Thinking computationally in translational psychiatry. A commentary on Neville et al. (2024).
Cogn Affect Behav Neurosci
; 24(2): 384-387, 2024 Apr.
Article
en En
| MEDLINE
| ID: mdl-38459406
ABSTRACT
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
1_ASSA2030
Problema de salud:
1_geracao_evidencia_conhecimento
Asunto principal:
Investigación Biomédica Traslacional
Límite:
Animals
/
Humans
Idioma:
En
Revista:
Cogn Affect Behav Neurosci
Asunto de la revista:
CIENCIAS DO COMPORTAMENTO
/
NEUROLOGIA
Año:
2024
Tipo del documento:
Article