Toward a Brain-Based Bio-Marker of Guilt

Abstract

Guilt is a quintessential emotion in interpersonal interactions and moral cognition. Detecting the presence and measuring the intensity of guilt-related neurocognitive processes is crucial to understanding the mechanisms of social and moral phenomena. Existing neuroscience research on guilt has been focused on the neural correlates of guilt states induced by various types of stimuli. While valuable in their own right, these studies have not provided a sensitive and specific bio-marker of guilt suitable for use as an indicator of guilt-related neurocognitive processes in novel experimental settings. In a recent study, we identified a distributed Guilt-Related Brain Signature (GRBS) based on 2 independent functional MRI datasets. We demonstrated the sensitivity of GRBS in detecting a critical cognitive antecedent of guilt, namely one’s responsibility in causing harm to another person, across participant populations from 2 distinct cultures (ie, Chinese and Swiss). We also showed that the sensitivity of GRBS did not generalize to other types of negative affective states (eg, physical and vicarious pain). In this commentary, we discuss the relevance of guilt in the broader scope of social and moral phenomena, and discuss how guilt-related biomarkers can be useful in understanding their psychological and neurocognitive mechanisms underlying these phenomena.

Modulating Subjective Pain Perception with Decoded MNI-space Neurofeedback

“…For instance, multiple "off-the-shelf" decoders have previously been trained to predict subjective and physiological outcomes including fear [25,26], negative emotions [16], guilt [27], empathy [28], threat conditioning [29], skin conductance reactivity and autonomic responses [25,30].…”

“…While real-time decoding of whole-brain activity is not new in the native space [23,24], facilitating real-time decoding in the MNI-space might potentially open up a range of new interesting applications. For instance, multiple “off-the-shelf” decoders have previously been trained to predict subjective and physiological outcomes including fear [25,26], negative emotions [16], guilt [27], empathy [28], threat conditioning [29], skin conductance reactivity and autonomic responses [25,30]. Conducting MNI space neurofeedback could potentially help validate such decoders by directly studying the link between the decoders’ expression and their predicted outcome.…”