Functional Connectivity Basis and Underlying Cognitive Mechanisms for Gender Differences in Guilt Aversion

Trust game

Participants performed a trust game adapted from the task originally used by Charness and Dufwenberg (2006). In this task, two subjects are paired as players A and B (Fig. 1A). First, player A must choose between In and Out options and simultaneously reveal their belief about τA (from 0% to 100%), the probability that player B will choose Cooperate. In other words, τA is player A’s level of trust in player B. If player A chooses Out, players A and B receive payments zA and zB , respectively. If player A chooses In, then knowing player A’s belief probability, player B must choose Cooperate or Defect. If player B chooses Defect, player A receives yA and player B receives yB ; if player B chooses Cooperate, then the two players receive xA and xB , respectively. In the example shown in Figure 1B, the belief probability of player A was 80%. If player B defected, player A and player B would receive 220 and 910 yen, respectively; if player B cooperated, they would receive 780 and 650 yen, respectively.

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Figure 1.

Task design. A, Design of the trust game. First, player A chooses In or Out, which reveals a belief probability of the likeliness that player B will choose Cooperate. If player A chooses Out (i.e., does not trust player B), player A and B receive zA and zB , respectively. If player A chooses In (i.e., trusts player B), then with the knowledge of player A’s belief probability, player B decides whether to Cooperate or Defect. If player B chooses Defect, players A and B receive yA and yB , respectively; if Cooperate, players A and B receive xA and xB , respectively. The actual assignment of x, y, z and τA for the 45 trials is shown in Extended Data Figure 1-1. B, An outline and example of experimental trials. After the green fixation period (2–5 s; cue phase), a task condition is presented for 5 s (choice phase), and participants are asked to press the Cooperate or Defect button (blue and red, respectively). Then, a yellow fixation cross is shown for 6–15 s (rest phase). C, An illustration of the complete experimental paradigm. For both the fMRI and online studies, in the first experiment, participants (as player A) chose In or Out and reveal their belief probability that player B would choose Cooperate. In the second experiment, participants (as player B) chose to Cooperate or Defect. Participants make their decisions while being scanned in the fMRI experiment. Instructions for the first and second experiments are shown in Extended Data Figure 1-2.

There are two important conditions regarding the payments in Figure 1A (see also the definitions of guilt and inequity aversion below): if (1) yA<zA<xA , then player A signals trust (cooperation) to player B when player A chooses In; if (2) zB<xB<yB , then player B feels guilt on disappointing player A relative to player A’s belief in what player A will receive. This trust game was originally designed and used in Nihonsugi et al. (2015).

Guilt aversion and inequity aversion

Guilt aversion (Charness and Dufwenberg, 2006; Battigalli and Dufwenberg, 2007, 2009) assumes that an individual dislikes not meeting another’s belief. Note that guilt sensitivity elicited in the trust game by guilt aversion theory is fundamentally related to the Test of Self-Conscious Affect-3 (TOSCA-3) and the Guilt and Shame Proneness Scale (GASP), which is a common measure of guilt sensitivity in psychology, but is unrelated to shame (Bracht and Regner, 2013; Bellemare et al., 2019).

This model includes social pressure on player B if the profile (In, Defect) is played (Fig. 1A). Player B is assumed to believe that if player A chooses In, then player A believes that he will get a return of τA⋅xA+(1−τA)⋅yA , because the setting of player A’s payoff is yA < zA < xA . The difference,{τA⋅xA+(1−τA)⋅yA}−yA=τA(xA−yA) , which is non-negative in our settings, can measure how much player B believes that he/she has disappointed player A relative to player A’s belief had player B chosen Defect. In other words, the difference τA(xA−yA) is the amount of guilt that player B experiences. Let us assume that γB is the parameter that measures player B’s sensitivity to guilt. A player is guilt-averse and will Cooperate if yB−γB⋅τA(xA−yA) < xB . In the example trial in Figure 1B, if 910−γB⋅0.8⋅(780−220) < 650 , player B will choose Cooperate. Since γ_B does not directly measure guilt experiences or emotional traits, we can only infer that “γ_B expresses sensitivity of guilt.” As mentioned in Results, however, our interpretation that γ_B expresses a guilty experience is consistent with the results of the postexperiment questionnaire.

