ArticleCerebral cortex (New York, N.Y. : 1991)2020
Variability in Action Selection Relates to Striatal Dopamine 2/3 Receptor Availability in Humans: A PET Neuroimaging Study Using Reinforcement Learning and Active Inference Models.
Article in Cerebral cortex (New York, N.Y. : 1991), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 37 citations in OpenAlex.
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- Dopamine encoding of novelty facilitates efficient uncertainty-driven exploration.PLoS computational biology · 2024Article
- The neurocognitive role of working memory load when Pavlovian motivational control affects instrumental learning.PLoS computational biology · 2023Article
- Blocking D2/D3 dopamine receptors in male participants increases volatility of beliefs when learning to trust others.Nature communications · 2023Article
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- Everything is connected: Inference and attractors in delusions.Schizophrenia research · 2022Article
- The computational relationship between reinforcement learning, social inference, and paranoia.PLoS computational biology · 2022Article
- Self-esteem depends on beliefs about the rate of change of social approval.Scientific reports · 2022Article
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- Early life experience sets hard limits on motor learning as evidenced from artificial arm use.eLife · 2021Article
- A change of mind: Globus pallidus activity and effective connectivity during changes in choice selections.The European journal of neuroscience · 2021Article
- Reduction in social learning and increased policy uncertainty about harmful intent is associated with pre-existing paranoid beliefs: Evidence from modelling a modified serial dictator game.PLoS computational biology · 2020Article
- Action selection in early stages of psychosis: an active inference approach.Journal of psychiatry & neuroscience : JPNArticle
Corrections and comments
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Authors and funding
13 authors at 6 institutions in 4 countries.
Funding
Abstract
Choosing actions that result in advantageous outcomes is a fundamental function of nervous systems. All computational decision-making models contain a mechanism that controls the variability of (or confidence in) action selection, but its neural implementation is unclear-especially in humans. We investigated this mechanism using two influential decision-making frameworks: active inference (AI) and reinforcement learning (RL). In AI, the precision (inverse variance) of beliefs about policies controls action selection variability-similar to decision 'noise' parameters in RL-and is thought to be encoded by striatal dopamine signaling. We tested this hypothesis by administering a 'go/no-go' task to 75 healthy participants, and measuring striatal dopamine 2/3 receptor (D2/3R) availability in a subset (n = 25) using [11C]-(+)-PHNO positron emission tomography. In behavioral model comparison, RL performed best across the whole group but AI performed best in participants performing above chance levels. Limbic striatal D2/3R availability had linear relationships with AI policy precision (P = 0.029) as well as with RL irreducible decision 'noise' (P = 0.020), and this relationship with D2/3R availability was confirmed with a 'decision stochasticity' factor that aggregated across both models (P = 0.0006). These findings are consistent with occupancy of inhibitory striatal D2/3Rs decreasing the variability of action selection in humans.
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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.