The Role of Valence and Meta-awareness in Mirror Self-recognition Using Hierarchical Active Inference

TL;DR

This paper introduces a hierarchical active inference model that integrates affective components to better understand self-recognition, revealing how valence influences mirror test behavior in agents.

Contribution

It presents the first computational model incorporating affect into self-recognition, demonstrating affective influences on mirror self-recognition behavior.

Findings

Negative valence occurs during self-recognition when unexpected internal states are learned.

Agents with strong negative expectations avoid mirrors to prevent undesired learning.

Model aligns with human self-recognition behaviors and emotional responses.

Abstract

The underlying processes that enable self-perception are crucial for understanding multisensory integration, body perception and action, and the development of the self. Previous computational models have overlooked an essential aspect: affective or emotional components cannot be uncoupled from the self-recognition process. Hence, here we propose a computational approach to study self-recognition that incorporates affect using state-of-the-art hierarchical active inference. We evaluated our model in a synthetic experiment inspired by the mirror self-recognition test, a benchmark for evaluating self-recognition in animals and humans alike. Results show that i) negative valence arises when the agent recognizes itself and learns something unexpected about its internal states. Furthermore, ii) the agent in the presence of strong prior expectations of a negative affective state will avoid the mirror altogether in anticipation of an undesired learning process. Both results are in line with current literature on human self-recognition.