Journal
CEREBRAL CORTEX
Volume 30, Issue 6, Pages 3573-3589Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhz327
Keywords
active inference; action selection; decision temperature; dopamine 2/3 receptors; go no-go task; reinforcement learning
Categories
Funding
- Academy of Medical Sciences [AMS-SGCL13-Adams]
- National Institute of Health Research [CL2013-18-003]
- NIHR UCLH Biomedical Research Centre
- Wellcome Strategic Award [095844/7/11/Z]
- National institute for Health Research
- EU-FP7 MC6 ITN IN-SENS grant [607616]
- Swedish Research Council [VR521-2013-2589]
- Wellcome Trust [088130/Z/09/Z, 094849/Z/10/Z]
- NIHR UCLH Biomedical Research Centre pump priming award [BRC252/NS/JR/101410]
- Medical Research Council-UK [MC-A656-5QD30]
- National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London
- MRC [MR/L022176/1, MR/N027078/1, G0700995, MR/N026063/1, MC_U120097115, MR/S007806/1] Funding Source: UKRI
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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 [C-11]-(+)-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 D(2/3)Rs decreasing the variability of action selection in humans.
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