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Confidence as Bayesian Probability: From Neural Origins to Behavior.

Florent Meyniel1, Mariano Sigman2, Zachary F Mainen3

  • 1Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, F-91191, Gif sur Yvette Cedex, France.

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This study proposes a unified computational model of confidence, defining it as Bayesian probability. It distinguishes between distributional and summary confidence representations in the brain, crucial for cognitive functions.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Psychology
  • Decision Making

Background:

  • Confidence research spans psychology and neuroscience.
  • Existing models lack a unified framework.
  • Bayesian probability offers a potential unifying principle.

Purpose of the Study:

  • To present a computational definition of confidence as Bayesian probability.
  • To unify diverse research on confidence across disciplines.
  • To explore neural representations of confidence.

Main Methods:

  • Computational modeling based on Bayesian probability.
  • Theoretical exploration of neural representations.
  • Analysis of how confidence is computed and utilized in the brain.

Main Results:

  • Confidence can be unified under a Bayesian probability framework.
  • Distinct forms of confidence representation exist: distributional and summary.
  • Summary confidence is derived from distributional confidence via neural readout mechanisms.

Conclusions:

  • A Bayesian computational view provides a unified framework for understanding confidence.
  • Neural readout mechanisms balance optimality and flexibility in confidence processing.
  • This framework supports confidence's role in diverse cognitive functions.