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Visual confidence accurately tracks increasing internal noise with eccentricity in peripheral vision.

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Humans can accurately monitor sensory noise in peripheral vision. This study shows perceptual confidence reliably tracks internal noise, improving decision-making.

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Area of Science:

  • Cognitive Neuroscience
  • Psychology
  • Computational Neuroscience

Background:

  • Sensory representations are noisy, impacting decision-making.
  • Perceptual confidence, the evaluation of decision quality, is crucial but its accuracy in tracking internal noise is debated.
  • Peripheral vision offers a unique environment to study this, but prior research yielded inconsistent findings.

Purpose of the Study:

  • To investigate whether perceptual confidence accurately reflects internal sensory noise, particularly in peripheral vision.
  • To test the Bayesian-confidence hypothesis, proposing confidence derives from the posterior probability distribution of perceptual estimates.
  • To resolve discrepancies in previous studies by employing a normative Bayesian framework and incentivized confidence measurements.

Main Methods:

  • Utilized a normative Bayesian framework to model perceptual confidence.
  • Conducted two perceptual tasks (spatial localization, orientation estimation) with varying stimulus eccentricity.
  • Measured confidence using post-decision wagering, where participants set a range around estimates for rewards based on accuracy and range width.
  • Estimated sensory noise, assuming it increases linearly with eccentricity, to predict confidence.

Main Results:

  • The Bayesian ideal-observer model provided the best prediction of confidence across both tasks.
  • Human confidence judgments accurately reflected the increased sensory noise observed with greater stimulus eccentricity.
  • Incentivized confidence measurements aligned with normative predictions.

Conclusions:

  • Humans possess the metacognitive ability to accurately monitor internal noise, even in peripheral vision.
  • Perceptual confidence serves as a reliable indicator of sensory evidence quality.
  • Findings support the Bayesian-confidence hypothesis and suggest optimal use of sensory information for decision-making.