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Visual metacognition: Measures, models, and neural correlates.

Dobromir Rahnev1

  • 1School of Psychology, Georgia Institute of Technology.

The American Psychologist
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Summary
This summary is machine-generated.

Visual metacognition research shows progress in measuring ability, modeling, and neural correlates. However, integrating these areas is crucial for future advancements in understanding self-awareness of perception.

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Area of Science:

  • Cognitive Neuroscience
  • Psychology
  • Computational Neuroscience

Background:

  • Visual metacognition involves evaluating performance on visual tasks.
  • It bridges visual psychophysics and metacognition research.

Purpose of the Study:

  • Review progress in visual metacognition.
  • Examine measures, computational models, and neural correlates.
  • Advocate for integration across these research areas.

Main Methods:

  • Review of psychophysical properties of metacognitive measures.
  • Examination of computational modeling frameworks and empirical targets.
  • Analysis of anatomical, functional, and causal manipulation studies on neural correlates.

Main Results:

  • Substantial progress in developing measures, models, and identifying neural correlates.
  • Limited integration between the three core research areas.
  • Identified intrinsic links between measures, models, and neural correlates.

Conclusions:

  • Continued progress in visual metacognition necessitates integrating measures, models, and neural findings.
  • Advancements in visual metacognition can inform other metacognition research subfields.