Computational Modelling for Alcohol Use Disorder

Matteo Colombo

doi:10.1007/s10670-022-00533-x

Computational Modelling for Alcohol Use Disorder

Matteo Colombo^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Scientific › peer-review

2 Citations (Scopus)

Abstract

In this paper, I examine Reinforcement Learning (RL) modelling practice in psychiatry, in the context of alcohol use disorders. I argue that the epistemic roles RL currently plays in the development of psychiatric classification and search for explanations of clinically relevant phenomena are best appreciated in terms of Chang's (2004) account of epistemic iteration, and by distinguishing mechanistic and aetiological modes of computational explanation.

Original language	English
Pages (from-to)	271-291
Number of pages	21
Journal	Erkenntnis
Volume	89
Early online date	10 Aug 2022
DOIs	https://doi.org/10.1007/s10670-022-00533-x
Publication status	Published - 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Alcohol use disorders
Alcohol-avoidance training
Reinforcement learning
Computational modelling
Psychiatric classification
Psychiatric explanation

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10.1007/s10670-022-00533-x

Cite this

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title = "Computational Modelling for Alcohol Use Disorder",

abstract = "In this paper, I examine Reinforcement Learning (RL) modelling practice in psychiatry, in the context of alcohol use disorders. I argue that the epistemic roles RL currently plays in the development of psychiatric classification and search for explanations of clinically relevant phenomena are best appreciated in terms of Chang's (2004) account of epistemic iteration, and by distinguishing mechanistic and aetiological modes of computational explanation.",

keywords = "Alcohol use disorders, Alcohol-avoidance training, Reinforcement learning, Computational modelling, Psychiatric classification, Psychiatric explanation",

author = "Matteo Colombo",

note = "Funding Information: I am grateful to Ke Chen, Andreas Heinz, Miriam Sebold and two anonymous reviewers for useful discussion and their generous comments on previous versions of this paper. This work was supported by the Alexander von Humboldt Foundation through a Humboldt Research Fellowship for Experienced Researchers at the Department of Psychiatry and Psychotherapy, at the Charit{\'e} University Clinic in Berlin. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2024",

doi = "10.1007/s10670-022-00533-x",

language = "English",

volume = "89",

pages = "271--291",

journal = "Erkenntnis",

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T1 - Computational Modelling for Alcohol Use Disorder

AU - Colombo, Matteo

N1 - Funding Information: I am grateful to Ke Chen, Andreas Heinz, Miriam Sebold and two anonymous reviewers for useful discussion and their generous comments on previous versions of this paper. This work was supported by the Alexander von Humboldt Foundation through a Humboldt Research Fellowship for Experienced Researchers at the Department of Psychiatry and Psychotherapy, at the Charité University Clinic in Berlin. Publisher Copyright: © 2022, The Author(s).

PY - 2024

Y1 - 2024

N2 - In this paper, I examine Reinforcement Learning (RL) modelling practice in psychiatry, in the context of alcohol use disorders. I argue that the epistemic roles RL currently plays in the development of psychiatric classification and search for explanations of clinically relevant phenomena are best appreciated in terms of Chang's (2004) account of epistemic iteration, and by distinguishing mechanistic and aetiological modes of computational explanation.

AB - In this paper, I examine Reinforcement Learning (RL) modelling practice in psychiatry, in the context of alcohol use disorders. I argue that the epistemic roles RL currently plays in the development of psychiatric classification and search for explanations of clinically relevant phenomena are best appreciated in terms of Chang's (2004) account of epistemic iteration, and by distinguishing mechanistic and aetiological modes of computational explanation.

KW - Alcohol use disorders

KW - Alcohol-avoidance training

KW - Reinforcement learning

KW - Computational modelling

KW - Psychiatric classification

KW - Psychiatric explanation

UR - https://www.scopus.com/pages/publications/85135864617

U2 - 10.1007/s10670-022-00533-x

DO - 10.1007/s10670-022-00533-x

M3 - Article

SN - 0165-0106

VL - 89

SP - 271

EP - 291

JO - Erkenntnis

JF - Erkenntnis

ER -

Computational Modelling for Alcohol Use Disorder

Abstract

UN SDGs

Keywords

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