ベイズ脳(Bayesian brain)

The Bayesian brain considers the brain as a statistical organ of hierarchical inference that predicts current and future events on the basis of past experience. According to this theory, the mind makes sense of the world by assigning probabilities to hypotheses that best explain (usually sparse and ambiguous) sensory data – and continually updating these hypotheses according to standard probabilistic rules of inference.

This fine-tuning (optimization) of perception and action operates under the single imperative of minimizing surprise (free energy) and uncertainty; thereby maximizing statistical and thermodynamic efficiency.

Learning in the Bayesian brain differs from reinforcement (and machine) learning because it occurs with understanding. Mental models of past experience use these experiences to anticipate new experiences, as opposed to being shaped by them. Continual optimisation of the models also enables efficient exchange with the environment in a self-organised, self-evidencing and unsupervised fashion.

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