TY - JOUR
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 - http://www.scopus.com/inward/record.url?scp=85135864617&partnerID=8YFLogxK
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 -
