Human neurocomputational mechanisms of guilt-driven and shame-driven altruistic behavior

Guilt and shame, two prominent moral emotions, underpin a wide array of social behaviors and phenomena, including norm compliance, cooperation, face-saving strategies, suicides, and even large-scale conflicts like wars (Haidt, 2003; Sznycer et al., 2021). Although guilt and shame often coexist after moral violation and serve to inhibit further transgressions (Eisenberg, 2000; Lewis, 1971), they differ in their associations with psychological and behavioral issues (Tangney and Dearing, 2003). For instance, shame is commonly associated with higher levels of anxiety, depression, stress, eating disorders, and aggression, whereas guilt is typically unrelated or negatively related to these issues (Blythin et al., 2020; Caldwell et al., 2021; Cândea and Szentagotai-Tătar, 2018; Elison et al., 2014; Kim et al., 2011; Schuster et al., 2021).

Recognizing their distinctions, a growing body of research in psychology and neuroscience has sought to elucidate the psychological and neural mechanisms differentiating guilt and shame, providing insights into how these emotions are processed and regulated. Psychological studies have revealed that guilt involves more concerns on one’s actions on others (i.e. negative behavioral impacts), whereas shame involves more concerns on one’s self-image (i.e. negative self-evaluations; Lewis, 1971; Tangney et al., 2007; Tangney and Dearing, 2003). Echoing and complementing these findings, neuroscience research showed that neural activity in brain regions related to other-oriented theory-of-mind processing (temporoparietal junction [TPJ] and dorsomedial prefrontal cortex [DMPFC]), self-oriented self-referential processing (anterior cingulate cortex [ACC] and DMPFC), and cognitive control (lateral prefrontal cortex [LPFC]) can distinguish guilt and shame (Piretti et al., 2023; Zhu et al., 2019a).

Although the psychological processes and neural activities related to guilt and shame experience are relatively well-documented, the cognitive antecedents of these emotions and their neural representation remain insufficiently understood. Existing research has identified harm (i.e. severity of harm) and responsibility (i.e. responsibility for harm) as cognitive antecedents influencing guilt (Abrams and Doosje, 2011; Berndsen et al., 2004; Cehajić-Clancy et al., 2011; Gao et al., 2021; Iyer et al., 2007; Koban et al., 2013; Li et al., 2020; Tangney, 1992; Yu et al., 2014) and shame (Iyer et al., 2007; Koban et al., 2013; Tangney, 1992). However, it remains unclear whether these factors differ in the strength of their influence on guilt and shame. Functionalist theories propose that guilt functions to minimize and repair undue harm on valued others, thereby addressing the adaptive problem of insufficiently valuing others—a behavior that can indirectly harm oneself (e.g. harming one’s partner can disrupt cooperation, ultimately damaging one’s own interests; Sznycer, 2019). In contrast, shame functions to prevent and mitigate the threat of being devalued by others, thus addressing the adaptive problem of reputational damage to the self (e.g. being perceived as deficient in competence or morality by others; Landers et al., 2024a; Sznycer, 2019; Sznycer et al., 2021). If the functionalist theories are correct, the intensity of guilt and shame should more closely track the information that reflects the adaptive problems they are meant to address (e.g. Sznycer et al., 2016). Accordingly, severity of harm—a factor reflecting inadequate valuation of others—is expected to have a stronger impact on guilt than on shame, whereas responsibility for harm—a factor tied to devaluation by others, particularly in open and transparent social contexts—is anticipated to exert a greater influence on shame than guilt (see Appendix 1 for illustrative examples).

Previous neuroscience studies have examined the neural response to harm or responsibility. Greater harm inflicted on others is associated with stronger activation in the DMPFC, TPJ, and insula (Crockett et al., 2017; Koban et al., 2013). Greater responsibility for harm is linked to stronger activation in the anterior middle cingulate cortex (aMCC) (Li et al., 2020; Yu et al., 2014). Despite these findings, it remains elusive how the human brain integrates harm and responsibility when both factors are simultaneously present and dynamically vary. One possibility is that distinct brain regions separately encode harm and responsibility, with their signals converging in other regions responsible for integrating these two factors (e.g. Hu et al., 2017; Yu et al., 2018). Alternatively, some brain regions may directly encode an integrated representation of harm and responsibility, potentially through computational processes such as their product or quotient (e.g. Gray et al., 2002).

According to appraisal theory, emotions are not directly elicited by cognitive antecedents (e.g. harm and responsibility) but arise from the appraisal processes applied to these antecedents (Lazarus and Smith, 1988; Moors et al., 2013). Since appraisal processes vary across individuals, the same stimuli can evoke emotions of distinct intensities and even distinct types across different individuals (Ellsworth, 2013; Moors et al., 2013). Individual differences in emotional experiences, such as guilt and shame, may originate from the variations in neural responses to their cognitive antecedents (Hamann and Canli, 2004; Morawetz and Basten, 2024). However, the neural substrates that determine the extent to which harm and responsibility are transformed into guilt and shame remain largely unknown.

