ArticlePLoS computational biology2020
Generalizing to generalize: Humans flexibly switch between compositional and conjunctive structures during reinforcement learning.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 31 citations in OpenAlex.
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- Mechanistic corticostriatal circuit model predicts learning-dependent fMRI dynamics and individual reward bias in humans.bioRxiv : the preprint server for biology · 2026Article
- Learning to Move and Plan like the Knight: Sequential Decision Making with a Novel Motor Mapping.Computational brain & behavior · 2025Article
- The dynamic interplay between in-context and in-weight learning in humans and neural networks.ArXiv · 2025Article
- Parallel trade-offs in human cognition and neural networks: The dynamic interplay between in-context and in-weight learning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Negative affect-driven impulsivity as hierarchical model-based overgeneralization.Trends in cognitive sciences · 2025Review
- Humans learn generalizable representations through efficient coding.Nature communications · 2025Article
- Cognitive Control.Annual review of psychology · 2025Review
- Semantic Anchors Facilitate Task Encoding in Continual Learning.Open mind : discoveries in cognitive science · 2025Article
- Article
- The Computational and Neural Bases of Context-Dependent Learning.Annual review of neuroscience · 2023Review
- Adaptive control of synaptic plasticity integrates micro- and macroscopic network function.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Review
- Contextual inference in learning and memory.Trends in cognitive sciences · 2023Review
- Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals.PLoS computational biology · 2023Article
- Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning.Artificial intelligence · 2022Article
- Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits.PLoS computational biology · 2022Article
- A One-Shot Shift from Explore to Exploit in Monkey Prefrontal Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Computational Investigations of Learning and Synchronization in Cognitive Control.Journal of cognition · 2022Article
- Hypothalamic Interactions with Large-Scale Neural Circuits Underlying Reinforcement Learning and Motivated Behavior.Trends in neurosciences · 2020Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Humans routinely face novel environments in which they have to generalize in order to act adaptively. However, doing so involves the non-trivial challenge of deciding which aspects of a task domain to generalize. While it is sometimes appropriate to simply re-use a learned behavior, often adaptive generalization entails recombining distinct components of knowledge acquired across multiple contexts. Theoretical work has suggested a computational trade-off in which it can be more or less useful to learn and generalize aspects of task structure jointly or compositionally, depending on previous task statistics, but it is unknown whether humans modulate their generalization strategy accordingly. Here we develop a series of navigation tasks that separately manipulate the statistics of goal values ("what to do") and state transitions ("how to do it") across contexts and assess whether human subjects generalize these task components separately or conjunctively. We find that human generalization is sensitive to the statistics of the previously experienced task domain, favoring compositional or conjunctive generalization when the task statistics are indicative of such structures, and a mixture of the two when they are more ambiguous. These results support a normative "meta-generalization" account and suggests that people not only generalize previous task components but also generalize the statistical structure most likely to support generalization.
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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.