By contrast, inequity aversion assumes a social preference for equitable payoffs (Fehr and Schmidt, 1999). An individual is inequity-averse if, in addition to their monetary self-interest, their utility decreases when the allocation of monetary payoffs is different. If an inequity-averse player suffers from inequity, they will choose an option that results in a smaller difference between their own and the other’s monetary payoffs. Notably, the advantageous-inequity (receiving a larger reward than others) in Fehr and Schmidt’s inequity-aversion model is also referred to as “guilt.” However, it is important to note that this outcome-based “guilt” and the intension-based “guilt” we treat in guilt-aversion are completely different.

As mentioned below, based on the results of the model selection using both the cross-validation analysis (predictive likelihood) and the Bayesian information criterion (BIC; Fig. 2B,C; see also below, Model validation and comparison), the absolute difference for inequity was found superior than the standard inequity aversion model, which splits the inequity into positive and negative terms, in the present study.

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Figure 2.

Behavioral results. A, In the fMRI study (n = 26 men, 26 women), the β value for guilt was higher in men than in woman (p = 0.046, t test), whereas the β value for inequity was higher in women than in men (p = 0.039, t test). B, We validated and compared the performance of 10 models using the repeated 3-fold cross-validations and found that the model containing three predictors (Reward, Guilt, and Inequity) was best for both fMRI and online studies. Rw: Reward; Gu: Guilt; Iq: Inequity; Ip: Inequity-positive; In: Inequity-negative. C, BIC also selected the same model (i.e., RwGuIq in B), with the second best being the Fehr and Schmidt type model (i.e., RwGuIpIn in B). For the selected model, a majority of participants exhibited the smallest BIC value for both the fMRI and online experiments. D, β(Guilt) had a significantly or marginally positive correlation with questions a, b, and c.

We integrated guilt aversion and inequity aversion into a utility function (uB ) for player B as follows: uB ={xB−αB|xA−xB|if the profile(In,Cooperate)yB−γB·τA·(xA−yA)−αB|yA−yB|if the profile(In,Defect), where αB is a constant that measures player B’s sensitivity to inequity. A narrowly self-interested agent is given the special case γB=αB=0 . In our game, players choose between binary actions that yield two different monetary payoff allocations, X=(xA,xB) and Y=(yA,yB) . The utilities of these allocations are given by the formula above, yielding uB(X) and uB(Y) .

Statistical analysis of behavioral data

We estimated three separate components, monetary self-interest, guilt, and inequity, for each participant based on the logistic model of stochastic choice. The probability that player B chooses Cooperate can be expressed as PB,Cooperate=1/1+e−{uB(X)−uB(Y)} . Although our model does not include an inverse temperature parameter explicitly, this does not imply the model does not consider decision noise. In fact, our model implicitly assumed the inverse temperature parameter to be 1. Such an implementation of the softmax function with the inverse temperature parameter = 1 is often seen in the behavioral analysis of the economic decision-making (Boorman et al., 2009; Cai, and Padoa-Schioppa, 2014; Suzuki et al., 2015) because the inverse temperature is relatively difficult to estimate. Based on this logistic model, we used a logistic regression as follows: logit(PB,Cooperate)=β0 + β1Rewardt + β2Guiltt   + β3Inequityt, where Rewardt is the size of the reward and calculated as xB−yB at time t, Guiltt is the size of guilt and calculated as −{0−τA·(xA−yA) }, and Inequityt is the size of inequity and calculated as −(|xA−xB|−|yA−yB|) . For convenience,β1 , β2 , and β3 are denoted as β(Reward) , β(Guilt) , and β(Inequity) , respectively. In order to orthogonalize the three explanatory variables, the actual τA used in the experiments was also set by the experimenter. Player B was asked to make decisions assuming that player A chose the In option. We therefore set τA to 60% or higher (player A is expected to choose the Out option when τ is small). More specifically, τA was 60% 7 times, 70% 5 times, 80% 13 times, 90% 11 times, and 100% 9 times. We display the actual values of x, y, z, and τA in Extended Data Figure 1-1. The correlation coefficients among the three explanatory variables were less than 0.30 and insignificant (p > 0.05); the values of guilt and inequity were designed to be orthogonal [the correlation coefficient of these two variables was −0.138 and nonsignificant (p = 0.367)] to dissociate the computational processes for guilt aversion and inequity aversion.