Shifting the focus from the origins of guilt and shame to their consequences, existing evidence on the distinct associations between these emotions and behavior remains incomplete. Guilt is widely recognized as a powerful motivator of altruistic behaviors, such as offering apologies and making amends (Graton and Ric, 2017; Howell et al., 2012; Ketelaar and Tung Au, 2003; Zhu et al., 2017). In comparison, shame appears to have a weaker promotive effect on altruistic behaviors compared to guilt (de Hooge et al., 2008; de Hooge et al., 2007; Declerck et al., 2014) and is commonly associated with non-cooperative and antisocial behaviors, including hiding, evasion, self-improvement, externalizing blame, and aggression (de Hooge et al., 2010; Gausel and Leach, 2011; Landers et al., 2024a; Tangney et al., 1996; Zhu et al., 2019c). Most studies examined only guilt or only shame, restricting direct comparisons of their behavioral effects. The few studies that have directly compared these emotions relied on participants’ recollections of past personal events (de Hooge et al., 2007) or imagined scenarios (Ghorbani et al., 2013) to elicit guilt and shame. Variations in recollections and imagined contexts may introduce potential confounds, making it difficult to discern whether the observed behavioral differences are caused by guilt and shame or by irrelevant contextual factors.

Neurally, whether the transformation of guilt and shame into behavior depends on distinct neural bases remains an open question. Yu et al., 2014 found that the aMCC and midbrain nuclei are involved in linking guilt to compensatory behavior. To the best of our knowledge, the neural bases underlying the conversion of shame into behavior have yet to be explored. Naturally, this also means that no studies have directly compared the neural correlates of guilt-driven and shame-driven behavior.

To address the research gaps outlined above and provide computational, algorithmic, and neural accounts of guilt and shame (see Yu et al., 2024), we combined computational modeling and functional magnetic resonance imaging (fMRI) with a novel interpersonal game. In this game, we independently manipulated the harm inflicted on a victim and the responsibility of participants. Throughout the experiment, participants engaged in compensatory decision-making while undergoing fMRI scanning, followed by post-experimental self-reports of their guilt and shame feelings. This paradigm allowed us to explore the associations among harm, responsibility, guilt, shame, and compensation, as well as to uncover relevant neural substrates. Based on the functionalist theories (Baumeister et al., 1994; Gilbert, 1997; Landers et al., 2024a; Sznycer, 2019; Sznycer et al., 2016), we predicted that harm would have a greater impact on guilt than shame, whereas responsibility has a greater impact on shame than guilt. Based on previous research on the relationships between guilt, shame, and altruistic behavior (de Hooge et al., 2007), we predicted that guilt would exert stronger influence on compensation than shame. Drawing from existing knowledge of the neural correlates of guilt and shame (Bastin et al., 2016; Li et al., 2020; Yu et al., 2020; Zhu et al., 2019a), we predicted that brain regions involved in emotional processing (e.g. insula), self-referential processing (e.g. DMPFC), theory-of-mind processing (e.g. TPJ), and cognitive control (e.g. LPFC) would play important roles in the neural representation of harm and responsibility, and the emergence of guilt-driven and shame-driven compensation.

The experiment consisted of two phases (Figure 1A). In the first phase, participants completed online personality questionnaires assessing trait guilt, trait shame, trait gratitude, and social value orientation (SVO) at least one day before the laboratory session. In the second phase, each participant came to the laboratory individually. They first underwent an individualized pain calibration procedure designed to familiarize them with the different levels of electric shock used later in the interpersonal game and then completed the interpersonal game while undergoing fMRI scanning (Figure 1B). In the interpersonal game, the participant acted as one of four deciders performing a dots estimation task. If any decider made an incorrect estimate, a receiver received a painful electric shock whose intensity was randomly determined. The task was repeated across multiple trials. Unbeknownst to the participant, the other deciders were confederates and the receiver was fictitious; all outcomes were predetermined. After each outcome, the participant decided how much financial compensation to provide to the receiver. We focused on trials in which the participant, acting as a decider, was informed that they had made an incorrect estimate. Harm (levels 1–4) was manipulated through the intensity of the electric shock, whereas responsibility (levels 1–4) was manipulated by varying how many of the other deciders also made incorrect estimates. Following the game, participants completed a post-game survey in which they reported their feelings of guilt and shame for different outcomes, rated their perceived responsibility, and answered additional questions. Further procedural details are provided in the Materials and methods section.

