Bayesian inference

Bayesian inference is a function of Bayesian probability. Bayesian probability is a measure of the likelihood of a desired outcome (H) (Colts winning the playoffs for instance) based on the conditional probabilities computed for a set of event-sequences (D) that would lead to the desired outcome ..adjusted for (divided-by) the conditional probabilities computed for the set of event-sequences leading to other possible outcomes (Hi ) (Dallas Giants or Eagles winning the playoffs).

Bayesian inference may be native to the way people make judgments. At the level of sensory processing, studies show that the nervous system perpetually distinguishes the most relevant signals, from incidental/peripheral signals, using likelihood estimates of a Bayesian sort. Signals that are the most likely outcome of ongoing activity, based on the contents of working memory, are given a boost. Signals considered less likely are held in abeyance and immediately suppressed if subsequent events do nothing to rehabilitate them.

In Bayesian terms, where H is the candidate signal and D is the current state of sensory memory, then the probability that H will be the winning candidate or P(D|H) ..is a function of sensory memory (D) mitigated by P(D|Hi) ..or the probability that the contents of sensory memory might favor other winning candidates (Hi ).