ArticleeLife2023
The functional form of value normalization in human reinforcement learning.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Motivation to exert effort dynamically adapts to reward context and environmental controllability.Cognitive, affective & behavioral neuroscience · 2026Article
- Deterministic dynamics of distributional multi-agent reinforcement learning.PLoS computational biology · 2026Article
- Attention modulates value normalization in human reinforcement learning by shaping reward encoding.Nature communications · 2026Article
- Reference-point dependent reinforcement learning in humans and rats.Nature communications · 2026Article
- Context induces distortions in value representations across multiple elicitation methods and learning modalities.Nature communications · 2026Article
- A reinforcement learning and sequential sampling model constrained by gaze data.PLoS computational biology · 2026Article
- Is the Whole the Sum of its Parts? Neural Computation of Consumer Bundle Valuation in Humans.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Action repetition biases choice in context-dependent decision-making.Communications psychology · 2025Article
- Feature-based reward learning shapes human social learning strategies.Nature human behaviour · 2025Article
- The interoceptive origin of reinforcement learning.Trends in cognitive sciences · 2025Review
- Experience-based risk taking is primarily shaped by prior learning rather than by decision-making.Nature communications · 2025Article
- The timescale and direction of influence of a third inferior alternative in human value-learning.Communications psychology · 2025Article
- Computational and Neural Evidence for Altered Fast and Slow Learning from Losses in Problem Gambling.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Representation of Anticipated Rewards and Punishments in the Human Brain.Annual review of psychology · 2025Review
- Human reinforcement learning processes and biases: computational characterization and possible applications to behavioral public policy.Mind & society · 2025Article
- Relative Value Encoding in Large Language Models: A Multi-Task, Multi-Model Investigation.Open mind : discoveries in cognitive science · 2025Article
- Comparing experience- and description-based economic preferences across 11 countries.Nature human behaviour · 2024Article
- Article
- Outcome context-dependence is not WEIRD: Comparing reinforcement- and description-based economic preferences worldwide.Research square · 2023Article
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2 authors.
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Abstract
Reinforcement learning research in humans and other species indicates that rewards are represented in a context-dependent manner. More specifically, reward representations seem to be normalized as a function of the value of the alternative options. The dominant view postulates that value context-dependence is achieved via a divisive normalization rule, inspired by perceptual decision-making research. However, behavioral and neural evidence points to another plausible mechanism: range normalization. Critically, previous experimental designs were ill-suited to disentangle the divisive and the range normalization accounts, which generate similar behavioral predictions in many circumstances. To address this question, we designed a new learning task where we manipulated, across learning contexts, the number of options and the value ranges. Behavioral and computational analyses falsify the divisive normalization account and rather provide support for the range normalization rule. Together, these results shed new light on the computational mechanisms underlying context-dependence in learning and decision-making.
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