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Related Experiment Video

Updated: Mar 21, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Perceptual Decision-Making as Probabilistic Inference by Neural Sampling.

Ralf M Haefner1, Pietro Berkes2, József Fiser3

  • 1Brain & Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA.

Neuron
|May 6, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for perception as probabilistic inference, explaining how cortical feedback influences sensory processing. It clarifies correlated neural variability, distinguishing task-related information from noise in decision-making.

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Area of Science:

  • Systems Neuroscience
  • Computational Neuroscience
  • Cognitive Neuroscience

Background:

  • Understanding cortical feedback and neural correlated variability are key challenges in systems neuroscience.
  • Traditional feedforward models struggle to explain complex sensory processing phenomena.

Purpose of the Study:

  • To develop a novel framework for perceptual decision-making integrating sensory and cognitive factors.
  • To explain the influence of cortical feedback on early sensory representations using probabilistic inference.
  • To characterize task-related neural variability.

Main Methods:

  • Developed a probabilistic inference model of perception.
  • Applied the model to a two-alternative forced choice task paradigm.
  • Analyzed task dependence of neural response correlations and choice probabilities.

Main Results:

  • The model successfully explains empirical findings, including task-dependent neural correlations.
  • It accounts for diverging time courses of choice probabilities and psychophysical kernels.
  • Identified a component of correlated variability representing task-related information.

Conclusions:

  • Cortical feedback significantly influences early sensory representations in a task-dependent manner.
  • The proposed model offers a normative approach to integrate sensory and cognitive processes in perceptual decision-making.
  • This framework provides testable predictions for future systems neuroscience research.