This logistic regression was computed using the R statistical package (R Core Team, 2021). We used the brglm package to conduct our maximum likelihood estimation with the bias-reduction method (Kosmidis, 2019).

Model validation and comparison

Our utility model comprises three separate components: Reward, Guilt, and Inequity, as defined above. With regard to Inequity, we adopted the absolute difference for Inequity. However, participants may alternatively use Fehr and Schmidt’s model, which splits the inequity into positive (called Inequity-positive hereafter) and negative (called Inequity-negative hereafter) terms. Therefore, we need to verify which model (component) better explains the data for the current experiments.

To address this issue, we first compared 10 possible models (for details of the 10 models, see Fig. 2B) based on the predictive negative log likelihoods using a cross-validation. This cross-validation approach for value-based decision-making allows us to avoid overfitting the data and to compare models with different numbers of parameters robustly. It has also been adopted in many recent studies (Daw, 2011; Smith et al., 2014; Linderman and Gershman, 2017; Park et al., 2019; Fig. 2B). We also compared more familiar BIC values for the models and exemplified the ones with the first and second minimum BIC to confirm the results (Fig. 2C).

More specifically, to compute the minimum predictive negative log-likelihood, we repeated bootstrap (500 iterations) 3-fold cross-validations for the model validation and comparison. For each model, we randomly divided 45 trials for each participant into three groups of equal size (i.e., 15). We fitted the model to 30 trials and predicted the behavior in the held-out 15 trials and repeated this process three times. We repeated this 3-fold cross-validation procedure 500 times and selected the model with the minimum predictive negative log-likelihood for held-out trials.

Participants

A total of 52 participants (mean age 21.2 years; SD = 1.4 years; 26 females) participated in the fMRI experiments. They were scanned on a Siemens 3T Trio scanner at the Center for Information and Neural Networks (CiNet) of the National Institute of Information and Communications Technology (NICT). The ethical committees of the NICT approved this study, and all participants gave informed consent. Participants received money proportional to the number of payoffs earned during the experiment (equivalent to 45–60 United States dollars). Although our task was the same as the one in Nihonsugi et al. (2015), we collected completely different participants in this study for two main reasons. First, we had access to a 64-channel MRI coil to analyze the DMPFC and VMPFC. The 64-channel brain coil provides a 1.3-fold higher signal-to-noise ratio in the brain cortex than the 32-channel array (Keil et al., 2013). Second, the number of participants (n = 42) in Nihonsugi et al. (2015) was not enough for re-analysis; Yarkoni (2009) suggested that a sample size of >50 is necessary for identifying a moderate correlation at relatively conservative thresholds. Additionally, we also wished to test whether we could replicate our previous results.

Experimental design and procedure

We conducted two experiments in which participants played a trust game in different roles (Fig. 1C; see also the instructions in Extended Data Fig. 1-2). In the first (behavioral) experiment, >10 participants per experiment were invited into a room and read instructions of the rules and procedure of the trust game. Every participant played the trust game as player A (i.e., choose In or Out and reveal belief probability τA ) and experienced one trial. The participants were informed that these choices would be used when player B made their choice in the second (fMRI) experiment. However, player A was not informed of player B’s identity. Participants were told that earnings for player A will be determined according to the actual outcome made by both players’ choices if A’s choice is used in the second experiment.