Figure 1

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Distinct impacts of harm and responsibility on guilt and shame

To confirm and compare the effects of harm and responsibility on guilt and shame, we ran two linear mixed-effect regression analyses. In the first analysis, participants’ emotion ratings were regressed onto harm levels, emotion types (guilt vs. shame), and their interaction. In the second analysis, participants’ emotion ratings were regressed onto responsibility levels, emotion types (guilt vs. shame), and their interaction. To determine whether harm and responsibility had significant effects on guilt and shame, respectively, we performed simple slope analyses for each emotion type. We found that harm significantly increased participants’ guilt ratings (β_guilt = 0.74, T(67) = 8.92, p<0.001) and shame ratings (β_shame = 0.23, T(67) = 2.77, p=0.007). Similarly, responsibility significantly increased participants’ guilt ratings (β_guilt = 0.57, T(61) = 5.27, p<0.001) and shame ratings (β_shame = 0.93, T(61) = 8.54, p<0.001). Of importance, harm had a stronger effect on guilt than on shame, as indicated by the interaction effect between harm and emotion type (β_guilt - β_shame = 0.51, T(41) = 5.96, p<0.001; Figure 2A; Appendix 3—table 3). Conversely, responsibility exerted a greater influence on shame than on guilt, as shown by the interaction effect between responsibility and emotion type (β_guilt - β_shame=–0.35, T(41) = 3.60, p<0.001; Figure 2B; Appendix 3—table 4). To control for the potential influence of other interaction effects, we conducted an additional linear mixed-effect regression. We regressed participants’ emotion ratings onto harm levels, responsibility levels, emotion types (guilt vs. shame), and all their interactions. The results showed that the interaction between harm and emotion type, as well as the interaction between responsibility and emotion type, remained significant (Appendix 3—table 5). Together, these findings demonstrate the differential impacts of harm and responsibility on guilt and shame.

Figure 2

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Computational models of compensatory behavior

After confirming their associations through separate linear mixed-effect regression analyses, we used utility models to characterize how individuals integrate harm with responsibility during guilt-driven and shame-driven compensatory decision-making within a unified computational framework. We constructed eight models across two model families, which aimed at capturing individuals’ distinct latent psychological processes. They varied in whether self-interest was considered, whether compensatory baseline existed, and how harm and responsibility were integrated (see Materials and methods). Based on the Bayesian information criteria, Model 1.3 outperformed other alternative models (Appendix 3—table 8). This model assumes that, during compensatory decision-making, individuals disregard their self-interest, adopt a compensatory baseline, and integrate harm and the number of wrongdoers in the form of a quotient (rather than integrating harm and responsibility in the form of a product). The utility (U) of a compensatory decision is described as follows (Model 1.3):

${\displaystyle {\displaystyle U\left(D\right)=-|\kappa \times \frac{H}{W}+\eta -D|}}$

where H and W are the level of harm and the number of wrongdoers, respectively. $\frac{H}{W}$ represents the average harm for which participants are responsible. The parameter κ reflects compensatory sensitivity. Higher κ values indicate that participants are inclined to spend more tokens to compensate for the harm they are responsible for. The parameter η denotes compensatory baseline. Higher η values indicate that participants believe they should distribute more tokens to the receiver for compensation, regardless of the level of harm and the number of wrongdoers. D is the number of tokens distributed to the receiver by participants. It is assumed that participants are averse to giving the receiver less compensation (undercompensation) and more compensation (overcompensation) they believe the receiver deserves.

We validated the winning model (Model 1.3) through a variety of analyses. Simulation tests indicated that the model could replicate the influence of harm level and responsibility level on compensation through simulation (Figure 3A and B) and predict participants’ compensatory decisions with significantly higher accuracy than the chance level (mean accuracy: 31%, 95% confidence interval (CI): [27%, 36%], chance level: 9%). Parameter recovery tests revealed that the parameters were highly identifiable (Pearson correlation between real and recovered parameters: r_κ=0.95, CI = [0.95, 0.96]; r_η=0.99, CI = [0.99, 0.99]). No evidence supports that the psychological processes represented by the parameters κ and η overlap, as the Pearson correlations between the two parameters were not significant (R=–0.17, p=0.289; Figure 3C).

Figure 3

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Neural representation of cognitive antecedents

To investigate the neural representation of the cognitive antecedents of guilt and shame during outcome evaluation, we constructed general linear models (GLMs) to identify brain regions that respond parametrically to harm, the number of wrongdoers, the quotient of harm divided by the number of wrongdoers, and the product of harm and responsibility (responsibility = 5 – the number of wrongdoers; see Materials and methods). No brain region showed significant responses to harm, which implies the brain doesn’t represent harm in isolation. The precentral and postcentral cortices, known to be involved in movement preparation (Porro et al., 1996; Thoenissen et al., 2002), exhibited positive parametric responses to the number of wrongdoers, potentially reflecting motor preparation for subsequent button-press action related to compensation (Appendix 3—table 9). The results of the neural representation of the level of harm and the number of wrongdoers remained consistent, regardless of whether these two parametric modulators were put in the same GLM or in separated GLMs (Appendix 3—Tables 9 and 10).

