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Factual and counterfactual learning in major adolescent depressive disorder, evidence from an instrumental learning study

Published online by Cambridge University Press:  10 May 2023

Qiang Shen
Affiliation:
Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education), 201620, Shanghai, China School of Business and Management, Shanghai International Studies University, 201620, Shanghai, China Joint Lab of Finance and Business Intelligence, Guangdong Institute of Intelligence Science and Technology, 519031, Zhuhai, China
Shiguang Fu
Affiliation:
Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education), 201620, Shanghai, China School of Business and Management, Shanghai International Studies University, 201620, Shanghai, China Joint Lab of Finance and Business Intelligence, Guangdong Institute of Intelligence Science and Technology, 519031, Zhuhai, China
Xiaoying Jiang
Affiliation:
Hangzhou Mental Health Center of Children and Adolescents, Hangzhou Seventh People's Hospital, 310006, Hangzhou, China
Xiaoyu Huang
Affiliation:
Hangzhou Mental Health Center of Children and Adolescents, Hangzhou Seventh People's Hospital, 310006, Hangzhou, China
Doudou Lin
Affiliation:
School of Management, Zhejiang University of Technology, 310023, Hangzhou, China
Qingyan Xiao
Affiliation:
Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education), 201620, Shanghai, China School of Business and Management, Shanghai International Studies University, 201620, Shanghai, China Joint Lab of Finance and Business Intelligence, Guangdong Institute of Intelligence Science and Technology, 519031, Zhuhai, China
Sitti Khadijah
Affiliation:
School of Management, Zhejiang University of Technology, 310023, Hangzhou, China
Yaping Yan
Affiliation:
Department of Neurology, The Second Affiliated Hospital of Zhejiang University, 310009, Hangzhou, China
Xiaoxing Xiong
Affiliation:
Department of Neurosurgery, Renmin Hospital of Wuhan University, 430060, Wuhan, China
Jia Jin
Affiliation:
Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education), 201620, Shanghai, China School of Business and Management, Shanghai International Studies University, 201620, Shanghai, China Joint Lab of Finance and Business Intelligence, Guangdong Institute of Intelligence Science and Technology, 519031, Zhuhai, China
Richard P. Ebstein
Affiliation:
China Center for Behavioral Economics and Finance, Southwestern University of Finance & Economics, 611130, Chengdu, China
Ting Xu
Affiliation:
School of Business, University of Ningbo, 315210, Ningbo, China
Yiquan Wang*
Affiliation:
Hangzhou Mental Health Center of Children and Adolescents, Hangzhou Seventh People's Hospital, 310006, Hangzhou, China
Jun Feng*
Affiliation:
School of Economics, Hefei University of Technology, 230601, Hefei, China
*
Corresponding author: Yiquan Wang; Email: wangyiquan1978@126.com; Jun Feng; Email: cb8226@hotmail.com
Corresponding author: Yiquan Wang; Email: wangyiquan1978@126.com; Jun Feng; Email: cb8226@hotmail.com

Abstract

Background

The incidence of adolescent depressive disorder is globally skyrocketing in recent decades, albeit the causes and the decision deficits depression incurs has yet to be well-examined. With an instrumental learning task, the aim of the current study is to investigate the extent to which learning behavior deviates from that observed in healthy adolescent controls and track the underlying mechanistic channel for such a deviation.

Methods

We recruited a group of adolescents with major depression and age-matched healthy control subjects to carry out the learning task with either gain or loss outcome and applied a reinforcement learning model that dissociates valence (positive v. negative) of reward prediction error and selection (chosen v. unchosen).

Results

The results demonstrated that adolescent depressive patients performed significantly less well than the control group. Learning rates suggested that the optimistic bias that overall characterizes healthy adolescent subjects was absent for the depressive adolescent patients. Moreover, depressed adolescents exhibited an increased pessimistic bias for the counterfactual outcome. Lastly, individual difference analysis suggested that these observed biases, which significantly deviated from that observed in normal controls, were linked with the severity of depressive symoptoms as measured by HAMD scores.

Conclusions

By leveraging an incentivized instrumental learning task with computational modeling within a reinforcement learning framework, the current study reveals a mechanistic decision-making deficit in adolescent depressive disorder. These findings, which have implications for the identification of behavioral markers in depression, could support the clinical evaluation, including both diagnosis and prognosis of this disorder.

Type
Original Article
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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Footnotes

*

These authors contributed equally to this work.

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