TL;DR
This paper presents a computational model using active inference and a self-prior to simulate mirror self-recognition behavior, demonstrating spontaneous self-awareness without external rewards.
Contribution
It introduces a novel Transformer-based self-prior mechanism that explains mirror self-recognition as emerging from multisensory experience learning, without explicit training.
Findings
Simulated infant recognized and removed a face sticker in 70% of cases.
Self-prior acts as an internal criterion for self versus non-self.
Model captures visual-proprioceptive associations as a probabilistic body schema.
Abstract
The mirror self-recognition test evaluates whether a subject touches a mark on its own body that is visible only in a mirror, and is widely used as an indicator of self-awareness. In this study, we present a computational model in which this behavior emerges spontaneously through a single mechanism, the self-prior, without any external reward. The self-prior, implemented with a Transformer, learns the density of familiar multisensory experiences; when a novel mark appears, the discrepancy from this learned distribution drives mark-directed behavior through active inference. A simulated infant, relying solely on vision and proprioception without tactile input, discovered a sticker placed on its own face in the mirror and removed it in approximately 70% of cases without any explicit instruction. Expected free energy decreased significantly after sticker removal, confirming that the…
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