Tracking subjects’ strategies in behavioural choice experiments at trial resolution

Investigating how, when, and what subjects learn during decision-making tasks requires tracking their choice strategies on a trial-by-trial basis. Here, we present a simple but effective probabilistic approach to tracking choice strategies at trial resolution using Bayesian evidence accumulation. We...

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Veröffentlicht in:eLife Jg. 13
Hauptverfasser: Maggi, Silvia, Hock, Rebecca M, O'Neill, Martin, Buckley, Mark, Moran, Paula M, Bast, Tobias, Sami, Musa, Humphries, Mark D
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England eLife Sciences Publications Ltd 01.03.2024
Schlagworte:
Algorithms
Animals
Bayes Theorem
Bayesian analysis
Bayesian inference
behavioural strategy
Binomial distribution
Choice Behavior
Decision making
Humans
Learning
Primates
Probability
Random variables
Rats
Reward
rhesus macaque
rat
neuroscience
decision making
behavioural strategy
Bayesian inference
human
ISSN:2050-084X, 2050-084X
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Zusammenfassung:Investigating how, when, and what subjects learn during decision-making tasks requires tracking their choice strategies on a trial-by-trial basis. Here, we present a simple but effective probabilistic approach to tracking choice strategies at trial resolution using Bayesian evidence accumulation. We show this approach identifies both successful learning and the exploratory strategies used in decision tasks performed by humans, non-human primates, rats, and synthetic agents. Both when subjects learn and when rules change the exploratory strategies of win-stay and lose-shift, often considered complementary, are consistently used independently. Indeed, we find the use of lose-shift is strong evidence that subjects have latently learnt the salient features of a new rewarded rule. Our approach can be extended to any discrete choice strategy, and its low computational cost is ideally suited for real-time analysis and closed-loop control.
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ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.86491