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This summary is machine-generated.

The distinction between optimal and suboptimal models is not useful for perceptual decision-making. Instead, refine optimality assumptions with brain processing limitations for better models.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Decision Science

Background:

  • The comparison of perceptual decision-making models often relies on a distinction between optimal and suboptimal performance.
  • Rahnev & Denison (R&D) argue this distinction is not useful for model comparison.

Purpose of the Study:

  • To re-evaluate the utility of the optimality assumption in perceptual decision-making models.
  • To propose a refined approach that incorporates constraints from sensory information processing.

Main Methods:

  • Critically analyze the R&D argument against the optimality assumption.
  • Propose integrating specific neural processing limitations into normative models.

Main Results:

  • The optimality assumption has been historically valuable for deriving decision-making models.
  • A strict optimal/suboptimal dichotomy is less informative than previously thought.

Conclusions:

  • Abandoning the optimality assumption prematurely is inadvisable.
  • Refining normative models with constraints on sensory information processing offers a more fruitful path forward for understanding perceptual decision-making.