Thinking computationally in translational psychiatry. A commentary on Neville et al. (2024)

There is a growing focus on the computational aspects of psychiatric disorders in humans. This idea also is gaining traction in nonhuman animal studies. Commenting on a new comprehensive overview of the benefits of applying this approach in translational research by Neville et al. ( Cognitive Affect...

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Bibliographic Details
Published in:Cognitive, affective, & behavioral neuroscience Vol. 24; no. 2; pp. 384 - 387
Main Authors: Yamamori, Yumeya, Robinson, Oliver J.
Format: Journal Article
Language:English
Published: New York Springer US 01.04.2024
Springer Nature B.V
Subjects:
Animal behavior
Animal cognition
Animals
Anxiety
Behavioral Science and Psychology
Clinical trials
Cognitive Psychology
Decision making
Disease Models, Animal
Exploratory behavior
Humans
Locomotor activity
Mental Disorders
Neurosciences
Open-field behavior
Pessimism
Psychiatry
Psychiatry - methods
Psychiatry - trends
Psychology
Reinforcement, Psychology
Special Issue/Preclinical Assays
Thinking - physiology
Translation
Translational Research, Biomedical - methods
Validity
Validity
Translational
Psychiatry
Computational
Reinforcement learning
ISSN:1530-7026, 1531-135X, 1531-135X
Online Access:Get full text
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Summary:There is a growing focus on the computational aspects of psychiatric disorders in humans. This idea also is gaining traction in nonhuman animal studies. Commenting on a new comprehensive overview of the benefits of applying this approach in translational research by Neville et al. ( Cognitive Affective & Behavioral Neuroscience 1–14, 2024 ), we discuss the implications for translational model validity within this framework. We argue that thinking computationally in translational psychiatry calls for a change in the way that we evaluate animal models of human psychiatric processes, with a shift in focus towards symptom-producing computations rather than the symptoms themselves. Further, in line with Neville et al.'s adoption of the reinforcement learning framework to model animal behaviour, we illustrate how this approach can be applied beyond simple decision-making paradigms to model more naturalistic behaviours.
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ISSN:1530-7026
1531-135X
1531-135X
DOI:10.3758/s13415-024-01172-1