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Tracking subjects' strategies in behavioural choice experiments at trial resolution
Silvia Maggi1, Rebecca M Hock1, Martin O'Neill1,2
1School of Psychology, University of Nottingham, Nottingham, United Kingdom.
Elife
|March 1, 2024
Summary
This study introduces a Bayesian approach to track learning and exploration strategies in decision-making tasks. The method reveals independent use of win-stay and lose-shift strategies across species, aiding in understanding learning.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Animal Behavior
Background:
- Understanding decision-making requires tracking choice strategies at a trial-by-trial level.
- Existing methods may not offer sufficient resolution or computational efficiency for real-time analysis.
Purpose of the Study:
- To present a novel probabilistic approach for tracking choice strategies during decision-making tasks.
- To validate this approach across diverse subjects including humans, primates, rats, and synthetic agents.
Main Methods:
- Utilized Bayesian evidence accumulation for trial-by-trial strategy tracking.
- Applied the method to analyze decision tasks in multiple species and artificial agents.
Main Results:
- The approach successfully identified learning and exploratory strategies (win-stay, lose-shift).
- Win-stay and lose-shift strategies were found to be used independently, contrary to some assumptions.
- The use of lose-shift strategy strongly indicated latent learning of new rules.
Conclusions:
- The developed Bayesian method provides a robust and computationally efficient way to analyze choice strategies.
- This approach offers new insights into the independent roles of different learning strategies in decision-making.
- The method is adaptable for real-time analysis and closed-loop control in various decision-making contexts.
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