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Explaining multiscale choice dynamics.

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Choice response times show complex multiscale dynamics. Distinct learning and control mechanisms explain these dynamics by updating representations of the environment and decision-maker abilities to guide choices.

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Area of Science:

  • Cognitive psychology
  • Computational neuroscience
  • Decision science

Background:

  • Choice response times exhibit pervasive multiscale dynamics, indicating sequential dependencies across various temporal scales.
  • The underlying mechanisms driving these complex dynamics remain poorly understood in decision-making research.

Purpose of the Study:

  • To explain the multiscale dynamics observed in choice response times.
  • To identify and link specific learning and control mechanisms to known sequential effects in decision-making.

Main Methods:

  • Modeling the superposition of distinct learning and control mechanisms.
  • Representing environmental structure and decision-maker abilities.
  • Modulating evidence accumulation processes based on learned representations.

Main Results:

  • Dynamics in the seconds to minutes range are explained by the superposition of multiple learning and control mechanisms.
  • These mechanisms update representations of the choice environment and/or decision-maker abilities after each choice.
  • The model successfully links these mechanisms to stimulus history, error-related, and hard-easy effects in choice sequences.

Conclusions:

  • A unified account explains multiscale dynamics in choice sequences and key experimental effects.
  • The proposed mechanisms provide a computational framework for understanding decision-making under sequential dependencies.
  • The model offers explanations at both group and individual levels for observed choice behavior.