The second experiment was conducted on average 6 d (range = 1–10 d) after the first experiment. All participants played the game as player B (i.e., choose Cooperate or Defect with knowledge of player A’s belief probability) for 45 trials. Participants were instructed to assume that player A chose In in this experiment (the Out option is illustrated as a dashed line in Fig. 1B). The sequence of the trials was randomized across subjects. Participants were told that the other participant (player A) differed for each trial and that the pairings were anonymous. We did not provide any feedback to the participants during the experiment. Participants were also informed that earnings for player B will be the sum of the show-up fee and the actual outcome obtained from both players’ choices in the 45 trials.

Because there was the risk that player B felt that the other player was hypothetical rather than real, we invited >10 participants at a time into a room in the first experiment to make them realize the other’s presence and impress on them that they would have a real partner in the second experiment. In addition, when giving instructions for the second experiment, we repeatedly explained that we had conducted similar first experiments many times and that there were many player As and the partner in the second experiment was one of them. In other words, on the day of the second experiment, the participants were likely to think about other participants in the first experiment. Thus, although the experiment was hypothetical, we assume that the participants were engaged in the tasks as if they were in a real interaction. Indeed, no participant reported or even referred to the absence of their partner in a postexperiment interview.

After reading the instructions for the task and procedure, the participants were briefed about the rules of the game by the experimenter and tested to confirm that they understood the rules. They were then individually invited into the scanning room and practiced the game using the response buttons in the scanner.

Functional images were acquired as participants played the game. The timeline of a trial is shown in Figure 1B. Each trial began with a 2- to 5-s preparation interval during which time a green fixation cross was presented for the first 1 s and then a yellow fixation cross (cue phase) was presented for the remainder of the time. The participants were then presented with the trust game, including the allocation of monetary payoffs for each choice and player A’s belief, and selected Cooperate or Defect by pressing the corresponding button within 5 s (choice phase). In each trial, participants made their choice on the assumption that player A chose In. This was followed by the presentation of a fixation cross for a variable time period of 6–15 s (rest phase).

After scanning, all participants answered the questionnaire. For guilt aversion behavior, participants were asked to answer the following three questions on a five-point scale (1: I don’t think so,…, 5: I think so):

1. Did you think that the reason why player A chose In was because they expected (and aimed) to gain xA yen (i.e., the result of player B choosing Cooperate)?

2. Did you think that choosing Defect would reduce the payoff (xA yen) expected by player A?

3. Did you feel guilt that your choice of Defect would reduce the payoff (xA yen) expected by player A?

Question a examined whether the respondent understood the partner’s intention of choosing In (the meaning behind the expectation); question b examined whether the respondent was aware that their choice of Defect reduces their partner’s expected payoff; and question c asked whether the respondent felt guilty when he/she reduced their partner’s expected payoff.

fMRI image acquisition

Scanning was performed on a Siemens 3T Trio scanner with a 64-channel coil at CiNet using an echoplanar imaging (EPI) sequence with the following parameters: repetition time (TR) = 3000 ms, echo time (TE) = 25 ms, flip angle = 90°, matrix = 64 × 64, field of view (FOV) = 192 mm, slice thickness = 3 mm, gap = 0 mm, and ascending interleaved slice acquisition of 51 axial slices. High-resolution T1-weighted anatomic scans were acquired using an MPRAGE pulse sequence (TR = 2000 ms, TE = 1.98 ms, FOV = 256 mm, image matrix 256 × 256, slice thickness = 1 mm). We discarded the first two EPI images before data processing to compensate for T1 saturation effects.

fMRI data preprocessing

SPM12 (http://www.fil.ion.ucl.ac.uk/spm) was used for the MRI data preprocessing and analysis. Preprocessing included motion correction, coregistration to the participant’s anatomic image, and spatial normalization to the standard Montreal Neurologic Institute (MNI) T2 template with a resampled voxel size of 2 mm. Coregistered EPI data were normalized using an anatomic normalization parameter. Spatial smoothing was performed using an 8-mm Gaussian kernel.

