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The underlying processes that enable self-perception are crucial forunderstanding multisensory integration, body perception and action, and thedevelopment of the self. Previous computational models have overlooked anessential aspect: affective or emotional components cannot be uncoupled fromthe self-recognition process. Hence, here we propose a computational approachto study self-recognition that incorporates affect using state-of-the-arthierarchical active inference. We evaluated our model in a synthetic experimentinspired by the mirror self-recognition test, a benchmark for evaluatingself-recognition in animals and humans alike. Results show that i) negativevalence arises when the agent recognizes itself and learns something unexpectedabout its internal states. Furthermore, ii) the agent in the presence of strongprior expectations of a negative affective state will avoid the mirroraltogether in anticipation of an undesired learning process. Both results arein line with current literature on human self-recognition.

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