  The idea that the brain is a probabilistic (Bayesian) inference machine,
continuously trying to figure out the hidden causes of its inputs, has become
very influential in cognitive (neuro)science over recent decades. Here I
present a relatively straightforward generalization of this idea: the primary
computational task that the brain is faced with is to track the probabilistic
structure of observations themselves, without recourse to hidden states. Taking
this starting point seriously turns out to have considerable explanatory power,
and several key ideas are developed from it: (1) past experience, encoded in
prior expectations, has an influence over the future that is analogous to
regularization as known from machine learning; (2) action generation
(interpreted as constraint satisfaction) is a special case of such
regularization; (3) the concept of attractors in dynamical systems provides a
useful lens through which prior expectations, regularization, and action
induction can be viewed; these thus appear as different perspectives on the
same phenomenon; (4) the phylogenetically ancient imperative of acting to
ensure and thereby observe conditions beneficial for survival is likely the
same as that which underlies perceptual inference. The Bayesian brain
hypothesis has been touted as promising to deliver a "unified science of mind
and action". In this paper, I sketch an informal step towards fulfilling that
promise, while avoiding some pitfalls that other such attempts have fallen prey
to.
