Bayesian inferences about the self (and others): A review

Highlights

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  People may use Bayesian inference to update their own self-representation.

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  Self- and other-representations may help predict outcomes of social interactions.

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  The value of an outcome is essentially the prior belief that it can be achieved.

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  ‘Active inference’ uses free-energy-minimization to achieve desirable outcomes.

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  A positive self-representation may be a desirable outcome of active inference.

Abstract

Viewing the brain as an organ of approximate Bayesian inference can help us understand how it represents the self. We suggest that inferred representations of the self have a normative function: to predict and optimise the likely outcomes of social interactions. Technically, we cast this predict-and-optimise as maximising the chance of favourable outcomes through active inference. Here the utility of outcomes can be conceptualised as prior beliefs about final states. Actions based on interpersonal representations can therefore be understood as minimising surprise – under the prior belief that one will end up in states with high utility. Interpersonal representations thus serve to render interactions more predictable, while the affective valence of interpersonal inference renders self-perception evaluative. Distortions of self-representation contribute to major psychiatric disorders such as depression, personality disorder and paranoia. The approach we review may therefore operationalise the study of interpersonal representations in pathological states.