The quotient of harm divided by the number of wrongdoers correlated parametrically with the activation in brain regions involved in social cognition. Specifically, the striatum linked to value computation (Bartra et al., 2013; Clithero and Rangel, 2014) and the posterior insula (pINS) associated with inequity aversion (Gao et al., 2018; Hsu et al., 2008) exhibited negative parametric responses to the quotient (Figure 4A; Appendix 3—table 11). In contrast, no brain region showed significant responses to the product of harm and responsibility. Together with the findings from computational modeling, these fMRI results suggest that individuals integrate harm and the number of wrongdoers in the form of a quotient, rather than integrating harm and responsibility in the form of a product.

Figure 4

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Neural basis of compensatory sensitivity

To examine the neural basis of the individual guilt-driven and shame-driven compensatory sensitivities, we correlated the coefficient estimates of guilt and shame on compensation from the linear mixed-effect regression (Appendix 3—table 7) with the whole-brain neural activity. The whole-brain analysis showed that the activation in the DMPFC/SMA was significantly correlated with both guilt-driven and shame-driven compensatory sensitivities (Appendix 3—table 14). Besides, the activity in the left temporal pole (TP) was significantly correlated with guilt-driven compensatory sensitivity but not with shame-driven compensatory sensitivity (Figure 5A), whereas activity in the bilateral inferior parietal lobe (IPL) and left LPFC clusters was significantly correlated with shame-driven compensatory sensitivity but not with guilt-driven compensatory sensitivity (Figure 5B). To directly assess whether these brain regions were more involved in guilt-driven or shame-driven compensatory sensitivity, we compared the Pearson correlations between brain activity and the two types of compensatory sensitivities. The results revealed that the left LPFC was more engaged in shame-driven compensatory sensitivity (Z=1.93, p=0.053), as its activity showed a marginally stronger positive correlation with shame-driven sensitivity than with guilt-driven sensitivity (Figure 5C). No significant difference was found in the Pearson correlations between the activity of the bilateral IPL and the two types of sensitivities (Appendix 3—table 15). For the TP, the effective sample size was too small to yield reliable results (see Materials and methods).

Figure 5

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In addition to guilt and shame, other emotions or motivations may also contribute to the transformation of harm and responsibility into compensatory behavior. The parameter κ from our winning computational model ought to capture the combined effects of various psychological processes on compensation, including guilt and shame. Confirming their associations, the parameter κ significantly correlated to both guilt-driven (β=2.06, T(38) = 5.85, p<0.001) and shame-driven (β=2.43, T(38) = 2.82, p=0.008) compensatory sensitivities (Appendix 3—table 16). To gain a more comprehensive understanding of the neural basis of compensatory sensitivity, we correlated κ with the whole-brain neural activity. The neural correlates of the parameter κ largely overlapped with those associated with guilt-driven and/or shame-driven compensatory sensitivities, including the DMPFC, supplementary motor area (SMA), left TP, left LPFC (LPFC), bilateral IPL (Figure 5A, B and D; Appendix 3—table 14 and Appendix 3—table 17). Notably, κ is also associated with activation in the bilateral anterior insula (aINS).

Neural correlates of trait guilt and compensation

As we observed significant correlations between trait guilt scores and compensatory behavior, we furthered our investigation by examining their neural correlates. A small-volume correction analysis revealed that repair action tendencies (i.e., a dimension of trait guilt) were significantly associated with the aMCC’s responses to the quotient of harm divided by the number of wrongdoers (peak MNI coordinates: [–3, 30, 15]; cluster size: 12 voxels; P_FWE = 0.003, small volume corrected; Figure 6A). The results of the aMCC, along with other clusters, retained significance after whole-brain correction (Figure 6B; Appendix 3—table 18). Moreover, only the activity in the aMCCs mediated the relationship between repair action tendencies and compensation (indirect effect: β=0.10, CI = [0.01, 0.21]; Figure 6C).

Figure 6

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No neural activity showed a significant correlation with negative behavior evaluations (i.e., another dimension of trait guilt), trait shame, trait gratitude, or SVO.

Guilt and shame have long been a focal point of research across various disciplines, including psychology, behavioral economics, neuroscience, and psychiatry (e.g. Chang et al., 2011; Gao et al., 2018; Landers et al., 2024b; Mottershead et al., 2024; Schuster et al., 2021; Sznycer, 2019; Tangney et al., 2007). Extensive research has investigated the psychological and neural activities associated with the experience of guilt and shame to enhance emotion regulation and improve behavioral prediction (Bastin et al., 2016; Miceli and Castelfranchi, 2018; Michl et al., 2014; Piretti et al., 2023; Pulcu et al., 2014; Takahashi et al., 2004; Tangney and Dearing, 2003; Xu et al., 2022; Zhu et al., 2019a). We extended this research by shifting the focus from the mere states of guilt and shame to their associations with the cognitive antecedents and behavioral consequences. Our findings advance the understanding of the psychological and neural mechanisms that underlie both the formation of guilt and shame and their subsequent transformation into compensatory behavior.

