Predictive processing

• Build model of the world, H, based on sensory input • Evaluate H by taking action, thereby getting a new O, using that to update the probability of H • Keep taking action to minimise the error on H to build confidence in H • If new O indicates that H’ is more probable, abandon H, and evaluate H’ by taking action

P(H│O)=(P(O│H)·P(H))/(P(O))

What we perceive (bottom up)

What we expect to perceive

An outer stimulus (that often was uninspected) that goes from the senses to the brain

The brain expects something

There are different layers which become more and more abstract and when we perceive something it starts with the least abstract one (our senses)

P(O|H) The probability of observing something given the hypothesis

We can only perceive one thing at a time, so when we see two overlapping images with a face and a house we can only perceive one of them at a time (you can only focus on one thing and the other things will be “blinded out”) Rivalry is characterized by this very dramatic change in actual visual consciousness

We always update our priors and beliefs

No longer symbols

Bottom up processing. When we perceive something unexpected

“a […] substantial view based on the rather uncontroversial idea that the brain is involved in information processing, and that information theory is cast in terms of the probability theory from which Bayes’ rule is derived” The brain is only concerned with minimizing prediction error

How it should be and not how it is

Reveal that we update our prior probability/hypothesis. In 1928 Emilio Diaz-Caneja (Diaz-Caneja 1928) discovered that if the two images are cut in half and combined such that one eye sees, for example, half a house and half a face, and the other eye sees the other halves of the house and the face, then there is not rivalry between what is presented to each eye, there is instead rivalry between the full, uncut images of the face and the house

We sample sensory input in the light of the model we hold about the world. We should expect that the brain minimizes prediction error by changing its position in the world and by changing the states of the world, both of which will change its sensory input Action in this sense of testing perceptual models is therefore a moment of prediction error minimization

Is done following Bayes’ rule to infer causes of sensory input. Minimization of the variation of the expected prediction error. A matter of selecting and adjusting hypotheses about the world in response to the prediction error they engender

Interpretations that come from experience

An approach that concentrates on the study of consciousness and the objects of direct experience.

Inappropriately imputing human-like properties to the brain and thereby confusing personal level explanations with subpersonal level explanations

Mental states are defined by a functional role that specifies a certain kind of input-output profile, given a certain internal state

The process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system

Characterized by or involving conclusions reached on the basis of evidence and reasoning.

Representations that do not change