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Subjective confidence reflects representation of Bayesian probability in cortex.

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Human confidence arises from neural population activity representing probability distributions. Precise sensory evidence correlates with higher reported confidence, supported by brain activity in key regions.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • The origins of human confidence remain incompletely understood.
  • Existing theories propose various mechanisms for confidence computation.
  • Bayesian approaches suggest confidence may stem from probabilistic representations.

Purpose of the Study:

  • To test the Bayesian hypothesis that confidence is based on neural population activity representing probability distributions.
  • To investigate the neural basis of subjective confidence.
  • To link computational models of confidence with empirical data.

Main Methods:

  • Implemented computational models of confidence.
  • Utilized psychophysics and functional magnetic resonance imaging (fMRI) in human participants.
  • Employed a generative model-based decoding technique to extract probability distributions from neural activity.

Main Results:

  • Subjective confidence directly tracked the shape of the decoded neural probability distribution.
  • Increased precision of sensory evidence, reflected in the distribution, led to higher reported confidence.
  • Neural activity in the insula, anterior cingulate cortex, and prefrontal cortex correlated with distribution shape and confidence, aligning with Bayesian models.

Conclusions:

  • Findings support statistical theories of confidence.
  • Probabilistic information encoded in neural population activity appears to guide the computation of subjective confidence.
  • The study provides neural evidence for Bayesian models of confidence.