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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Cognitive, affective, & behavioral neuroscience Ročník 24; číslo 2; s. 384 - 387
Hlavní autori:	Yamamori, Yumeya, Robinson, Oliver J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.04.2024 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1530-7026 1531-135X 1531-135X
DOI:	10.3758/s13415-024-01172-1