Consistent with previous studies (Gao et al., 2021; Iyer et al., 2007; Koban et al., 2013; Li et al., 2020; Tangney, 1992; Yu et al., 2014), we observed that both harm and responsibility increase individuals’ feelings of guilt and shame. Of importance, for the first time, we discovered that harm exerts a stronger effect on guilt than on shame, whereas responsibility has a stronger effect on shame than on guilt. These findings provide empirical support for the proposition that guilt and shame serve distinct social functions. According to functionalist theories (e.g. Baumeister et al., 1994; Gilbert, 1997; Sznycer, 2019; Sznycer et al., 2021), guilt functions to curb the harm imposed on valued others, whereas shame functions to mitigate the cost of reputational damage to oneself. If these theories hold true, guilt should be more sensitive than shame to the information related to harm inflicted on valued others, where shame should be more sensitive than guilt to the information related to reputational damage to oneself (Sznycer et al., 2016). However, few studies have provided such direct evidence. An exception is the study by Landers et al., 2024b, which found that information related to harm inflicted on valued others (e.g., liking of a victim) and concerns about reputational damage (e.g. fear of a victim) were respectively predictive of guilt and shame. Notably, Landers et al., 2024b employed a vignette-based paradigm and assessed participants’ guilt and shame using items that reflected the motivational tendencies characteristic of these emotions (e.g. guilt: ‘I would go to him and apologize for it’; shame: ‘I would feel like avoiding him for a while’). In contrast, our study utilized a laboratory-based paradigm with real-time decision-making, directly measured participants' state guilt and shame experiences, and tested new psychological factors (harm and responsibility). This approach offers greater ecological validity and provides novel evidence (Yu et al., 2024).

Existing findings suggest that guilt is more strongly linked to altruistic behavior than shame is (de Hooge et al., 2008; de Hooge et al., 2007; Declerck et al., 2014; Gausel and Leach, 2011; Graton and Ric, 2017; Ketelaar and Tung Au, 2003; Tangney et al., 2007; Tangney and Dearing, 2003). Nevertheless, because of methodological limitations, such as failing to compare guilt and shame directly or employing methods that may introduce confounding variables, conclusive evidence has been lacking. Overcoming these limitations, our study demonstrates that while both guilt and shame promote compensation, guilt is more effective in prompting compensatory behavior. The functionalist theories offer a framework for understanding why guilt exerts a stronger effect on compensation than shame does. Guilt corresponds to the adaptive problem of insufficiently valuing others (Sznycer, 2019). To address it, individuals in guilt must bring benefits to those they have harmed—typically through altruistic behavior—to correct the inequity caused by their wrongdoing. In contrast, shame is tied to reputational damage (Sznycer, 2019; Sznycer et al., 2021). Although altruistic behavior can also mitigate shame by demonstrating their social value, individuals may resort to other strategies—such as avoidance or aggression—to protect themselves from potential devaluation by others (de Hooge et al., 2018; Zhu et al., 2019c).

For a deeper understanding of social emotions, it is crucial to formally model their cognitive operations and investigate the neural underpinnings of these cognitive operations (Yu et al., 2024). Building on this research line, our computational modeling results reveal that individuals in guilt and shame disregard their self-interest, adopt a compensatory baseline, and mentally distribute harm across all wrongdoers. The findings not only offer a mechanistic explanation at the behavioral (algorithmic) level for guilt- and shame-driven compensatory decision-making, but also deepen the understanding of the phenomenon of responsibility diffusion by offering a formal mathematical formulation and linking it to compensatory behavior (Darley and Latané, 1968).

Notably, in many computational models of social decision-making, self-interest plays a crucial role (e.g. Wu et al., 2024). However, our computational findings suggest that participants disregarded self-interest during compensatory decision-making. A possible explanation is that the personal stakes in our task were relatively small (a maximum loss of 5 Chinese yuan), whereas the harm inflicted on the receiver was highly stigmatized (i.e. an electric shock). Under conditions where the harm is highly salient and the cost of compensation is low, participants may be inclined to disregard self-interest and focus solely on making appropriate compensation.

