Tracking subjects’ strategies in behavioural choice experiments at trial resolution

Journal article

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

Authors: Maggi, S; Hock, RM; O'Neill, M; Buckley, M; Moran, PM

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: eLife Sciences Publications
• Journal: eLife
• Volume: 13
• Pages: e86491
• Article number: e86491
• Publication date: 2024-03-01
• Acceptance date: 2024-02-23
• DOI: 10.7554/elife.86491
• EISSN: 2050-084X
• ISSN: 2050-084X
• Language: English
• Keywords: Cognitive psychology; Salient; Psychology; Machine learning; Probabilistic logic; Control (management); Computer science; Bayesian probability; Artificial intelligence