The somatic marker hypothesis proposes that the cortical representation of visceral signals is a crucial component of emotional processing. No previous study has investigated the information flow among brain regions that process visceral information during emotional perception. In this magnetoencephalography study of 32 healthy subjects of either sex, heartbeat-evoked responses (HERs), which reflect the cortical processing of heartbeats, were modulated by the perception of a sad face. The modulation effect was localized to the prefrontal cortices, the globus pallidus, and an interoceptive network including the right anterior insula (RAI) and dorsal anterior cingulate cortex (RdACC). Importantly, our Granger causality analysis provides the first evidence for the increased flow of heartbeat information from the RAI to the RdACC during sad face perception. Moreover, using a surrogate R-peak analysis, we have shown that this HER modulation effect was time-locked to heartbeats. These findings advance the understanding of brain-body interactions during emotional processing.

In the resting state, heartbeats evoke cortical responses called heartbeat-evoked responses (HERs), which reflect cortical cardiac interoceptive processing. While previous studies have reported that the heartbeat evokes cortical responses at a regional level, whether the heartbeat induces synchronization between regions to form a network structure remains unknown. Using resting-state MEG data from 85 human subjects of both genders, we first showed that heartbeat increases the phase synchronization between cortical regions in the theta frequency but not in other frequency bands. This increase in synchronization between cortical regions formed a network structure called the heartbeat-induced network (HIN), which did not reflect artificial phase synchronization. In the HIN, the left inferior temporal gyrus and parahippocampal gyrus played a central role as hubs. Furthermore, the HIN was modularized, containing five subnetworks called modules. In particular, module 1 played a central role in between-module interactions in the HIN. Furthermore, synchronization within module 1 had a positive association with the mood of an individual. In this study, we show the existence of the HIN and its network properties, advancing the current understanding of cortical heartbeat processing and its relationship with mood, which was previously confined to region level.

To learn through feedback, feedback should be reliable. However, if feedback is blurred by irrelevant social information, learning in a volatile environment, which requires fast learning and adaptation, might be disturbed. In this study, we investigated how feedback with social noise interferes with learning in a volatile environment by designing a probabilistic associative learning task in which the association probability changes dynamically, and the outcome was randomly blurred by an emotional face with incongruent valence. Learning in this situation was modelled by HGF-S such that emotionally incongruent feedback induces perceptual uncertainty called social noise. The Bayesian model comparison showed that the HGF-S model explains the subjects’ behaviour well, and the simulation showed that social noise interrupts both learning the association probability and the volatility. Furthermore, the learning interruption influenced the subsequent decision. Finally, we found that the individual difference in how the same emotionally incongruent feedback induces social noise in varying degrees was related to the differences in event-related desynchronization induced by happy and sad faces in the right anterior insula, which encodes the degree of emotional feeling. These results advance our understanding of how feedback with emotional interference affects learning.

In many decision-making situations, suboptimal choices are increased by uncertainty. However, when wrong choices could lead to social punishment, such as blame, people might try to improve their performance by minimizing suboptimal choices which could be achieved by increasing the subjective cost of errors, thereby globally reducing decision noise or reducing an uncertainty-induced component of decision noise. In this functional MRI study, 46 participants performed a choice task in which the probability of a correct choice with a given cue and the conditional probability of blame feedback (by making an incorrect choice) changed continuously. By comparing computational models of behaviour, we found that participants optimized their performance by preferentially reducing a component of decision noise associated with uncertainty. Simultaneously, expecting blame significantly deteriorated participants’ mood. Model-based fMRI analyses and dynamic causal modeling indicates that the optimization mechanism based on the expectation of being blamed would be controlled by a neural circuit centered on the right medial prefrontal cortex. These results show novel behavioural and neural mechanisms regarding how humans optimize uncertain decisions under the expectation of being blamed that negatively influences mood.

Controllability perception significantly influences motivated behavior and emotion and requires an estimation of one’s influence on an environment. Previous studies have shown that an agent can infer controllability by observing contingency between one’s own action and outcome if there are no other outcome-relevant agents in an environment. However, if there are multiple agents who can influence the outcome, estimation of one’s genuine controllability requires exclusion of other agents’ possible influence. Here, we first investigated a computational and neural mechanism of controllability inference in a multi-agent setting. Our novel multi-agent Bayesian controllability inference model showed that other people’s action-outcome contingency information is integrated with one’s own action-outcome contingency to infer controllability, which can be explained as a Bayesian inference. Model-based functional MRI analyses showed that multi-agent Bayesian controllability inference recruits the temporoparietal junction (TPJ) and striatum. Then, this inferred controllability information was leveraged to increase motivated behavior in the vmPFC. These results generalize the previously known role of the striatum and vmPFC in single-agent controllability to multi-agent controllability, and this generalized role requires the TPJ in addition to the striatum of single-agent controllability to integrate both self- and other-related information. Finally, we identified an innate positive bias toward the self during the multi-agent controllability inference, which facilitated behavioral adaptation under volatile controllability. Furthermore, low positive bias and high negative bias were associated with increased daily feelings of guilt. Our results provide a mechanism of how our sense of controllability fluctuates due to other people in our lives, which might be related to social learned helplessness and depression.