General analysis methods

To explore the neural basis of guilt, inequity and value difference, we performed a general linear model (GLM) analysis of the functional data. We constructed two GLM models.

Region of interest (ROI) analysis

For the Guilt contrast in GLM1, because of the lack of adequate previous neuroimaging studies and consistent imaging results for guilt aversion, we had no specific priori hypothesis and performed no ROI analysis. However, for GLM1.1 (gender difference in guilt), we did have a priori hypothesis from previous lesion studies that showed the VMPFC is involved in gender differences in social cognition (Tranel et al., 2005; Sutterer et al., 2015). Therefore, we performed a ROI analysis on whether this region survived a small volume correction at p < 0.05 with an FWE correction. For Inequity contrast in GLM1 and GLM1.2, because we had a priori hypothesis from previous research that found the amygdala and striatum are involved in inequity (Haruno and Frith, 2010; Tricomi et al., 2010; Gospic et al., 2011; Crockett et al., 2013; Haruno et al., 2014; Tanaka et al., 2017) and there exists a gender difference in inequity (Soutschek et al., 2017), we employed a ROI analysis with a small volume correction (p < 0.05; small volume FWE corrected). With regard to value difference in GLM2, we again had a priori hypothesis from previous research that found the VMPFC is involved in value difference (Hunt et al., 2012; Nicolle et al., 2012). Therefore, we employed a ROI analysis with a small volume correction (p < 0.05; small volume FWE corrected).

The small volume of the VMPFC and DMPFC was based on a 15-mm sphere around the coordinates (x = 2, y = 41, z = −6) and (x = −3, y = 48, z = 30), because these coordinates were used in a neuroimaging study (Baumgartner et al., 2011) of social preferences similar to ours. In that study, the VMPFC coordinates were determined by averaging the peak coordinates across five neuroimaging studies (value and economic decision-making), and the DMPFC coordinates were based on a meta-analysis study on social cognition (van Overwalle, 2009). Furthermore, the VMPFC coordinates (subjective value: x = 2, y = 46, z = −8; decision stage: x = 2, y = 40, z = −8) in a previous meta-analysis (Bartra et al., 2013) are very close to the coordinates we used. The small volumes for the amygdala and striatum were defined using the WFU PickAtlas toolbox (Maldjian et al., 2003).

Psycho-physiological interaction (PPI) analysis

We performed two PPI analyses using the function of SPM12.

PPI1

Having confirmed that the VMPFC was involved in value difference by the GLM2 analysis, we next conducted a hypothesis-based PPI analysis to examine whether this VMPFC activity truly integrates the value components of Guilt and Inequity. More specifically, we used VMPFC (shown in Fig. 3C) as a seed region and examined whether brain areas associated with VMPFC × Guilt overlapped with the Guilt-correlated areas (i.e., DLPFC and DMPFC in Fig. 3A) and whether brain areas associated with VMPFC × Inequity overlapped with the Inequity-correlated area (i.e., striatum in Fig. 3B). For each subject, we extracted the time course of activity from a 5-mm-radius volume of interest (VOI) around the peak voxel in the VMPFC (shown in Fig. 3C). Based on the procedure by Gitelman et al. (2003), the time series of the VOI was extracted and then deconvolved, multiplied with the psychological variable (size of Guilt or Inequity), and reconvolved with the HRF set up as the PPI regressor. The three regressors (i.e., PPI regressor, VOI time series, and psychological variable) were then convolved with the canonical HRF and entered into the regression model along with six head motion parameters. The individual parameter estimate image for the PPI regressor was subsequently subjected to a one-sample t test. Finally, we also included a gender-indicating variable and performed a group analysis to identify brain regions showing increased functional connectivity with the seed VOI during the Choice phase. For the whole-brain analysis, we used a threshold of p < 0.001 uncorrected.

