ArticleProceedings of the National Academy of Sciences of the United States of America2022
Mice exhibit stochastic and efficient action switching during probabilistic decision making.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Population geometries in frontal cortex coordinate value-based decisions.bioRxiv : the preprint server for biology · 2026Article
- Decoding behavior with minimal and interpretable agent models.PLoS computational biology · 2026Article
- Orbitofrontal noradrenaline supports adaptive learning-rate adjustment in probabilistic reversal learning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- N-Methyl-D-Aspartate receptors controlbioRxiv : the preprint server for biology · 2026Article
- Cross-frequency coordination in a hippocampo-cortical circuit during probabilistic reversal learning.Communications biology · 2026Article
- Hybrid neural-cognitive models reveal how memory shapes human reward learning.Nature human behaviour · 2026Article
- Dorsal prefrontal cortex drives perseverative behavior in mice.Nature communications · 2026Article
- Learning reweights the decision dynamics of cortico-basal ganglia-thalamic pathways from deliberation to commitment.bioRxiv : the preprint server for biology · 2026Article
- Time-dependent deployment of medial prefrontal cortical representations in male mice.Nature communications · 2026Article
- Infinite hidden Markov models can dissect the complexities of learning.Nature neuroscience · 2026Article
- Environmental dynamics impact whether matching is optimal.PNAS nexus · 2026Article
- Identifying the factors governing internal state switches during nonstationary sensory decision-making.Nature communications · 2025Article
- Inferring learning rules during de novo task learning.bioRxiv : the preprint server for biology · 2025Article
- Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.Research square · 2025Article
- Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.bioRxiv : the preprint server for biology · 2025Article
- Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning.PLoS computational biology · 2025Article
- Foraging animals use dynamic Bayesian updating to model meta-uncertainty in environment representations.PLoS computational biology · 2025Article
- Striatal arbitration between choice strategies guides few-shot adaptation.Nature communications · 2025Article
- Deciphering neuronal variability across states reveals dynamic sensory encoding.Nature communications · 2025Article
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4 authors.
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Abstract
In probabilistic and nonstationary environments, individuals must use internal and external cues to flexibly make decisions that lead to desirable outcomes. To gain insight into the process by which animals choose between actions, we trained mice in a task with time-varying reward probabilities. In our implementation of such a two-armed bandit task, thirsty mice use information about recent action and action–outcome histories to choose between two ports that deliver water probabilistically. Here we comprehensively modeled choice behavior in this task, including the trial-to-trial changes in port selection, i.e., action switching behavior. We find that mouse behavior is, at times, deterministic and, at others, apparently stochastic. The behavior deviates from that of a theoretically optimal agent performing Bayesian inference in a hidden Markov model (HMM). We formulate a set of models based on logistic regression, reinforcement learning, and sticky Bayesian inference that we demonstrate are mathematically equivalent and that accurately describe mouse behavior. The switching behavior of mice in the task is captured in each model by a stochastic action policy, a history-dependent representation of action value, and a tendency to repeat actions despite incoming evidence. The models parsimoniously capture behavior across different environmental conditionals by varying the stickiness parameter, and like the mice, they achieve nearly maximal reward rates. These results indicate that mouse behavior reaches near-maximal performance with reduced action switching and can be described by a set of equivalent models with a small number of relatively fixed parameters.
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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.