Whatever next? Predictive brains, situated agents, and the future of cognitive science

Abstract

A exceptionally large number of excellent commentary proposals inspired a special research topic for further discussion of this target article's subject matter, edited by Axel Cleeremans and Shimon Edelman in Abstract: 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.

Slower reacquisition after partial extinction in human contingency learning.

“…Keywords: Extinction, contingency learning, relapse, aversive learning Slower reacquisition after partial extinction 2 Learning relationships between different events relevant to organisms is ubiquitous, and takes place almost constantly (Clark, 2013;Friston, 2003). Most of the time, this acquired knowledge is helpful, as it is the basis of useful predictions about future events or inferences about their relationships.…”

“…This effect was found after partial extinction (i.e., reinforced trials were occasionally experienced during extinction, Experiment 1) and progressive extinction treatments (Experiment 3), and it was not only due to differences in uncertainty levels between the partial and a standard extinction group (Experiment 2). The theoretical explanation of these results, the potential uses of this strategy in applied situations, and its current limitations are discussed.Keywords: Extinction, contingency learning, relapse, aversive learning Slower reacquisition after partial extinction 2 Learning relationships between different events relevant to organisms is ubiquitous, and takes place almost constantly (Clark, 2013;Friston, 2003). Most of the time, this acquired knowledge is helpful, as it is the basis of useful predictions about future events or inferences about their relationships.…”

Representing composed meanings through temporal binding

“…For example, participants are more likely to see a picture that is masked using continuous flash suppression (a form of binocular rivalry) when they can hear its name (Lupyan & Ward, 2013); since continuous flash suppression impedes access to the semantics of a masked image (Moors, Boelens, van Overwalle, & Wagemans, 2016), this result suggests that linguistic meanings are translated into low level visual features (see also Ostarek & Huettig, in press), a finding that aligns with predictive coding accounts of cognition, in which high-level knowledge is constantly used to generate lower-level predictions about the world, to facilitate perception and interaction (A. Clark, 2013). Experiment 1's results thus indicate that compositional processes are quite powerful in how they quickly facilitate interactions with the world.…”

Noise, uncertainty, and interest: Predictive coding and cognitive penetration

“…And Clark (2013) traces the idea of giving a central role for error signals in all brain processes to the influential cybernetician W. Ross Ashby, who claimed that "the whole function of the brain is summed up in: error correction" (Ashby 1947). Clark interprets this provocative claim with a provocative claim of his own: "brains are essentially prediction machines" (Clark 2013). …”

“…For example, Andy Clark has suggested that on the PCF, standard distinctions between perceptual processes and cognitive processes may dissolve (Clark 2013). Plausibly, cognitive penetration of perception presupposes a distinction between perceptual and cognitive processes, even if there is vagueness about exactly where the boundary lies (though see Vetter & Newen 2014 for an opposing view).…”