Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this “hierarchical prediction machine” approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.

Key words: action, attention, bayesian brain, expectation, generative model, hierarchy, perception, precision, predictive coding, prediction, prediction error, top-down processing.

Bindra’s proposal is still an attempt at “minor revision” that does not get away from the traditional tenet that living organisms are passive receivers of stimuli to which they react according to pseudo-mechanical principles A detailed analysis of what is covered by the term “gnostic organization” would, I believe, reveal the indispensability of induction and of inductively built-up representations of desirable and undesirable states and events that can then serve as goals in the assembly of adaptively modifiable behaviors

Key words: cognition, cybernetics, feedback

Key words: cognition