Abstract
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 language | English |
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Pages (from-to) | 85-97 |
Number of pages | 13 |
Journal | Neuroscience and biobehavioral reviews |
Volume | 41 |
Early online date | 15 Feb 2013 |
DOIs | |
Publication status | Published - 1 Apr 2014 |
Bibliographical note
Copyright © 2013 Elsevier Ltd. All rights reserved.Keywords
- Animals
- Bayes Theorem
- Brain/physiology
- Humans
- Illusions/physiology
- Models, Neurological
- Perception/physiology
- Recognition, Psychology/physiology
- Self Concept
- Self-recognition
- Self-awareness
- Voice recognition
- Face recognition
- Body ownership
- Bayesian
- Free energy
- Predictive coding
- Prediction error
- Rubber hand illusion
- Enfacement