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Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

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Published on: September 10, 2018

Overcoming indecision by changing the decision boundary.

Gaurav Malhotra1, David S Leslie2, Casimir J H Ludwig1

  • 1School of Experimental Psychology, University of Bristol.

Journal of Experimental Psychology. General
|April 14, 2017
PubMed
Summary
This summary is machine-generated.

Humans adjust their decision-making boundaries over time to maximize rewards, aligning with ideal observer models. While generally effective, some deviations from optimal strategies were observed, though these errors were relatively minor.

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Area of Science:

  • Cognitive Psychology
  • Decision Science
  • Neuroscience

Background:

  • Dominant decision-making theories propose integrating evidence to a fixed threshold.
  • Recent research suggests decreasing decision boundaries can maximize rewards in realistic scenarios.
  • Empirical evidence for dynamic boundary adjustments in humans is limited.

Purpose of the Study:

  • To investigate whether humans dynamically adjust decision boundaries over time to maximize reward rate.
  • To identify conditions under which time-varying boundaries are optimal for decision-making.
  • To compare human performance against an ideal observer model for decision boundary modulation.

Main Methods:

  • Conducted six expanded-judgment experiments with sequential, noisy, binary evidence.
  • Utilized an ideal observer model to predict optimal time-varying decision boundaries.
  • Analyzed participants' evidence samples to infer the slope of their decision boundaries.

Main Results:

  • Participants modulated their decision boundary slopes in line with ideal observer predictions.
  • Observed systematic, yet robust, deviations from optimality in human decision strategies.
  • Demonstrated clear empirical evidence for time-varying decision boundaries in human participants.

Conclusions:

  • Human decision-making incorporates dynamic adjustments to decision boundaries, supporting normative models.
  • Suboptimal boundary settings in humans are robust, incurring minimal performance costs.
  • The study provides insights into the optimization variables in human decision-making processes.