# Brain mechanisms underlying self-other distinction for bodily self-recognition

## TL;DR

The paper explores how the brain distinguishes between self and others through sensorimotor signals and proposes a model for self-recognition.

## Contribution

A novel self-other inference model is proposed, integrating visual, somatosensory, and motor signals via Bayesian causal inference.

## Key findings

- Pre-reflective bodily self-recognition arises from spatiotemporally integrated sensorimotor signals.
- A self-other inference model successfully captures latent state representations from synthetic neural activity.
- The model suggests distinct brain areas process cues for self-other distinction.

## Abstract

Accumulating evidence indicates that single neurons in the primate brain specifically encode sensorimotor experience about the self or others. Although the self-other distinction has been a major focus of social neuroscience research, little is known about the underlying mechanisms that enable the recognition of bodily self. Here, we review the literature demonstrating that pre-reflective bodily self-recognition can be achieved through the spatiotemporally contingent integration of visual, somatosensory, and motor signals arising from sensorimotor experience. We propose a self-other inference model as a neural computation for self-other distinction, in which the likelihood of being oneself is updated constantly based on Bayesian causal inference using appearance, contingency, and perspective cues. The results of simulation incorporating a state-space point-process model revealed that our self-other inference model successfully captures the latent state representation about the self-other distinction from synthetic neural activity. We hypothesize that the self-other inference model is implemented by distinct brain areas that process individual cues and their integrative hubs. This hypothesis is experimentally testable using cutting-edge technologies such as area-specific or pathway-selective silencing.