Prediction error minimization
Jakob Hohwy · Oxford University Press eBooks · 2013
The central mechanism for hierarchical perceptual inference is prediction on the basis of internal, generative models, revision of model parameters, and minimization of prediction error. This is the way in which the brain engages in perceptual inference, as described in the previous chapter. This chapter describes this idea in detail. It uses a statistical analogy of model fitting to explain the notion of prediction error and then gradually builds up more and more complex versions of the theory, ending with broad ideas from information theory and statistical physics concerning mutual information, free energy and surprisal. The overall picture is of a self-supervised system that is closely supervised by the sensory signal it receives from the world, but which is hidden behind the veil of sensory input. This is a profound reversal of the way we normally think about the top-down and bottom-up signals in the brain. The system is able to recognize the causes of its sensory input in a mechanistic manner, by implicitly inverting its generative model.