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Barriers and solutions to the adoption of translational tools for computational psychiatry

Computational psychiatry is a field aimed at developing formal models of information processing in the human brain, and how alterations in this processing can lead to clinical phenomena. There has been significant progress in the development of tasks and how to model them, presenting an opportunity...

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Autores principales: Benrimoh, David, Fisher, Victoria, Mourgues, Catalina, Sheldon, Andrew D., Smith, Ryan, Powers, Albert R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611570/
https://www.ncbi.nlm.nih.gov/pubmed/37280282
http://dx.doi.org/10.1038/s41380-023-02114-y
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author Benrimoh, David
Fisher, Victoria
Mourgues, Catalina
Sheldon, Andrew D.
Smith, Ryan
Powers, Albert R.
author_facet Benrimoh, David
Fisher, Victoria
Mourgues, Catalina
Sheldon, Andrew D.
Smith, Ryan
Powers, Albert R.
author_sort Benrimoh, David
collection PubMed
description Computational psychiatry is a field aimed at developing formal models of information processing in the human brain, and how alterations in this processing can lead to clinical phenomena. There has been significant progress in the development of tasks and how to model them, presenting an opportunity to incorporate computational psychiatry methodologies into large- scale research projects or into clinical practice. In this viewpoint, we explore some of the barriers to incorporation of computational psychiatry tasks and models into wider mainstream research directions. These barriers include the time required for participants to complete tasks, test-retest reliability, limited ecological validity, as well as practical concerns, such as lack of computational expertise and the expense and large sample sizes traditionally required to validate tasks and models. We then discuss solutions, such as the redesigning of tasks with a view toward feasibility, and the integration of tasks into more ecologically valid and standardized game platforms that can be more easily disseminated. Finally, we provide an example of how one task, the conditioned hallucinations task, might be translated into such a game. It is our hope that interest in the creation of more accessible and feasible computational tasks will help computational methods make more positive impacts on research as well as, eventually, clinical practice.
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spelling pubmed-106115702023-10-29 Barriers and solutions to the adoption of translational tools for computational psychiatry Benrimoh, David Fisher, Victoria Mourgues, Catalina Sheldon, Andrew D. Smith, Ryan Powers, Albert R. Mol Psychiatry Perspective Computational psychiatry is a field aimed at developing formal models of information processing in the human brain, and how alterations in this processing can lead to clinical phenomena. There has been significant progress in the development of tasks and how to model them, presenting an opportunity to incorporate computational psychiatry methodologies into large- scale research projects or into clinical practice. In this viewpoint, we explore some of the barriers to incorporation of computational psychiatry tasks and models into wider mainstream research directions. These barriers include the time required for participants to complete tasks, test-retest reliability, limited ecological validity, as well as practical concerns, such as lack of computational expertise and the expense and large sample sizes traditionally required to validate tasks and models. We then discuss solutions, such as the redesigning of tasks with a view toward feasibility, and the integration of tasks into more ecologically valid and standardized game platforms that can be more easily disseminated. Finally, we provide an example of how one task, the conditioned hallucinations task, might be translated into such a game. It is our hope that interest in the creation of more accessible and feasible computational tasks will help computational methods make more positive impacts on research as well as, eventually, clinical practice. Nature Publishing Group UK 2023-06-06 2023 /pmc/articles/PMC10611570/ /pubmed/37280282 http://dx.doi.org/10.1038/s41380-023-02114-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Perspective
Benrimoh, David
Fisher, Victoria
Mourgues, Catalina
Sheldon, Andrew D.
Smith, Ryan
Powers, Albert R.
Barriers and solutions to the adoption of translational tools for computational psychiatry
title Barriers and solutions to the adoption of translational tools for computational psychiatry
title_full Barriers and solutions to the adoption of translational tools for computational psychiatry
title_fullStr Barriers and solutions to the adoption of translational tools for computational psychiatry
title_full_unstemmed Barriers and solutions to the adoption of translational tools for computational psychiatry
title_short Barriers and solutions to the adoption of translational tools for computational psychiatry
title_sort barriers and solutions to the adoption of translational tools for computational psychiatry
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611570/
https://www.ncbi.nlm.nih.gov/pubmed/37280282
http://dx.doi.org/10.1038/s41380-023-02114-y
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