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

Andy Clark*

*Corresponding author for this work

Research output: Contribution to journalArticle

1560 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)181-204
Number of pages24
JournalBehavioral and Brain Sciences
Volume36
Issue number3
DOIs
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • action
  • attention
  • Bayesian brain
  • expectation
  • generative model
  • hierarchy
  • perception
  • precision
  • prediction
  • prediction error
  • predictive coding
  • top-down processing

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