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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


Adams, Rick A; Moutoussis, Michael; Nour, Matthew M; Dahoun, Tarik; Lewis, Declan; Illingworth, Benjamin; Veronese, Mattia; Mathys, Christoph; de Boer, Lieke; Guitart-Masip, Marc; Friston, Karl J; Howes, Oliver D; Roiser, Jonathan P (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. Cerebral Cortex, 30(6):3573-3589.

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.

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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Additional indexing

Item Type:Journal Article, refereed, further contribution
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Scopus Subject Areas:Life Sciences > Cognitive Neuroscience
Life Sciences > Cellular and Molecular Neuroscience
Uncontrolled Keywords:Cognitive Neuroscience, Cellular and Molecular Neuroscience
Language:English
Date:18 May 2020
Deposited On:08 Jan 2021 13:31
Last Modified:24 Apr 2024 01:48
Publisher:Oxford University Press
ISSN:1047-3211
OA Status:Hybrid
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/cercor/bhz327
PubMed ID:32083297
Project Information:
  • : FunderFP7
  • : Grant ID607616
  • : Project TitleIN-SENS - Deciphering inter- and intracellular signalling in schizophrenia
  • Content: Published Version
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)