ArticleProceedings of the National Academy of Sciences of the United States of America2021
Human subjects exploit a cognitive map for credit assignment.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Automatic value learning results in counterproductive human behavior.Nature communications · 2026Article
- Disinformation elicits learning biases.eLife · 2026Article
- Adaptive integration of model-based and model-free strategies in human reinforcement learning of reachable space.bioRxiv : the preprint server for biology · 2026Article
- Metacognitive efficiency in learned value-based choice.PLoS computational biology · 2026Article
- Information uncertainty influences learning strategy from sequentially delayed rewards.PLoS computational biology · 2026Article
- Noradrenergic and Dopaminergic modulation of meta-cognition and meta-control.PLoS computational biology · 2025Article
- Cognitive Graphs: Representational Substrates for Planning.Decision (Washington, D.C.) · 2024Article
- Exploring the steps of learning: computational modeling of initiatory-actions among individuals with attention-deficit/hyperactivity disorder.Translational psychiatry · 2024Article
- Interactive cognitive maps support flexible behavior under threat.Cell reports · 2023Article
- Emotions as computations.Neuroscience and biobehavioral reviews · 2023Review
- Dopaminergic challenge dissociates learning from primary versus secondary sources of information.eLife · 2022Article
- Model-based learning retrospectively updates model-free values.Scientific reports · 2022Article
- Dopamine enhances model-free credit assignment through boosting of retrospective model-based inference.eLife · 2021Article
- Executive Function Assigns Value to Novel Goal-Congruent Outcomes.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Assigning the right credit to the wrong action: compulsivity in the general population is associated with augmented outcome-irrelevant value-based learning.Translational psychiatry · 2021Article
- Efficiency and prioritization of inference-based credit assignment.Current biology : CB · 2021Article
- Model based planners reflect on their model-free propensities.PLoS computational biology · 2021Article
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3 authors.
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Abstract
An influential reinforcement learning framework proposes that behavior is jointly governed by model-free (MF) and model-based (MB) controllers. The former learns the values of actions directly from past encounters, and the latter exploits a cognitive map of the task to calculate these prospectively. Considerable attention has been paid to how these systems interact during choice, but how and whether knowledge of a cognitive map contributes to the way MF and MB controllers assign credit (i.e., to how they revaluate actions and states following the receipt of an outcome) remains underexplored. Here, we examine such sophisticated credit assignment using a dual-outcome bandit task. We provide evidence that knowledge of a cognitive map influences credit assignment in both MF and MB systems, mediating subtly different aspects of apparent relevance. Specifically, we show MF credit assignment is enhanced for those rewards that are related to a choice, and this contrasted with choice-unrelated rewards that reinforced subsequent choices negatively. This modulation is only possible based on knowledge of task structure. On the other hand, MB credit assignment was boosted for outcomes that impacted on differences in values between offered bandits. We consider mechanistic accounts and the normative status of these findings. We suggest the findings extend the scope and sophistication of cognitive map-based credit assignment during reinforcement learning, with implications for understanding behavioral control.
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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.