Explaining multiscale choice dynamics

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.