ArticleeLife2024
Tracking subjects' strategies in behavioural choice experiments at trial resolution.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Stage-Dependent Effects of "Too Little and Too Much" Medial Prefrontal Activity on Reversal Learning in Rats: Functional Inhibition Impairs Early, Whereas Neural Disinhibition Impairs Late Reversals.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Quantifying uncertainty in drift diffusion models of decision making under temporal dependence and parameter variability.bioRxiv : the preprint server for biology · 2026Article
- Hypothesis testing governs strategic motor learning.NPJ science of learning · 2026Article
- Strain- and age-dependent divergence in mouse appetitive spatial learning and decision strategies.Frontiers in behavioral neuroscience · 2026Article
- Infinite hidden Markov models can dissect the complexities of learning.Nature neuroscience · 2026Article
- Abstract rule learning promotes cognitive flexibility in complex environments across species.Nature communications · 2025Article
- Rhythmic modulation of dorsal hippocampus across distinct behavioral timescales during spatial set-shifting.bioRxiv : the preprint server for biology · 2025Article
- Flexible decision-making is related to strategy learning, vicarious trial and error, and medial prefrontal rhythms during spatial set-shifting.Learning & memory (Cold Spring Harbor, N.Y.) · 2024Article
- Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts.PLoS computational biology · 2024Article
- Impaired reversal learning in the Dlg2+/- rat model of genetic risk for psychiatric disorder: Important questions regarding the neuro-behavioral mechanisms of reversal learning.Genes, brain, and behavior · 2023Article
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Authors and funding
8 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.