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Figure 3.

Activities correlated with Guilt, Inequity, and Utility in both genders. A, Activities in the right and left DLPFC and DMPFC were correlated with guilt (right DLPFC, p < 0.001; left DLPFC, p < 0.001; DMPFC, p < 0.001). Activities related with Guilt in both genders are listed in Extended Data Figure 3-1. B, The bilateral ventral striatum activity was correlated with inequity (right ventral striatum, p = 0.035; left ventral striatum, p = 0.042). Activities related with Inequity in both genders are listed in Extended Data Figure 3-2. C, left, Activity in the VMPFC was positively correlated with the value difference (larger utility-smaller utility; p = 0.040, see also Extended Data Fig. 3-3). Top right, Overlay of the VMPFC × Guilt cluster (green) and the Guilt-correlated region shown in A (red). These two areas overlap in the DMPFC (brown). For display purposes, we used a threshold of p < 0.001 uncorrected for the Guilt contrast, and a threshold of p < 0.005 uncorrected for VMPFC × Guilt. Results of the PPI analysis for VMPFC × Guilt in both genders are summarized in Extended Data Figure 3-4. Bottom right, Overlay of the VMPFC × Inequity cluster (green) and the Inequity-correlated region shown in B (red). These two areas overlap in the striatum (brown) at the relaxed threshold. For display purposes, the threshold of the VMPFC × Inequity contrast is uncorrected p < 0.05. Results of the PPI analysis for VMPFC × Inequity in both genders are summarized in Extended Data Figure 3-5.

Extended Data Figure 3-1

Activities related with Guilt in both genders. Download Figure 3-1, DOCX file.

Extended Data Figure 3-2

Activities related with Inequity in both genders. Download Figure 3-2, DOCX file.

Extended Data Figure 3-3

Activities related with value differences in both genders. Download Figure 3-3, DOCX file.

Extended Data Figure 3-4

Results of the PPI analysis for VMPFC × Guilt in both genders. Download Figure 3-4, DOCX file.

Extended Data Figure 3-5

Results of the PPI analysis for VMPFC × Inequity in both genders. Download Figure 3-5, DOCX file.

PPI2

The goal of this analysis was to examine whether different brain networks are involved in the computation of guilt and inequity between men and women. More specifically, this analysis aimed to find differences between men and women in brain regions that correlate more strongly with VMPFC or striatum activity as guilt or inequity increases. For each subject, we extracted the time course of activity from VOIs with a 5-mm-radius around the peak voxel in the VMPFC, as shown in Figure 4A, and the ventral striatum, as shown in Figure 5A. For this analysis, the PPI terms were defined as VMPFC × guilt and ventral striatum × inequity. We entered six variables (i.e., PPI regressor, VOI time series and psychological variable for guilt and inequity, respectively) and movement regressors into a GLM. The individual parameter estimate image for the PPI regressor was subsequently subjected to a one-sample t test. Finally, group analysis was performed to identify brain regions showing increased functional connectivity with the seed VOIs. A two-sample t test was performed to further assess different connectivity patterns between men and women. For the whole-brain analysis, a threshold of p < 0.001 uncorrected at the peak voxel level with an extent threshold of k = 20 was adopted.

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Figure 4.

