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Related Experiment Video

Updated: May 3, 2026

Using Looming Visual Stimuli to Evaluate Mouse Vision

Using Looming Visual Stimuli to Evaluate Mouse Vision

Published on: June 13, 2019

Bayesian inference by visuomotor neurons in the prefrontal cortex.

Thomas A Langlois¹, Julie A Charlton², Robbe L T Goris³

¹Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, Cambridge, MA 02139.

Proceedings of the National Academy of Sciences of the United States of America

|March 27, 2025

Summary

Neural circuits in the prefrontal cortex integrate sensory information with prior expectations to form perceptual decisions. This process, resembling Bayesian inference, influences decision-making accuracy and bias.

Keywords:

Bayesian inference perceptual decision-making prefrontal cortex

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Area of Science:

Neuroscience
Cognitive Science
Decision Science

Background:

Perceptual judgments rely on neural activity in decision-making areas.
Prior expectations can bias perception when sensory input is ambiguous, reflecting Bayesian inference.
The neural mechanisms combining sensory data and prior expectations for decisions remain unclear.

Purpose of the Study:

To investigate how prefrontal cortex circuits combine sensory signals and prior expectations during perceptual decision-making.
To characterize the neural dynamics underlying the integration of sensory evidence and prior beliefs.
To determine the computational principles governing these neural processes in ambiguous environments.

Main Methods:

Recorded neural population activity in the prefrontal cortex of macaque monkeys.
Trained monkeys to make perceptual judgments on ambiguous visual stimuli under varying distributions.
Isolated and analyzed the decision variable (DV) dynamics reflecting perceptual choices.

Main Results:

The dynamical evolution of the decision variable (DV) integrates both sensory signals and prior expectations.
Prior expectations influenced the DV's trajectory before and during stimulus presentation.
Reduced DV dynamic range, driven by prior expectations, led to more biased and less sensitive decisions.

Conclusions:

Prefrontal cortex circuits implement a form of approximate hierarchical Bayesian inference.
Neural dynamics of the DV reveal how prior knowledge shapes perceptual decisions.
Findings elucidate the neural basis of Bayesian computation in decision-making circuits.