ZotWeb	article-journal
Src Url	Clark (2013)

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. The paper 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.

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Citer:(Clark, 2013)

FTag: Clark-2013

APA7: Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. https://doi.org/10.1017/S0140525X12000477

Abstract

[...] cells that support perception and action by constantly attempting to match incoming sensory inputs with top - down expectations or predictions.

hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing.

[...] 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.

What is adaptive success ?
RoleOfAttention | Perception | ArticleAgent

adaptive succes

hierarchical prediction machine’ approach

examines this ‘ hierarchical prediction machine’ approach

generative model

Action - oriented predictive processing (Traitement prédictif orienté action)

Perceptual learning and inference is necessary to induce prior expectations about how the sensorium unfolds. Action is engaged to resample the world to fulfil these expectations. This places perception and action in intimate relation and accounts for both with the same principle (Friston, Daunizeau, and Kiebel (2009) p. 12)

La perception de la sensation nécessite un apriori d'inférence permettant un apprentissage nécessaire à l'amélioration de l'atteinte des intentions par l'action.

[...] the hierarchical predictive processing perspective suggests concerning situated [...] agency (Thelen and Smith (1994), Hutchins (1995), Wilson (1994) (2004), Haugeland (1998), Hurley (1998), Clark (1997) (2008), Clark and Chalmers (1998), Rowlands (1 999) (2006), Noë (2004), (2009), Wheeler (2005), Menary (2007)). At least on the face of it, the predictive processing story seem to pursue a rather narrowly neurocentric focus, albeit one that reveals (1.5 above) some truly intimate links between perception and action.

[...] the hierarchical predictive processing perspective suggests concerning situated [...] agency [...] seem to pursue a rather narrowly neurocentric focus [...] that reveals \ [...] intimate links between perception and action. (Clark, 2013)

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