Perceptual Decision-Making as Probabilistic Inference by Neural Sampling

Haefner, Ralf M.; Berkes, Pietro; Fiser, József

doi:10.1016/j.neuron.2016.03.020

Cited by 207Visual perception and processing mechanismsNeural dynamics and brain functionMultisensory perception and integration

Perceptual Decision-Making as Probabilistic Inference by Neural Sampling

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Haefner, Ralf M., Berkes, Pietro, Fiser, József·2016·Neuron

DOI 10.1016/j.neuron.2016.03.020

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Abstract

We address two main challenges facing systems neuroscience today: understanding the nature and function of cortical feedback between sensory areas and of correlated variability. Starting from the old idea of perception as probabilistic inference, we show how to use knowledge of the psychophysical task to make testable predictions for the influence of feedback signals on early sensory representations. Applying our framework to a two-alternative forced choice task paradigm, we can explain multiple empirical findings that have been hard to account for by the traditional feedforward model of sensory processing, including the task dependence of neural response correlations and the diverging time courses of choice probabilities and psychophysical kernels. Our model makes new predictions and characterizes a component of correlated variability that represents task-related information rather than performance-degrading noise. It demonstrates a normative way to integrate sensory and cognitive components into physiologically testable models of perceptual decision-making.

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Approximate inference algorithms such as sampling may explain cortical feedback signals and activity correlations.
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Authors: Haefner, R. M., Berkes, P., & Fiser, J.
Affiliations: University of Rochester (US)
Brandeis University (US)
Central European University (AT)
Corresponding author: Haefner, Ralf M.
ORCID: Haefner, Ralf M.
Berkes, Pietro
Fiser, József
Venue: Neuron
Publisher: Elsevier BV
Published: 2016-05
Volume: 90
Issue: 3
Pages: 649-660
ISSN: 0896-6273 (ISSN-L)
DOI: 10.1016/j.neuron.2016.03.020
OpenAlex: W2104922910
Cited by: 207
References: 67
Citations by year: 2026: 14 · 2025: 17 · 2024: 16 · 2023: 22 · 2022: 17 · 2021: 25 · 2020: 21 · 2019: 17 · 2018: 22 · 2017: 18 · 2016: 12 · 2015: 5 · 2014: 1
FWCI: 14.49
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 368 d)
Locations: Neuron (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
Keywords: perceptual decision making, probabilistic inference, cortical feedback, two-alternative forced choice, choice probabilities
MeSH terms: Cerebral Cortex, Choice Behavior, Cognition, Decision Making, Humans, Models, Neurological, Perception, Psychomotor Performance, Sensory Receptor Cells

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Perceptual Decision-Making as Probabilistic Inference by Neural Sampling

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