Results of gender differences for guilt in neural activity. A, Men showed greater VMPFC activity than women (p = 0.029). As displayed in the box plot, the extracted contrast estimates in the VMPFC demonstrate that men showed increased VMPFC activity in response to guilt (p < 0.001, t test). Importantly, the VMPFC seed exhibited positive correlation with activity in the right DLPFC as guilt increases for men but not for women (p < 0.001, uncorrected). Differences of activities related to guilt between men and women are listed in Extended Data Figure 4-1. B, Overlay of the VMPFC, which is related to gender difference in Guilt (blue), and the Guilt-correlated region (red). For display purposes, the threshold for the Guilt areas is p < 0.001 uncorrected and the VMPFC threshold is p < 0.005 uncorrected. The activation of the VMPFC involved in gender difference in Guilt largely overlaps with the clusters of activation correlated with guilt (overlap area; brown). C, Overlay of the VMPFC cluster shown in Figure 3C, which was positively correlated with the value difference (green), and the VMPFC cluster shown in A, which showed differential activation in the guilt contrast (men > women; blue). These two areas are close but do not overlap. D, Using a PPI analysis, a comparison of men and women showed enhanced functional connectivity of the VMPFC with the right DLPFC during the processing of guilt only in men (orange areas). This activation area (DLPFC) largely overlaps with the clusters of activation correlated with guilt shown in Figure 3A (shown in this figure as red areas). Results of the PPI analysis for guilt when testing for gender differences are shown in Extended Data Figure 4-2. E, Mediation analysis of the relationship of gender, DLPFC-VMPFC connectivity and β(Guilt) shows that DLPFC-VMPFC connectivity is a complete mediator of the interaction between gender and guilt-aversion behavior. Path coefficients are shown next to arrows with SEs in parentheses; *p < 0.05, ***p < 0.001. F, Diagram summarizing the results of our analyses. Activities in the DLPFC and DMPFC were correlated with guilt in both genders. The blue line represents a stronger connectivity between the VMPFC and right DLPFC in men than in women depending on VMPFC × Guilt, and the green line represents stronger positive coupling between the VMPFC and DMPFC depending on VMPFC × value difference.

Extended Data Figure 4-1

Differences of activities related to guilt between men and women. Download Figure 4-1, DOCX file.

Extended Data Figure 4-2

Results of the PPI analysis for guilt when testing for gender differences. Download Figure 4-2, DOCX file.

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Figure 5.

Results of gender differences in neural activity for inequity. A, Women showed greater ventral striatum activity than men (p = 0.008). The box plot illustrates the contrast estimates in the right ventral striatum and shows that only women showed increased activity in response to inequity (p < 0.001, t test). Differences of activities related to inequity between men and women are summarized in Extended Data Figure 5-1. B, A mediation analysis shows that the mediation effect of the striatum is significant (a*b, p < 0.001). Path coefficients are shown next to the arrows with SEs in parentheses; *p < 0.05, ***p < 0.001.

Extended Data Figure 5-1

Differences of activities related to inequity between men and women. Download Figure 5-1, DOCX file.

Mediation analysis

We performed a mediation analysis to test whether the interaction between gender and guilt-based prosocial behavior was mediated by a brain function using a mediation toolbox (https://github.com/canlab/MediationToolbox; Wager et al., 2008). Briefly, this analysis was based on a standard three-variable path model, as shown in Figure 4D. This analysis quantifies the degree to which a relationship between two variables, X and Y, can be explained by another variable, M.

For the guilt-aversion behavioral analysis, we defined X as the gender-indicating variable (1 = men), Y as the behavioral variable, β(Guilt), and M as the brain variable functional connectivity between the right DLPFC and VMPFC (Fig. 4A). Following convention, we required that three tests reach statistical significance in the mediation analysis. First, path a measured the association between the gender-indicating variable and the functional connectivity. Second, path b measured the association between the functional connectivity and β(Guilt) after controlling for the gender-indicating variable. Third, the mediation effect, defined as the product of the indirect paths (a×b ), must be significant. We refer to the overall predictor-outcome relationship as effect c and the direct effect controlling for the mediator c′ . Thus, the a×b effect tests the significance of c−c′ . We conducted bootstrap tests (10,000 iterations) for statistical significance of the mediators.

