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A Bayesian Attractor Model for Perceptual Decision Making.

Sebastian Bitzer1, Jelle Bruineberg2, Stefan J Kiebel3

  • 1Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, 04103 Leipzig, Germany; Department of Psychology, Technische Universität Dresden, 01062 Dresden, Germany.

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This study introduces a new computational model for perceptual decision-making by integrating attractor and Bayesian inference frameworks. The model explains flexible decisions, decision-dependent sensory gain, and confidence, advancing our understanding of brain mechanisms.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Decision Science

Background:

  • Mechanisms of perceptual decision-making remain debated, with current models focusing on evidence accumulation.
  • Existing models struggle to explain decision flexibility, decision-dependent sensory gain, and confidence computations.

Purpose of the Study:

  • To propose a novel computational model for perceptual decisions by combining attractor dynamics and Bayesian inference.
  • To provide a unified framework explaining flexible decisions, decision-dependent sensory gain, and confidence.

Main Methods:

  • Embedded an attractor model of decision-making within a probabilistic Bayesian inference framework.
  • Fitted the proposed model to experimental data to validate its explanatory power.

Main Results:

  • The novel model successfully explains decision-making behavior.
  • Demonstrated the model's ability to update decisions based on stimulus changes.
  • Showcased the model's capacity to account for top-down effects and compute explicit confidence measures.

Conclusions:

  • The integrated attractor-Bayesian model offers a more comprehensive account of perceptual decision-making.
  • This framework reconciles existing theories and explains complex perceptual phenomena.
  • The model provides a foundation for understanding neural correlates of decision flexibility, sensory gain modulation, and confidence.