Aug 13, 2015

A Bayesian Attractor Model for Perceptual Decision Making

PLoS Computational Biology
Sebastian BitzerStefan J Kiebel

Abstract

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence c...Continue Reading

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References

  • References71
  • Citations3

Citations

Mentioned in this Paper

Behavior, Animal
Adrenal Cortex Diseases
Sigmoid Colon
Reaction Time
Neurons
Brain
Etiology
Sensory Level
Learning Disorders
Embedding

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