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Updated: Jun 14, 2025
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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
Published on: May 8, 2021
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Reinforcement learning of adaptive control strategies
Leslie K Held1, Luc Vermeylen2, David Dignath3
1Department of Experimental Psychology, Ghent University, Henri Dunantlaan 2, 9000, Ghent, Belgium. leslie.held@ugent.be.
Communications Psychology
|September 6, 2024
Summary
People can learn to adjust cognitive control through reinforcement, adapting their focus based on rewards for task performance. This study shows reinforcement learning can optimize cognitive control strategies.
Area of Science:
- Cognitive psychology
- Neuroscience
Background:
- Cognitive control allows humans to regulate goal-directed behavior by adjusting reliance on task information.
- Traditionally, cognitive control adjustments are linked to experienced or anticipated task-rule conflict.
Purpose of the Study:
- To investigate whether cognitive control settings can be adapted through reinforcement learning.
- To examine how selective rewards for performance on congruent or incongruent trials influence cognitive control.
Main Methods:
- Three preregistered task switching experiments involving 415 participants.
- Selective reinforcement of correct performance on trials with high (incongruent) or low (congruent) task-rule conflict.
- Drift diffusion modelling to analyze within-trial adjustments.
Main Results:
- Participants rewarded for incongruent trials exhibited smaller congruency effects, indicating adaptive control.
- Reinforcement successfully modulated cognitive control settings based on the reward scheme.
- Drift diffusion models suggested conflict-dependent adjustments in response thresholds.
Conclusions:
- Cognitive control strategies can be learned through reinforcement, similar to stimulus-response associations.
- Reinforcement learning offers a mechanism for optimizing cognitive control in response to environmental contingencies.
- Findings highlight the flexibility of cognitive control adaptation through reward-based learning.
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