ArticlePLoS computational biology2022
Asymmetric and adaptive reward coding via normalized reinforcement learning.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- A nonlinear relationship between prediction errors and learning rates in human reinforcement-learning.PLoS computational biology · 2025Article
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- Reward prediction error neurons implement an efficient code for reward.Nature neuroscience · 2024Article
- Dynamics Learning Rate Bias in Pigeons: Insights from Reinforcement Learning and Neural Correlates.Animals : an open access journal from MDPI · 2024Article
- Craving money? Evidence from the laboratory and the field.Science advances · 2024Article
- An opponent striatal circuit for distributional reinforcement learning.bioRxiv : the preprint server for biology · 2024Article
- Multi-timescale reinforcement learning in the brain.bioRxiv : the preprint server for biology · 2023Article
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Abstract
Learning is widely modeled in psychology, neuroscience, and computer science by prediction error-guided reinforcement learning (RL) algorithms. While standard RL assumes linear reward functions, reward-related neural activity is a saturating, nonlinear function of reward; however, the computational and behavioral implications of nonlinear RL are unknown. Here, we show that nonlinear RL incorporating the canonical divisive normalization computation introduces an intrinsic and tunable asymmetry in prediction error coding. At the behavioral level, this asymmetry explains empirical variability in risk preferences typically attributed to asymmetric learning rates. At the neural level, diversity in asymmetries provides a computational mechanism for recently proposed theories of distributional RL, allowing the brain to learn the full probability distribution of future rewards. This behavioral and computational flexibility argues for an incorporation of biologically valid value functions in computational models of learning and decision-making.
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