The Perceptual Prediction Paradox

Press, Clare; Kok, Peter and Yon, Daniel. 2019. The Perceptual Prediction Paradox. Trends in Cognitive Sciences, 24(1), pp. 13-24. ISSN 1364-6613 [Article]

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Abstract or Description

From streams of noisy sensory information, we must generate perceptual experiences that are broadly accurate (veridical) and tell us what we did not already know (informative). Bayesian cognitive models propose that predispositions to perceive what we expect will generate more veridical experiences. However, conflicting cancellation models propose that we should perceptually prioritise the unexpected because it is informative. Recent findings from the learning literature may suggest a resolution. We may be initially predisposed to perceive what we expect to generate broadly accurate perceptual representations. However, later processes subsequently highlight unexpected signals that are sufficiently ‘surprising’. We will therefore broadly perceive what we expect, unless unexpected signals are likely to be informative for model updating.

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We are grateful to Jennifer Cook, Peter Redgrave, and Floris de Lange for useful discussions, Emily Thomas for comments on the manuscript, and Gustav Kuhn for sharing his hollow face image. The work was supported by a Leverhulme Trust (RPG-2016-105) and Wellcome Trust (204770/Z/16/Z) grant awarded to C.P.


perception, learning, inference, expectation, surprise

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1 November 2019Accepted
28 November 2019Published Online

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Date Deposited:

24 Feb 2020 12:14

Last Modified:

12 Jun 2021 18:42

Peer Reviewed:

Yes, this version has been peer-reviewed.


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