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

被引：17

作者：

Adams, Rick A. ^{[1
,2
,3
,4
]}

Moutoussis, Michael ^{[5
,6
]}

Nour, Matthew M. ^{[3
,4
,7
]}

Dahoun, Tarik ^{[3
,4
,8
]}

Lewis, Declan ^{[1
]}

Illingworth, Benjamin ^{[1
]}

Veronese, Mattia ^{[9
]}

Mathys, Christoph ^{[6
,10
,11
,12
]}

de Boer, Lieke ^{[13
]}

Guitart-Masip, Marc ^{[6
,13
]}

Friston, Karl J. ^{[5
]}

Howes, Oliver D. ^{[3
,4
,7
]}

Roiser, Jonathan P. ^{[1
]}

机构：

[1] UCL, Inst Cognit Neurosci, 17 Queen Sq, London WC1N 3AZ, England

[2] UCL, Div Psychiat, London W1T 7NF, England

[3] Hammersmith Hosp, MRC London Inst Med Sci, Robert Steiner MRI Unit, Psychiat Imaging Grp, London W12 0NN, England

[4] Imperial Coll London, Hammersmith Hosp, Fac Med, Inst Clin Sci, London W12 0NN, England

[5] UCL, Wellcome Ctr Human Neuroimaging, London WC1N 3BG, England

[6] Max Planck UCL Ctr Computat Psychiat & Ageing Res, London WC1B 5EH, England

[7] Kings Coll London, Inst Psychiat Psychol & Neurosci IoPPN, Dept Psychosis Studies, London SE5 8AF, England

[8] Univ Oxford, Warneford Hosp, Dept Psychiat, Oxford OX3 7JX, England

[9] Kings Coll London, Ctr Neuroimaging Sci, Inst Psychiat Psychol & Neurosci IoPPN, London SE5 8AF, England

[10] Scuola Internazl Super Avanzati SISSA, I-34136 Trieste, Italy

[11] Univ Zurich, Inst Biomed Engn, Translat Neuromodeling Unit TNU, CH-8032 Zurich, Switzerland

[12] Swiss Fed Inst Technol, CH-8032 Zurich, Switzerland

[13] Karolinska Inst, Aging Res Ctr, S-17165 Stockholm, Sweden

来源：

CEREBRAL CORTEX | 2020年 / 30卷 / 06期

基金：

英国医学研究理事会; 英国惠康基金; 欧盟第七框架计划; 瑞典研究理事会;

关键词：

active inference; action selection; decision temperature; dopamine; 2/3; receptors; go no-go task; reinforcement learning; D2; RECEPTORS; BINDING; SENSITIVITY; MODULATION; PREDICTION; RELEASE; CHOICES; REWARD; D1;

D O I：

10.1093/cercor/bhz327

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

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.

引用

页码：3573 / 3589

页数：17

共 83 条

[1] Long-term test-retest reliability of striatal and extrastriatal dopamine D2/3 receptor binding: study with [11C]raclopride and high-resolution PET [J].