Tracking subjects’ strategies in behavioural choice experiments at trial resolution

Abstract

AbstractInvestigating the strategies engaged by subjects in decision making and learning 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. We find learning occurs earlier and more often than estimated using classical approaches. Also, win-stay and lose-shift strategies, often considered as complementary, are consistently used independently, with the adoption of lose-shift preceding both learning new rules and switching away from old rules. 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.