Understanding of the brain and the principles governing neural processing requires theories
that are parsimonious, can account for a diverse set of phenomena, and can make testable …

At any given moment, our brain processes multiple inputs from its different sensory
modalities (vision, hearing, touch, etc.). In deciphering this array of sensory information, the …

Whereas probabilistic models describe the dependence structure between observed
variables, causal models go one step further: They predict, for example, how cognitive …

Perception is about making sense, that is, understanding what events in the outside world
caused the sensory observations. Consistent with this intuition, many aspects of human …

Abstract Griffiths, Chater, Norris, and Pouget (2012) argue that we have misunderstood the
Bayesian approach. In their view, it is rarely the case that researchers are making claims that …

Until recently, the question of how the brain performs causal inference has been studied
primarily in the context of cognitive reasoning. However, this problem is at least equally …

Statistical regularities in the environment create prior beliefs that we rely on to optimize our
behavior when sensory information is uncertain. Bayesian theory formalizes how prior …

The complexity of the cognitive system makes powerful metaphors such as the probabilistic
mind and the Bayesian brain appealing on the one hand, but limited on the other. The trick is …

A Bayesian approach can contribute to an understanding of the brain on multiple levels, by
giving normative predictions about how an ideal sensory system should combine prior …

According to a growing trend in theoretical neuroscience, the human perceptual system is
akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that …