Compassion As an Intervention to Attune to Universal Suffering of Self and Others in Conflicts: A Translational Framework.

Compassion meditation, rooted in Buddhist philosophy, may help protect mental health amid intensifying conflicts. The practice involves attuning equally to friend, enemy, and neutral person, aiming to suspend identity-based conceptual thoughts and ego-preserving biases that obscure reality. A Bayesian active inference framework models the person as a Bayesian Engine that constructs phenomena from aggregates (forms, sensations, discriminations, actions, consciousness). Rigid identity-grasping beliefs cause the engine to malfunction by blocking updates from prediction errors. The proposed brain model has three components: Relation-Modeling (Default-Mode Network), Reality-Checking (Frontoparietal and Ventral Attention Networks), and Conflict-Alarming (Salience Network). Compassion meditation may strengthen brain regions that suspend prior beliefs and enhance attunement to others.

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Abstract

As interpersonal, racial, social, and international conflicts intensify in the world, it is important to safeguard the mental health of individuals affected by them. According to a Buddhist notion "if you want others to be happy, practice compassion; if you want to be happy, practice compassion," compassion practice is an intervention to cultivate conflict-proof well-being. Here, compassion practice refers to a form of concentrated meditation wherein a practitioner attunes to friend, enemy, and someone in between, thinking, "I'm going to help them (equally)." The compassion meditation is based on Buddhist philosophy that mental suffering is rooted in conceptual thoughts that give rise to generic mental images of self and others and subsequent biases to preserve one's egoism, blocking the ultimate nature of mind. To contextualize compassion meditation scientifically, we adopted a Bayesian active inference framework to incorporate relevant Buddhist concepts, including mind (buddhi), compassion (karuna), aggregates (skandhas), suffering (duhkha), reification (samaropa), conceptual thoughts (vikalpa), and superimposition (prapañca). In this framework, a person is considered a Bayesian Engine that actively constructs phenomena based on the aggregates of forms, sensations, discriminations, actions, and consciousness. When the person embodies rigid beliefs about self and others' identities (identity-grasping beliefs) and the resulting ego-preserving bias, the person's Bayesian Engine malfunctions, failing to use prediction errors to update prior beliefs. To counter this problem, after recognizing the causes of sufferings, a practitioner of the compassion meditation aims to attune to all others equally, friends and enemies alike, suspend identity-based conceptual thoughts, and eventually let go of any identity-grasping belief and ego-preserving bias that obscure reality. We present a brain model for the Bayesian Engine of three components: (a) Relation-Modeling, (b) Reality-Checking, and (c) Conflict-Alarming, which are subserved by (a) the Default-Mode Network (DMN), (b) Frontoparietal Network (FPN) and Ventral Attention Network (VAN), and (c) Salience Network (SN), respectively. Upon perceiving conflicts, the strengthening or weakening of ego-preserving bias will critically depend on whether the SN up-regulates the DMN or FPN/VAN, respectively. We propose that compassion meditation can strengthen brain regions that are conducive for suspending prior beliefs and enhancing the attunements to the counterparts in conflicts.