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Related Experiment Video

Updated: Jan 2, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

The Perceptual Prediction Paradox.

Clare Press¹, Peter Kok², Daniel Yon³

¹Department of Psychological Sciences, Birkbeck, University of London, London, UK.

Trends in Cognitive Sciences

|December 3, 2019

Summary

This summary is machine-generated.

Our brains construct reality using expectations, balancing what is likely with what is surprising. This new model explains how perception is both accurate and informative, resolving conflicting theories on expectation and perception.

Keywords:

expectation inference learning perception surprise

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Area of Science:

Cognitive Neuroscience
Perception Science
Computational Neuroscience

Background:

The brain must create accurate (veridical) and novel (informative) perceptions from sensory input.
Existing theories on how expectations shape perception are contradictory.
Some models suggest expected events dominate perception, while others propose suppression of expected events.

Purpose of the Study:

To resolve the conflict between existing theories of expectation and perception.
To propose a unified model explaining how perception is both veridical and informative.
To investigate the role of probabilistic knowledge in shaping perceptual outcomes.

Main Methods:

Development of a two-process computational model.
Theoretical integration of existing frameworks on expectation and perception.
Analysis of how probabilistic knowledge influences perceptual biases.

Main Results:

The proposed model reconciles conflicting theories on expectation's role in perception.
Probabilistic knowledge initially biases perception towards likely events.
Surprising events are subsequently upweighted, enhancing perceptual informativeness.

Conclusions:

A novel two-process model explains how the brain achieves both veridical and informative perception.
Expectations play a dual role: biasing towards the likely and highlighting the surprising.
This framework offers a unified understanding of perceptual inference and learning.