For inequity-aversion behavioral analysis, we defined X as the gender-indicating variable (1 = women), Y as the behavioral variable β(Inequity) , and M as the brain variables (striatum shown in Fig. 5A).

Participants

We analyzed data from 4723 participants (mean age 37.9 years, SD = 15.4 years, 2737 females; for more detailed descriptive statistics, see Table 1) who followed the task instructions correctly and spent longer than 1 h to complete seven different personality trait tests such as Big Five Inventory, anxiety (STAI) and depression (BDI) and the trust game task. These data were collected using our in-house online experiment system. The study protocol was approved by the ethical committees of the NICT, and all participants gave informed consent. For their participation, participants were paid in cashable points proportional to the number of payoffs earned during the experiment (equivalent to 3–5 United States dollars).

Experimental design and procedure

Participants performed a trust game on our in-house online experiment system in a similar way to the fMRI study (Fig. 1C). We conducted two consecutive experiments in which participants played a trust game in a different role. Before the first experiment, online participants read the rules of the trust game and the procedure. In the first experiment, every participant played the trust game as player A (i.e., choose In or Out and reveal belief probability τA ) and experienced one trial. Participants knew these choices would be used, and the pairings were anonymous when player B made their choice in the second behavioral experiment.

In the second experiment, all participants played the game as player B (i.e., choose Cooperate or Defect with knowledge of player A’s belief probability). Participants (player B) were instructed to assume that player A chose In. Every participant experienced 45 trials. The sequence of the trials was randomized across subjects. Participants were told that the other participant (player A) differed for each trial and that the pairings were anonymous. We did not provide any feedback to the participants during the experiment. All participants answered seven different personality trait tests including the Big Five Inventory. The final earnings were calculated following the same pattern as the fMRI study.

Evaluation of cognitive mechanisms using Big Five Inventory

We first examined the relationship between guilt-aversion [β(Guilt) ] and gender. Specifically, we estimated β(Guilt) for participants by the same logistic regression as the fMRI study and compared β(Guilt)s between men and women. To investigate two different cognitive processes (i.e., agreeableness and conscientiousness) potentially underlying gender difference in guilt aversion and to control for the confounding effects of the participant’s socioeconomic status, we conducted a multiple linear regression analysis based on the following equation: β(Guilt)i=β1Neuroticismi + β2Extraversioni + β3Opennessi+β4Agreeablenessi + β5Conscientiousnessi + β6Agei+β7SelfEduHistoryi + β8ParentsEduHistoryi + β9Income + β10Occupation + β11SubjectiveSESi + β12Sexi×Neuroticismi + β13Sexi×Extraversioni + β14Sexi×Opennessi + β15Sexi×Agreeablenessi + β16Sexi×Conscientiousnessi + β17Sexi×Agei + β18Sexi×SelfEduHistoryi + β19Sexi×ParentsEduHistoryi + β20Sexi×Income + β21Sexi×Occupation + β22Sexi×SubjectiveSESi + εi, where Neuroticismi , Extraversioni , Opennessi , Agreeableness_i, and Conscientiousnessi are the individual’s Big Five score (Murakami and Murakami, 1999), Agei is the individual’s age, SelfEduHistoryi and ParentsEduHistoryi are the individual’s scores of educational history and his/her parents’ score of educational history, respectively (Okada et al., 2014), Incomei and Occupationi are the individual’s income and occupation, respectively (Ganzeboom et al., 1992), and SubjectiveSESi is the individual’s subjective socioeconomic status (Adler et al., 2000). Sexi is the binary variable representing individual (1)’s sex (men = 1) and used to represent interactive effects with Big Five scores and socioeconomic status variables.The multiple linear regressions were conducted using the glm package based on the R statistical package (R Core Team, 2021).