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Recognising and representing one's self as distinct from others is a fundamental component of self-awareness. However, current theories of self-recognition are not embedded within global theories of cortical function and therefore fail to provide a compelling explanation of how the self is processed. We present a theoretical account of the neural and computational basis of self-recognition that is embedded within the free-energy account of cortical function. In this account one's body is processed in a Bayesian manner as the most likely to be "me". Such probabilistic representation arises through the integration of information from hierarchically organised unimodal systems in higher-level multimodal areas. This information takes the form of bottom-up "surprise" signals from unimodal sensory systems that are explained away by top-down processes that minimise the level of surprise across the brain. We present evidence that this theoretical perspective may account for the findings of psychological and neuroimaging investigations into self-recognition and particularly evidence that representations of the self are malleable, rather than fixed as previous accounts of self-recognition might suggest.

Original publication




Journal article


Neurosci Biobehav Rev

Publication Date





85 - 97


Bayesian, Body ownership, Enfacement, Face recognition, Free energy, Prediction error, Predictive coding, Rubber hand illusion, Self-awareness, Self-recognition, Voice recognition, Animals, Bayes Theorem, Brain, Humans, Illusions, Models, Neurological, Perception, Recognition (Psychology), Self Concept