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Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

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Published on: February 3, 2015

Bayesian sampling in visual perception.

Rubén Moreno-Bote1, David C Knill, Alexandre Pouget

  • 1Foundation Sant Joan de Déu, Parc Sanitari Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain. rmoreno@bcs.rochester.edu

Proceedings of the National Academy of Sciences of the United States of America
|July 12, 2011
PubMed
Summary
This summary is machine-generated.

This study shows that the brain uses Bayesian sampling for visual perception, where percepts are samples from probability distributions. This finding supports the idea that the nervous system samples interpretations rather than committing to one.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Perception and action are often modeled as probabilistic inferences.
  • Sampling interpretations from probability distributions may offer advantages over committing to a single interpretation.

Purpose of the Study:

  • To investigate if visual percepts represent samples from probability distributions, termed Bayesian sampling.
  • To determine if the brain employs Bayesian sampling for interpreting ambiguous visual stimuli.

Main Methods:

  • Utilized a bistable visual display with superimposed moving drifting gratings.
  • Manipulated pairs of sensory cues to elicit changes in perceived depth ordering.
  • Analyzed the dominance fractions of each percept reported by subjects.

Main Results:

  • The fractions of percept dominance adhered to the multiplicative rule predicted by Bayesian sampling.
  • Demonstrated that attractor neural networks can sample probability distributions.
  • Showed that linear input currents and probabilistic population codes enable neural networks to sample distributions.

Conclusions:

  • Visual percepts align with Bayesian sampling, suggesting the brain samples interpretations from probability distributions.
  • Attractor neural networks provide a potential neural mechanism for implementing Bayesian sampling.
  • This research offers insights into the computational principles underlying visual perception and decision-making.