At the neural level, our findings demonstrate the involvement of the posterior insula and striatum in representing the cognitive antecedents of guilt and shame. Specifically, the activation in these brain regions decreased as the quotient of harm divided by the number of wrongdoers increased. Harm inflicted on the victim, particularly the portion for which a wrongdoer is responsible, creates a sense of inequity between them. Beyond its well-established role in interoceptive awareness (Craig, 2009; Craig, 2011), the pINS has been implicated in processing economic inequity in allocation tasks (Gao et al., 2018; Hsu et al., 2008). For instance, Hsu et al., 2008 reported that pINS activation negatively correlates with the degree of inequity, suggesting that greater inequity elicits lower pINS activation. Our results extend this role of the pINS beyond economic inequity to encompass harm inequity. Given that the striatum is implicated in value computation (Bartra et al., 2013; Crockett et al., 2017; Rilling et al., 2008), we believe that its activity reflects individuals’ perception of the loss (i.e. harm) inflicted on the victim. In contrast, no brain region had significant responses to the product of harm and responsibility. Thus, the fMRI and computational modeling findings offer convergent evidence indicating that individuals are more likely to integrate these cognitive antecedents in a form of quotient.

In addition, no brain region exhibited significant responses to harm. Only the sensorimotor areas showed significant responses to the number of wrongdoers (i.e. the complement of responsibility, 5 – responsibility level). Although the fMRI findings revealed that no brain region associated with social cognition showed significant responses to harm or responsibility, this does not suggest that the human brain encodes only a unified measure integrating harm and responsibility and does not process them as separate entities. Using more fine-grained techniques, such as intracranial electrophysiological recordings, it may still be possible to observe independent neural representations of harm and responsibility.

As to emotion sensitivity, our findings show that individuals who tend to convert responsibility into shame exhibit reduced activation in brain regions associated with other-oriented theory-of-mind processing, specifically the TPJ and STS. The TPJ and STS have been implicated in inferring others' mental states (Schurz et al., 2014). Lower activation in these regions indicates that individuals with higher responsibility-driven shame sensitivity may be less engaged in considering the victim’s experiences and thoughts. This aligns with existing research on shame, which, compared to guilt, is associated with less concerns on one’s actions on others (Tangney and Dearing, 2003) and weaker activation in the TPJ (Zhu et al., 2019a).

Regarding compensatory sensitivity, our results show that both individuals with higher guilt-driven and shame-driven compensatory sensitivity have stronger activation in the DMPFC. This region is central to both theory-of-mind processing (Schurz et al., 2014) and self-referential processing (Northoff et al., 2006), playing a crucial role in combining others’ thoughts and feelings with one’s own (D’Argembeau et al., 2007; Saxe et al., 2006). Zhu et al., 2019a have identified the DMPFC’s involvement in the experience of both guilt and shame. Our findings here further highlight its role in translating these emotions into compensatory behavior.

We found that the TP’s activity is positively related to individuals’ guilt-driven compensatory sensitivity. This region is considered a core part of the theory-of-mind network (Frith and Frith, 2003). Numerous studies suggest that the activation in this region reflects retrieval of both general conceptual knowledge (Lambon Ralph and Patterson, 2008) and social conceptual knowledge (e.g. social rules; Ross and Olson, 2010; Sugiura et al., 2006; Tsukiura et al., 2010; Zahn et al., 2007). The retrieval of such information likely facilitates understanding others’ thoughts and empathizing with their suffering (Olson et al., 2007; Schurz et al., 2014). Empathy, in turn, has been widely established as a significant driver of altruistic behavior, including compensatory behavior (Ding and Lu, 2016 ; Eisenberg and Miller, 1987). Our findings confirm the involvement of the TP in translating guilt into compensation.

The IPL’s activity has a positive correlation with individuals’ shame-driven compensatory sensitivity. This region is associated with various non-social and social cognitive functions, including number processing (Pinel et al., 2004; Sandrini et al., 2004), salience processing (Kahnt et al., 2014), and theory-of-mind processing (Igelström and Graziano, 2017; Tusche et al., 2016). Two recent studies provided direct evidence showing that the IPL plays a role in encoding others’ benefits during altruistic decision making (Hu et al., 2017; Hu et al., 2021). Our findings about IPL can be explained by its involvement in generating other-regarding motives (Hu et al., 2021) that facilitate the conversion from shame to compensation.

We did not find a significant difference in the correlations between TP activity and guilt-driven versus shame-driven compensatory sensitivities. Similarly, no significant difference was observed in the correlations between IPL activity and shame-driven versus guilt-driven compensatory sensitivities. These findings suggest that neither of these regions plays a domain-specific role in compensation driven by guilt or shame.

