Distinct neural computations scale the violation of expected reward and emotion in social transgressions

Abstract

Traditional decision-making models conceptualize humans as adaptive learners utilizing the differences between expected and actual rewards (prediction errors, PEs) to maximize outcomes, but rarely consider the influence of violations of emotional expectations (emotional PEs) and how it differs from reward PEs. Here, we conducted a fMRI experiment (n = 43) using a modified Ultimatum Game to examine how reward and emotional PEs affect punishment decisions in terms of rejecting unfair offers. Our results revealed that reward relative to emotional PEs exerted a stronger prediction to punishment decisions. On the neural level, the left dorsomedial prefrontal cortex (dmPFC) was strongly activated during reward receipt whereas the emotions engaged the bilateral anterior insula. Reward and emotional PEs were also encoded differently in brain-wide multivariate patterns, with a more sensitive neural signature observed within fronto-insular circuits for reward PE. We further identified a fronto-insular network encompassing the left anterior cingulate cortex, bilateral insula, left dmPFC and inferior frontal gyrus that encoded punishment decisions. In addition, a stronger fronto-insular pattern expression under reward PE predicted more punishment decisions. These findings underscore that reward and emotional violations interact to shape decisions in complex social interactions, while the underlying neurofunctional PEs computations are distinguishable.

Fig. 1. Experimental protocol, task design, and main goals and corresponding analytic workflow.

a Experimental timeline. The questionnaire A and B include positive and negative affect scale, and state-trait anxiety inventory. b The modified Ultimatum Game task and PEs computation. Before each offer individuals predict how much money they anticipate to receive from the proposers, and how they would feel in terms of valence and arousal when receiving the anticipated offer. After receiving the actual offers, individuals report their current actual emotional experience in terms of arousal and valence and finally decide either to accept or to reject the offer. Main behavioral outcome where the computed reward and emotional PEs in terms of establishing trial-by-trial PEs: a reward PE (color coded in blue), an arousal PE (color coded in red) and a valence PE (color coded in yellow) scaling the difference between subjects’ prediction about the reward or emotion and their actual experience. c Major goals and analytic workflow of the current study. On the behavioral level, the predictive contribution of the reward and emotional PE to the decision to punish the proposer (i.e. rejecting the offers) is determined, modeling of the simultaneously acquired fMRI data aim at: i) determining the univariate activation profiles of reward and emotional experience during the prediction and experience period to find separable neural underpinnings of reward and emotional, ii) training distinct multivariate neural patterns of emotional and reward PEs, as well as of punishment decisions to further indicate which neural pattern of PEs is specific to that of punishment decisions, iii) finally examining the links between the multivariate neural patterns for punishment decisions and all PEs to elucidate the neural pathway underlying the effects of reward and emotional PEs in social choices. Image in a–c were obtained from Flaticon.com under the free license with attribution.

Fig. 2. Predictive role of the different prediction errors for the decision to punish a social norm-violating proposer.

a All PEs showed a significant predictive effect on punishment decisions such that participants showed higher punishment rates when they experienced less reward or lower valence but higher arousal than anticipated. b There was no significant gender difference on the reliance of all PEs to make punishment decisions. The lines reflect the probability of different choice pairs including rejecting versus accepting in the Ultimatum Game task, and the negative values represent negative PEs, suggesting less reward, less pleasantness, as well as less arousal than expected. Shaded areas reflect ± 1*standard errors. ***p < 0.001.

Fig. 3. Brain regions processing the reward experience and emotional response to monetary offers, respectively.

a The left dmPFC was strongly engaged during reward experiences, while emotion processing regions such as the bilateral aINS showed increased activation for evaluating the intensity and valence of emotions upon receiving the actual split of the monetary offer (b). For the purpose of illustrating the specific activation patterns for the reward experiences and the corresponding emotion evaluations, the parameter estimates extracted from spherical (radius: 8 mm) regions of interest in the identified dmPFC and aINS regions were presented with box and density plots. The error bars reflect minimum and maximum value of the data, while the thick lines inside the box represent median value of the data. The red points represent outliers. The shaded areas indicate the distribution of regional extracted parameters for reward experience and emotional response to monetary offers.

Fig. 4. Multivariate neural expressions of reward and emotion PEs and their distinction.

a Whole brain multivoxel pattern for differentiating punishment and accept reward PE included the right pINS, right vlPFC and left ACC as key contributors to the. The violin and box plots show the distributions of multivariate neural pattern map response to classify the reward PE separated by punishment and accept decisions, reflecting that the reward PE signature exhibited a stronger responses for the punishment decisions compared to accept decisions, t₍₄₂₎ = 2.16, p = 0.04, two-tailed, paired t-test. The box is bounded by the first and third quartiles, and the whiskers stretched to the greatest and lowest values within the median ± 1.5 interquartile range, while each colored line between dots represents each participant’s paired data (red line: correct classification; blue line, incorrect classification). The forced-choice classification accuracy was 0.72, p < 0.0001, two-tailed, binomial test. b Bootstrapping test results for SVM classifier weight correlations. The short red lines reflect 95% confidence intervals obtained from bootstrap tests (500 samples). No regions showed significant correlations between SVM classifier weights. (c) Group-level correlations between activation of contrast images for punishment and accept-decision separated reward or emotional PEs. No regions showed significant average correlations between activations of contrast values across participants. The error bars reflect minimum and maximum value of the data, while the thick lines inside the box represent median value of the data. The red points represent outliers. ACC accuracy, AUC area under curve, Spec specificity, Sens sensitivity, *p < 0.05.

Fig. 5. Univariate activations and multivariate expression patterns for punishment decisions.

a Univariate activation for the difference between punishment and accept decisions showed that the bilateral ACC, left dmPFC and aINS were activated strongly for punishment choices (reject the unfair offer). b Whole-brain multivariate neural expression classifying the punishment and accept decisions suggested that regions such as the left dmPFC, aINS and inferior frontal gyrus made stable prediction to punishment decisions. The violin and box plots show the distributions of responses of the multivariate neural pattern in the classification of punishment and accept decisions, indicating a higher level of reactivity to the punishment compared to accept decisions, t₍₄₂₎ = 10.85, p < 0.001, two-tailed, paired t-test. The box is bounded by the first and third quartiles, and the whiskers stretched to the greatest and lowest values within the median ± 1.5 interquartile range, while each colored line between dots represents each participant’s paired data (red line: correct classification; blue line, incorrect classification). The forced-choice classification accuracy was 0.78, p < 0.001, two tailed, binomial test. ACC accuracy, AUC area under curve, Spec specificity, Sens sensitivity, ***p < 0.001.

Fig. 6. Exploratory correlation between neural signatures of PEs and punishment decisions.

a The scatter plots reflect that stronger pattern expression within frontal-insular network representing punishment reward PE was significantly correlated with increased number of punishment choices across participants, while this association was diminished for emotional PEs (b). The histograms show correlation coefficients from permutation tests, whereas the dashed lines represent the true correlation.