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)

Some of the features of animal and human categorical perception (CP) for color, pitch and speech are exhibited by neural net simulations of CP with one-dimensional inputs: When a backprop net is trained to discriminate and then categorize a set of stimuli, the second task is accomplished by "warping" the similarity space (compressing within-category distances and expanding between-category distances). This natural side-effect also occurs in humans and animals. Such CP categories, consisting of named, bounded regions of similarity space, may be (...) the ground level out of which higher-order categories are constructed; nets are one possible candidate for the mechanism that learns the sensorimotor invariants that connect arbitrary names (elementary symbols?) to the nonarbitrary shapes of objects. This paper examines how and why such compression/expansion effects occur in neural nets. (shrink)

Can what we know change what we see? Does language affect cognition and perception? The last few years have seen increased attention to these seemingly disparate questions, but with little theoretical advance. We argue that substantial clarity can be gained by considering these questions through the lens of predictive processing, a framework in which mental representations—from the perceptual to the cognitive—reflect an interplay between downward-flowing predictions and upward-flowing sensory signals. This framework provides a parsimonious account of how what we know (...) ought to change what we see and helps us understand how a putatively high-level trait such as language can impact putatively low-level processes such as perception. Within this framework, language begins to take on a surprisingly central role in cognition by providing a uniquely focused and flexible means of constructing predictions against which sensory signals can be evaluated. Predictive processing thus provides a plausible mechanism for many of the reported effects of language on perception, thought, and action, and new insights on how and when speakers of different languages construct the same “reality” in alternate ways. (shrink)

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)