs between shifting from \u0026ldquo;rejecting unfairness to accepting unfairness\u0026rdquo; versus \u0026ldquo;accepting fairness to accepting unfairness\u0026rdquo;, an Ultimatum Game with EEG time-frequency analysis (ERSP) was conducted. Participants either accepted a moderately unfair offer (7:3) after rejecting an extreme unfair one (9:1), requiring suppression of inequity aversion, or accepted the same after a fair offer (5:5), without prior rejection. Behaviorally, acceptance rates did not differ. Neurally, the \u0026ldquo;reject-to-accept\u0026rdquo; condition exhibited stronger alpha (7\u0026ndash;13 Hz) and beta (13\u0026ndash;25 Hz) event-related desynchronization (ERD) in the right temporoparietal junction (rTPJ), indicating higher inhibitory control costs. Findings elucidate adaptive mechanisms balancing emotional aversion and strategic flexibility in social inequality contexts, informing interventions for fairness-sensitive individuals and equitable policy design.\u003c/p\u003e","manuscriptTitle":"Inequity Aversion Makes Gains Harder: Evidence from Neural dynamics in the ultimatum game","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 11:34:42","doi":"10.21203/rs.3.rs-6937568/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":"352aed2c-d8d8-4026-9e4b-db4c3711bd83","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58120665,"name":"Social science/Business and management"},{"id":58120666,"name":"Social science/Economics"},{"id":58120667,"name":"Social science/Politics and international relations"},{"id":58120668,"name":"Social science/Psychology"},{"id":58120669,"name":"Social science/Science technology and society"},{"id":58120670,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2025-12-16T17:08:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 11:34:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6937568","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6937568","identity":"rs-6937568","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}