ReviewAnnual review of psychology2017
Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework.
Review in Annual review of psychology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 158 papers.
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Who cites it
158 citing papers in PubMed, 486 citations in OpenAlex.
- When "why" meets "how" in episodic memory: Towards a computational model of Boyle and Brown's functional analysis.Learning & behavior · 2026Article
- Hippocampal-cingulate dynamics in the human brain link reinforcement-learning and memory.bioRxiv : the preprint server for biology · 2026Article
- Hippocampo-neocortical interaction as compressive retrieval-augmented generation.Nature communications · 2026Article
- Hybrid neural-cognitive models reveal how memory shapes human reward learning.Nature human behaviour · 2026Article
- Article
- A habit and working memory model as an alternative account of human reward-based learning.Nature human behaviour · 2026Article
- Information uncertainty influences learning strategy from sequentially delayed rewards.PLoS computational biology · 2026Article
- Prioritizing Detailed Item Memories Through Post-Encoding Motivation Requires Consolidation.Open mind : discoveries in cognitive science · 2026Article
- Statistical learning: A core mechanism in a developmental hierarchy.Current opinion in neurobiology · 2025Review
- Noisy Memory Generates Value in Changing Environments.Adaptive behavior · 2025Article
- Reconciling time and prediction error theories of associative learning.Nature communications · 2025Article
- How working memory and reinforcement learning interact when avoiding punishment and pursuing reward concurrently.Journal of experimental psychology. General · 2025Article
- Thalamic regulation of reinforcement learning strategies across prefrontal-striatal networks.Nature communications · 2025Article
- Observational learning strategies impact the neural correlates of declarative memory formation.Communications biology · 2025Article
- Superior colliculus encodes the visual cue associated with rewarding behavior in sucrose self-administration in mice.Scientific reports · 2025Article
- Episodic Memories Bias Contextual Threat Inferences after Aerobic and Mind-Body Exercise.Mental health and physical activity · 2025Article
- A scalable reinforcement learning framework inspired by hippocampal memory mechanisms for efficient contextual and sequential decision making.Scientific reports · 2025Article
- Article
- A Multi-Region Brain Model to Elucidate the Role of Hippocampus in Spatially Embedded Decision-Making.bioRxiv : the preprint server for biology · 2025Article
- Empowering safer socially sensitive autonomous vehicles using human-plausible cognitive encoding.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
98 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 2 institutions in 1 country.
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Abstract
We review the psychology and neuroscience of reinforcement learning (RL), which has experienced significant progress in the past two decades, enabled by the comprehensive experimental study of simple learning and decision-making tasks. However, one challenge in the study of RL is computational: The simplicity of these tasks ignores important aspects of reinforcement learning in the real world: (a) State spaces are high-dimensional, continuous, and partially observable; this implies that (b) data are relatively sparse and, indeed, precisely the same situation may never be encountered twice; furthermore, (c) rewards depend on the long-term consequences of actions in ways that violate the classical assumptions that make RL tractable. A seemingly distinct challenge is that, cognitively, theories of RL have largely involved procedural and semantic memory, the way in which knowledge about action values or world models extracted gradually from many experiences can drive choice. This focus on semantic memory leaves out many aspects of memory, such as episodic memory, related to the traces of individual events. We suggest that these two challenges are related. The computational challenge can be dealt with, in part, by endowing RL systems with episodic memory, allowing them to (a) efficiently approximate value functions over complex state spaces, (b) learn with very little data, and (c) bridge long-term dependencies between actions and rewards. We review the computational theory underlying this proposal and the empirical evidence to support it. Our proposal suggests that the ubiquitous and diverse roles of memory in RL may function as part of an integrated learning system.
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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.