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Optimal reward harvesting in complex perceptual environments.

Vidhya Navalpakkam1, Christof Koch, Antonio Rangel

  • 1Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA. vidhya@caltech.edu

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Humans optimally combine item value and visual saliency for rapid decision-making in complex environments. This Bayesian approach maximizes rewards under time pressure, even with unpredictable target features.

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Area of Science:

  • Cognitive neuroscience
  • Decision science
  • Computational modeling

Background:

  • Rapidly choosing among multiple targets in complex environments is crucial for survival.
  • Previous research examined value and saliency independently, leaving their combined effect on choice unknown.

Purpose of the Study:

  • To investigate how the brain integrates target value and perceptual saliency during rapid decision-making.
  • To determine if human choices align with an ideal Bayesian observer model that combines these factors.

Main Methods:

  • Three experiments involving human subjects maximizing monetary rewards from brief visual displays.
  • Systematic variation of target value and visual saliency, with distractors present.
  • Comparison of behavioral data against computational models (value-seeking, saliency-seeking, Bayesian observer).

Main Results:

  • Human decisions were influenced by both target value and feature contrast (saliency).
  • Behavior aligned with the predictions of an ideal Bayesian observer model.
  • This consistency held even when target feature contrast varied unpredictably.

Conclusions:

  • Individuals dynamically and optimally integrate value and saliency for reward maximization under time pressure.
  • The brain employs a Bayesian approach to decision-making when selecting among multiple targets in cluttered displays.