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Humans adapt their decision-making strategies by accounting for mental costs, which increase quadratically with policy complexity. This research quantifies mental effort using information theory.

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Area of Science:

  • Cognitive Science
  • Decision Science
  • Information Theory

Background:

  • Context-dependent decisions involve mental costs, but their nature and resource consumption remain unclear.
  • Existing computational models often assume linear scaling of mental costs, an untested simplification.

Purpose of the Study:

  • To formalize mental costs using an information-theoretic framework based on rate-distortion theory.
  • To investigate how policy complexity relates to mental costs in decision-making.

Main Methods:

  • Defined policy cost as a function of mutual information between states and actions (policy complexity).
  • Conducted four decision-making experiments with varied task manipulations.
  • Applied information-theoretic cost formulation to experimental data.

Main Results:

  • The proposed information-theoretic mental cost formulation parsimoniously describes adaptive adjustments in policy complexity.
  • A quadratic cost function, indicating supralinear costs with increasing policy complexity, provided the best fit to human data.
  • Demonstrated that humans account for mental costs in strategy formation.

Conclusions:

  • Humans exhibit meta-cognitive abilities to manage mental costs during decision-making.
  • Developed a domain-general approach for quantifying mental effort.
  • Findings suggest quadratic scaling of mental costs is more realistic than linear assumptions.