ArticlePLoS computational biology2024
Reward Bases: A simple mechanism for adaptive acquisition of multiple reward types.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Mesocorticostriatal Reinforcement Learning of State Representation and Value with Implications for the Mechanisms of Schizophrenia.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Dopaminergic responses to identity prediction errors depend differently on the orbitofrontal cortex and hippocampus.Behavioral neuroscience · 2026Article
- A brief history of dopamine prediction errors.Frontiers in computational neuroscience · 2026Review
- Need-selective gating of dopamine neuron cue responses by real and virtual hunger.bioRxiv : the preprint server for biology · 2025Article
- A decision-space model explains context-specific decision-making.Nature communications · 2025Article
- Striatal dopamine signals errors in prediction across different informational domains.Science advances · 2025Article
- Prospective contingency explains behavior and dopamine signals during associative learning.Nature neuroscience · 2025Article
- Multi-timescale reinforcement learning in the brain.Nature · 2025Article
- The devilish details affecting TDRL models in dopamine research.Trends in cognitive sciences · 2025Review
- The curious case of dopaminergic prediction errors and learning associative information beyond value.Nature reviews. Neuroscience · 2025Review
- Expectancy-related changes in firing of dopamine neurons depend on hippocampus.Nature communications · 2024Article
- The role of prospective contingency in the control of behavior and dopamine signals during associative learning.bioRxiv : the preprint server for biology · 2024Article
- Multi-timescale reinforcement learning in the brain.bioRxiv : the preprint server for biology · 2023Article
- Expectancy-related changes in firing of dopamine neurons depend on hippocampus.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Animals can adapt their preferences for different types of reward according to physiological state, such as hunger or thirst. To explain this ability, we employ a simple multi-objective reinforcement learning model that learns multiple values according to different reward dimensions such as food or water. We show that by weighting these learned values according to the current needs, behaviour may be flexibly adapted to present preferences. This model predicts that individual dopamine neurons should encode the errors associated with some reward dimensions more than with others. To provide a preliminary test of this prediction, we reanalysed a small dataset obtained from a single primate in an experiment which to our knowledge is the only published study where the responses of dopamine neurons to stimuli predicting distinct types of rewards were recorded. We observed that in addition to subjective economic value, dopamine neurons encode a gradient of reward dimensions; some neurons respond most to stimuli predicting food rewards while the others respond more to stimuli predicting fluids. We also proposed a possible implementation of the model in the basal ganglia network, and demonstrated how the striatal system can learn values in multiple dimensions, even when dopamine neurons encode mixtures of prediction error from different dimensions. Additionally, the model reproduces the instant generalisation to new physiological states seen in dopamine responses and in behaviour. Our results demonstrate how a simple neural circuit can flexibly guide behaviour according to animals' needs.
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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.