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Suboptimality in perceptual decision making.

Dobromir Rahnev1, Rachel N Denison2

  • 1School of Psychology, Georgia Institute of Technology, Atlanta, GA 30332. drahnev@gmail.comrahnevlab.gatech.edu.

The Behavioral and Brain Sciences
|February 28, 2018
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Summary
This summary is machine-generated.

Researchers reviewed suboptimal performance in perceptual tasks, finding numerous classes of suboptimal decisions. They advocate shifting focus from optimality to detailed observer models for better understanding perceptual decision making.

Keywords:
Bayesian decision theorycue combinationmodelingoptimalityperceptual decision makingsuboptimalityuncertaintyvision

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Psychology

Background:

  • Human perceptual decisions are frequently characterized as optimal.
  • This optimality claim faces criticism for being overly flexible and lacking explanatory power.
  • Debate exists regarding the validity and scope of optimality claims in perceptual decision-making research.

Purpose of the Study:

  • To review the literature on suboptimal performance in perceptual tasks.
  • To critically examine the concept and application of optimality in perceptual decision making.
  • To propose a shift in research focus towards detailed observer models.

Main Methods:

  • Comprehensive literature review of suboptimal perceptual decisions.
  • Categorization of eight distinct classes of suboptimal perceptual decisions.
  • Discussion of conceptual limitations of focusing solely on optimality.

Main Results:

  • Identified various suboptimal decisions: improper criteria, speed-accuracy trade-offs, confidence ratings, cue misweighting, illusions, and biases.
  • Highlighted definitional challenges and limited value of optimality claims.
  • Compiled hypotheses explaining the origins of suboptimal perceptual decisions.

Conclusions:

  • The field should de-emphasize assessing optimality.
  • Focus should shift to building and testing detailed observer models.
  • Research should prioritize explaining suboptimal behavior over assessing optimality.