In contrast, LPFC activity exhibited a stronger correlation with shame-driven compensatory sensitivity than with guilt-driven compensatory sensitivity, indicating a domain-specific role of the LPFC in shame-related compensation. The LPFC is implicated in cognitive control (Koechlin et al., 2003) and the optimization of social decision-making (Buckholtz and Marois, 2012; Feng et al., 2015). Some brain stimulation studies have demonstrated that disrupting LPFC activity impairs individuals’ ability to inhibit selfish or aggressive impulses, which can incur social devaluation and punishment from others (Knoch et al., 2006; Knoch et al., 2009; Riva et al., 2015). Further research has extended these findings by emphasizing the LPFC’s role in strategic social behavior (Ruff et al., 2013; Strang et al., 2015). For instance, Ruff et al., 2013 found that when individuals face the possibility of being punished for selfish behavior, enhancing LPFC activity suppresses selfish impulses and promotes altruistic behavior. However, in the absence of punishment risk, enhancing LPFC activity instead reduces altruistic behavior and promotes self-interest. Considering that guilt is typically alleviated through altruistic behavior (Tang et al., 2019), whereas coping strategies for shame are more varied—ranging from altruistic behavior to aggression and avoidance (Sznycer, 2019; Sznycer et al., 2016)—shame appears to be more closely linked to strategic thinking than guilt. This explains why LPFC activity, which is associated with strategic behavior, is more strongly related to shame-driven compensatory sensitivity than to guilt-driven compensatory sensitivity.

The neural correlates of the parameter κ largely overlapped with those linked to compensatory sensitivities driven by guilt and shame. Intriguingly, beyond that, κ also showed a strong association with aINS activity. Insula, known as a key node in the salience network (Uddin, 2015), engages in during experiencing various negative emotions, including sadness (Wagner et al., 2011), disgust (Craig, 2009), guilt (Yu et al., 2014), shame (Piretti et al., 2023; Zhu et al., 2019a), and indebtedness (Gao et al., 2024). Social neuroscience research highlights the critical role of the aINS in processing norm violations and guiding behavior accordingly (Bellucci et al., 2018; Zinchenko and Arsalidou, 2018). For example, Chang et al., 2011 found that aINS serves to mitigate anticipated negative feelings triggered by norm violations by facilitating individuals' reciprocity toward their partners' investments in a trust game, thereby maintaining adherence to the norm of reciprocity. Consistently, numerous studies on the ultimatum game reveal aINS’s involvement in rejecting unfair offers and upholding the norm of fairness (Feng et al., 2015; Gabay et al., 2014). In the same line, the findings on the involvement of aINS in social conformity also manifest its role in monitoring norm violations and reinforcing adherence to social norms (Berns et al., 2010; Klucharev et al., 2009). Given the involvement of the aINS in various social-affective processes, our findings suggest that the motivation to uphold social norms might directly shape individuals' compensatory behavior or indirectly influence it through emotions beyond guilt and shame, with aINS activity playing a pivotal role in this process.

Interestingly, we found that the sensorimotor areas were associated with the representation of a shame-related cognitive antecedent (i.e. responsibility) and emotional sensitivity. Our findings align with the result from a fMRI meta-analysis, which identified the involvement of sensorimotor regions in processing shame (Piretti et al., 2023). It has been suggested that sensorimotor activation may reflect typical shame-related action tendencies, such as reduced social presence, speech, and movement (Piretti et al., 2023). However, in our study, participants were required to remain completely still throughout the experiment to maintain MRI data quality and were continuously monitored, eliminating the possibility of physical withdrawal. Therefore, the observed sensorimotor activation may reflect motor preparation for subsequent button-press responses associated with compensation rather than a general tendency toward shame-related avoidance. Future studies that permit participants to engage in actual avoidance behaviors could further clarify the role of sensorimotor areas in shame processing.

In line with previous research (Cohen et al., 2011), our findings reveal that both dimensions of trait guilt were significantly associated with compensatory behavior, whereas neither dimension of trait shame exhibited such an association. Furthermore, we found neural responses in the aMCC mediated the relationship between repair action tendencies (one dimension of trait guilt) and compensation. A substantial body of research has revealed that guilt processing consistently activates the aMCC (see a meta-analysis, Gifuni et al., 2017). It is suggested that, in the context of guilt, the aMCC plays a role in detecting the conflict between social norms and actual behavior and signaling this conflict via generating negative emotions (Bastin et al., 2016; Gifuni et al., 2017). In addition, Yu et al., 2014 linked the aMCC activity with compensatory behavior. Accordingly, our fMRI findings suggest that individuals with stronger tendency to engage in compensation across various moral violation scenarios (indicated by their repair action tendencies) are more sensitive to the severity of the violation and therefore engage in greater compensatory behavior. However, the neural correlates of negative behavior evaluations (another dimension of trait guilt) were absent. The reasons underlying the non-significant neural finding may be multifaceted. One possibility is that negative behavior evaluations influence neural responses indirectly through intermediate processes not captured in our study (e.g. specific motivational states).

Although previous research has found that trait gratitude and SVO are significantly associated with altruistic behavior in contexts such as donation (Van Lange et al., 2007; Yost-Dubrow and Dunham, 2018) and reciprocity (Ma et al., 2017; Yost-Dubrow and Dunham, 2018), their associations with compensatory decisions in the present study were not significant. This suggests that the effects of trait gratitude and SVO on altruistic behavior are context-dependent and may not predict all forms of altruistic behavior.

