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 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. (shrink)

The target article sketched and explored a mechanism (action-oriented predictive processing) most plausibly associated with core forms of cortical processing. In assessing the attractions and pitfalls of the proposal we should keep that element distinct from larger, though interlocking, issues concerning the nature of adaptive organization in general.

The goal of perceptual systems is to allow organisms to adaptively respond to ecologically relevant stimuli. Because all perceptual inputs are ambiguous, perception needs to rely on prior knowledge accumulated over evolutionary and developmental time to turn sensory energy into information useful for guiding behavior. It remains controversial whether the guidance of perception extends to cognitive states or is locked up in a “cognitively impenetrable” part of perception. I argue that expectations, knowledge, and task demands can shape perception at multiple (...) levels, leaving no part untouched. The position advocated here is broadly consistent with the notion that perceptual systems strive to minimize prediction error en route to globally optimal solutions :181–204, 2013). On this view, penetrability should be expected whenever constraining lower-level processes by higher level knowledge is minimizes global prediction error. Just as Fodor feared cognitive penetration of perception threatens theory-neutral observation and the distinction between observation and inference. However, because theories themselves are constrained by the task of minimizing prediction error, theory-laden observation turns out to be superior to theory-free observation in turning sensory energy into useful information. (shrink)