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.

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. - In: CEREBRAL CORTEX. - ISSN 1047-3211. - 30:6(2020), pp. 3573-3589. [10.1093/cercor/bhz327]

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

Mathys, Christoph;
2020-01-01

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.
2020
30
6
3573
3589
10.1093/cercor/bhz327
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
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/110836
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