ArticleNature human behaviour2017
The successor representation in human reinforcement learning.
Article in Nature human behaviour, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 161 papers.
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Who cites it
161 citing papers in PubMed.
- Modeling other minds: Bayesian inference explains human choices in group decision-making.Science advances · 2019Trial
- Natural and engineered safe reinforcement learning.Communications psychology · 2026Review
- Experience-dependent emergence of multi-scale spatial organization during foraging in freely moving marmosets.iScience · 2026Article
- Acetylcholine: a candidate substrate for hippocampal predictive learning?Nature reviews. Neuroscience · 2026Review
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- Data augmentation as a framework for modeling hippocampal contributions to generalization.ArXiv · 2026Article
- The hippocampus rapidly integrates sequence representations during novel multistep predictions.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Article
- Planning in the Brain: It's Not What You Think It Is.Annual review of neuroscience · 2026Review
- Flexible route planning and rapid structure learning by mice in complex environments.bioRxiv : the preprint server for biology · 2026Article
- Medial temporal lobe encodes cognitive maps of real-world social networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Sleep strengthens successor representations of learned sequences in humans.PLoS biology · 2026Article
- Building Goal-Directed Cognitive Graphs.bioRxiv : the preprint server for biology · 2026Article
- Adaptive integration of model-based and model-free strategies in human reinforcement learning of reachable space.bioRxiv : the preprint server for biology · 2026Article
- Contextual inference through flexible integration of environmental features and behavioural outcomes.PLoS computational biology · 2026Article
- Accounting for sensitivity of latent learning to behavioral statistics with successor representations.PLoS computational biology · 2026Article
- Dopaminergic responses to identity prediction errors depend differently on the orbitofrontal cortex and hippocampus.Behavioral neuroscience · 2026Article
- Task, person, and experiential characteristics drive the transfer of learning.Communications psychology · 2026Article
- Article
- Between planning and map building: Prioritizing replay when future goals are uncertain.Neuron · 2025Review
- Trial-by-trial learning of successor representations in human behavior.bioRxiv : the preprint server for biology · 2025Article
101 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
Theories of reward learning in neuroscience have focused on two families of algorithms thought to capture deliberative versus habitual choice. 'Model-based' algorithms compute the value of candidate actions from scratch, whereas 'model-free' algorithms make choice more efficient but less flexible by storing pre-computed action values. We examine an intermediate algorithmic family, the successor representation, which balances flexibility and efficiency by storing partially computed action values: predictions about future events. These pre-computation strategies differ in how they update their choices following changes in a task. The successor representation's reliance on stored predictions about future states predicts a unique signature of insensitivity to changes in the task's sequence of events, but flexible adjustment following changes to rewards. We provide evidence for such differential sensitivity in two behavioural studies with humans. These results suggest that the successor representation is a computational substrate for semi-flexible choice in humans, introducing a subtler, more cognitive notion of habit.
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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.