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This study introduces a Bayesian framework for understanding self-evaluation, unifying confidence and error detection. It models metacognition as a "second-order" inference, explaining variations in self-assessment accuracy.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Psychiatry
  • Decision Science

Background:

  • Metacognitive assessments of decision quality guide behavior, especially with limited feedback.
  • Existing computational frameworks lack a unified account for confidence and error detection.
  • Dissociations between performance and metacognition often rely on ad hoc assumptions.

Purpose of the Study:

  • To present a general Bayesian framework for self-evaluation.
  • To computationally model confidence and error detection within a unified account.
  • To explain variations in metacognitive accuracy.

Main Methods:

  • Developed a "second-order" Bayesian inference framework.
  • Modeled self-evaluation as inferring the performance of another actor.
  • Contrasted second-order models with simpler first-order models using simulations.

Main Results:

  • The second-order computation framework unifies confidence and error detection.
  • The model explains metacognitive performance variations relative to task performance.
  • Generated novel predictions regarding self-action contributions to metacognition.

Conclusions:

  • Second-order computation provides a unified account of self-evaluation.
  • This framework offers insights into intact and impaired self-evaluation.
  • Suggests second-order computation underlies self-evaluative judgments across domains.