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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Vydáno v:	Cognitive, affective, & behavioral neuroscience Ročník 24; číslo 2; s. 384 - 387
Hlavní autoři:	Yamamori, Yumeya, Robinson, Oliver J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.04.2024 Springer Nature B.V
Témata:	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
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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.
AbstractList	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. 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.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. 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. 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. [...]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. Conversely, animal behaviour that is outwardly similar to human symptoms may be a result of entirely different computational mechanisms. [...]thinking more computationally, and by extension mechanistically, means reassessing existing translational models to ensure that they are computationally aligned with human psychiatric processes or developing new ones with this explicit intention. Behavioural tests that rely on more naturalistic and ecological behaviours, such as free exploration of a spatial environment, are much easier to set up and run in animals, which is why such tests like the elevated-plus maze or the open field test are more popular preclinical models of anxiety (Griebel & Holmes, 2013). Recent work indicates that it also is possible to model this kind of more naturalistic exploration behaviour by using the RL framework by casting it as a Markov decision process (MDP). [...]one could manipulate the reward signal for exploration to model exploration (approach) drives or adjust the probability of state transitions to reflect general locomotor activity.
Author	Robinson, Oliver J. Yamamori, Yumeya
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Cites_doi	10.1038/nrd4075 10.1162/cpsy_a_00026 10.3758/s13415-023-01137-w 10.1126/scitranslmed.3003142 10.7554/eLife.87720.4 10.1098/rstb.2020.0525
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SubjectTerms	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
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Title	Thinking computationally in translational psychiatry. A commentary on Neville et al. (2024)
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