This research has several limitations. First, post-task assessments of guilt and shame, unlike in-task assessments, rely on memory and may thus be less precise, although in-task assessments could have heightened participants’ awareness of these emotions and biased their decisions. Second, our measures of guilt and shame depend on participants’ conceptual understanding of the two emotions. While this is common practice in studies with adult participants (Michl et al., 2014; Wagner et al., 2011; Zhu et al., 2019a), it may be less appropriate for research involving children. Third, although we aimed to construct a conceptually comprehensive computational model space informed by prior research and our own understanding, it does not encompass all plausible models. Future research is encouraged to explore additional possibilities. Fourth, fMRI cannot establish causality. Future studies using brain stimulation techniques (e.g. transcranial magnetic stimulation) are needed to clarify the causal role of brain regions in guilt-driven and shame-driven altruistic behavior. Fifth, we did not explicitly measure emotions similar to guilt and shame (e.g. indebtedness), which would have been helpful for understanding their distinct contributions. Sixth, marginally significant results should be viewed cautiously and warrant further examination in future studies with larger sample sizes.

Our study makes several innovative contributions. First, we developed a novel paradigm that effectively elicits guilt and shame at comparable intensities, enabling researchers to systematically explore the associations among guilt, shame, their cognitive antecedents, and behavioral consequences. Future research could combine this paradigm with other cognitive neuroscience methods, such as electroencephalography (EEG) or magnetoencephalography (MEG), and adapt it to investigate additional behaviors linked to guilt and shame, including donation (Xu, 2022), avoidance (Shen et al., 2023), and aggression (Velotti et al., 2014). Second, our behavioral findings provide high-quality empirical evidence for functionalist theory, aligning with the contemporary trend of comprehending emotions through their adaptive functions (Landers et al., 2024b; Sznycer et al., 2021). Third, our computational and neural findings offer a clear delineation of the neurocognitive mechanisms underlying guilt and shame. Building on knowledge that harm and responsibility are related to guilt and shame, our results further reveal how these cognitive antecedents are integrated. While previous studies have broadly identified brain regions associated with guilt and shame processing as a whole (Bastin et al., 2016; Gifuni et al., 2017; Piretti et al., 2023), our study advances this understanding by breaking down guilt and shame processing into distinct processes and precisely mapping the neural correlates of each process.

Our study has potential practical implications. The behavioral findings may help counselors understand how cognitive interventions targeting perceptions of harm and responsibility could influence experiences of guilt and shame. The neural findings highlight specific brain regions (e.g. TPJ) as potential intervention targets for regulating these emotions. Given the close links between guilt, shame, and various psychological disorders (e.g. Kim et al., 2011; Lee et al., 2001; Schuster et al., 2021), strategies to regulate these emotions may contribute to symptom alleviation. Nevertheless, because this study was conducted with healthy adults, caution is warranted when considering applications to other populations.

In conclusion, our findings support the functionalist theory by demonstrating distinct effects of harm and responsibility on guilt and shame, as well as differences in the efficiency with which guilt and shame translate into compensatory behaviors. Notably, harm and responsibility are integrated in a manner consistent with responsibility diffusion prior to influencing guilt-driven and shame-driven compensation. Furthermore, the distinct stages involved in guilt and shame processing correspond to activities in specific neural regions related to value computation, salience processing, theory-of-mind processing, self-referential processing, and cognitive control. By simultaneously providing computational, algorithmic, and neural accounts of guilt and shame (Yu et al., 2024), our study advances the holistic understanding of these emotions, which provides insights into how guilt and shame can be regulated and informs the treatment of guilt- and shame-related mental disorders.

The code, behavioral data, and preprocessed fMRI data are public available on the Open Science Framework (https://osf.io/sve7h/files).

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Author details

1. Ruida Zhu

   Department of Psychology, Sun Yat-sen University, Guangzhou, China

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1316-7526

2. Huanqing Wang

   Department of Psychology, The Ohio State University, Columbus, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Chunliang Feng

   Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

4. Linyuan Yin

   Department of Psychology, Sun Yat-sen University, Guangzhou, China

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

5. Ran Zhang

   1. State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
   2. Beijing Key Laboratory of Safe AI and Superalignment, Beijing, China

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

6. Yi Zeng

   1. Beijing Key Laboratory of Safe AI and Superalignment, Beijing, China
   2. Beijing Institute of AI Safety and Governance, Beijing, China
   3. Brain-inspired Cognitive AI Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China
   4. University of Chinese Academy of Sciences, Beijing, China
   5. Long-term AI, Beijing, China

   Contribution

   Writing – review and editing

   Competing interests

   Yi Zeng is affiliated with Long-term AI, China. The author has no other competing interests to declare

7. Chao Liu

   1. State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
   2. Beijing Key Laboratory of Safe AI and Superalignment, Beijing, China

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – original draft, Writing – review and editing

   For correspondence

   liuchao@bnu.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1149-2314

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