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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Normative decision rules in changing environments.

Nicholas W Barendregt1, Joshua I Gold2, Krešimir Josić3

  • 1Department of Applied Mathematics, University of Colorado Boulder, Boulder, United States.

Elife
|October 25, 2022
PubMed
Summary
This summary is machine-generated.

Brain decision-making models are enhanced by adaptive thresholds that adjust to changing evidence and rewards. These dynamic thresholds improve performance in real-world, variable conditions, outperforming static models.

Keywords:
computational biologydecision-makingdynamic programmingneurosciencenonenormative modelingsystems biology

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Normative models are crucial for understanding brain decision-making.
  • Previous models focused on stable conditions, limiting relevance to dynamic environments.
  • Commitment rules in decision-making under changing contexts remain poorly understood.

Purpose of the Study:

  • To derive a normative model for decisions under changing contexts (evidence quality or reward).
  • To investigate how adaptive decision thresholds maximize performance in dynamic environments.
  • To compare the performance of adaptive thresholds against static and urgency-gated thresholds.

Main Methods:

  • Derived a normative decision model incorporating context changes.
  • Analyzed decision thresholds that dynamically respond to and anticipate environmental shifts.
  • Tested model performance against human response times in tasks with time-varying evidence and reward.

Main Results:

  • Optimal decision thresholds adapt to, and even anticipate, changes in evidence quality and reward.
  • Adaptive thresholds exhibit distinct temporal patterns based on context changes.
  • Adaptive models demonstrate robust performance even with implementation noise.
  • Models with adaptive thresholds better explain human response times than static or urgency-gated models.

Conclusions:

  • Decision-making is a dynamic, adaptive process utilizing expectations.
  • Adaptive thresholds are essential for maximizing performance in naturalistic, changing environments.
  • This work bridges normative and neural models of decision-making, highlighting dynamic expectation updating.