Academic Journal
cpm: A python library for theory-driven modelling in computational psychiatry.
| Τίτλος: | cpm: A python library for theory-driven modelling in computational psychiatry. |
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| Συγγραφείς: | Dome, Lenard, Hezemans, Frank H., Kadri, Kenza, Wagner, Ben J., Webb, Andrew, Hauser, Tobias U. |
| Πηγή: | PLoS Computational Biology; 7/13/2026, Vol. 22 Issue 7, p1-31, 31p |
| Θεματικοί όροι: | Reinforcement learning, Bayesian analysis, Python programming language, Signal detection, Cognitive neuroscience, Hierarchical Bayes model, Computer simulation |
| Περίληψη: | The Computational Psychiatry Modelling (cpm) toolbox is a Python library for theory-driven modelling in computational psychiatry and cognitive (neuro-)science. cpm integrates a wide range of established approaches into a single framework. It is designed to be accessible to both expert and non-expert modellers in order to conduct cutting-edge computational modelling, while adhering to best practices. The toolbox provides a flexible, modular architecture that adjusts to different needs. It covers a wide range of problems (such as risky decision-making, reward/punishment learning, perceptual metacognition), models (including those based on associative, reinforcement learning, and signal detection theories), and methods (such as hierarchical parameter estimation using empirical and variational Bayesian techniques). Such a customisable toolbox aims to lower the barrier for beginners and to facilitate access to advanced modelling approaches in psychiatry and beyond. Author summary: Computational psychiatry is a field that makes extensive use of computational methods to understand mental health. In this field and related disciplines, there is a strong need to design, implement, and apply mathematical models of behaviour and cognition (such as reinforcement learning or Bayesian models). However, coding these models from scratch poses a significant challenge for researchers without extensive computational training, including clinicians and other stakeholders who are interested in understanding the underlying principles of mental disorders. Here, we describe the cpm toolbox, providing a step-by-step walkthrough of the current design, alongside links to related resources. We believe that building such an overarching, customisable, and user-friendly toolbox can facilitate access to computational modelling, and thus jump-start computational approaches in the field and beyond. [ABSTRACT FROM AUTHOR] |
| Copyright of PLoS Computational Biology is the property of Public Library of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=1553734X&ISBN=&volume=22&issue=7&date=20260713&spage=1&pages=1-31&title=PLoS Computational Biology&atitle=cpm%3A%20A%20python%20library%20for%20theory-driven%20modelling%20in%20computational%20psychiatry.&aulast=Dome%2C%20Lenard&id=DOI:10.1371/journal.pcbi.1014481 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: cpm: A python library for theory-driven modelling in computational psychiatry. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dome%2C+Lenard%22">Dome, Lenard</searchLink><br /><searchLink fieldCode="AR" term="%22Hezemans%2C+Frank+H%2E%22">Hezemans, Frank H.</searchLink><br /><searchLink fieldCode="AR" term="%22Kadri%2C+Kenza%22">Kadri, Kenza</searchLink><br /><searchLink fieldCode="AR" term="%22Wagner%2C+Ben+J%2E%22">Wagner, Ben J.</searchLink><br /><searchLink fieldCode="AR" term="%22Webb%2C+Andrew%22">Webb, Andrew</searchLink><br /><searchLink fieldCode="AR" term="%22Hauser%2C+Tobias+U%2E%22">Hauser, Tobias U.</searchLink> – Name: TitleSource Label: Source Group: Src Data: PLoS Computational Biology; 7/13/2026, Vol. 22 Issue 7, p1-31, 31p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+detection%22">Signal detection</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+neuroscience%22">Cognitive neuroscience</searchLink><br /><searchLink fieldCode="DE" term="%22Hierarchical+Bayes+model%22">Hierarchical Bayes model</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The Computational Psychiatry Modelling (cpm) toolbox is a Python library for theory-driven modelling in computational psychiatry and cognitive (neuro-)science. cpm integrates a wide range of established approaches into a single framework. It is designed to be accessible to both expert and non-expert modellers in order to conduct cutting-edge computational modelling, while adhering to best practices. The toolbox provides a flexible, modular architecture that adjusts to different needs. It covers a wide range of problems (such as risky decision-making, reward/punishment learning, perceptual metacognition), models (including those based on associative, reinforcement learning, and signal detection theories), and methods (such as hierarchical parameter estimation using empirical and variational Bayesian techniques). Such a customisable toolbox aims to lower the barrier for beginners and to facilitate access to advanced modelling approaches in psychiatry and beyond. Author summary: Computational psychiatry is a field that makes extensive use of computational methods to understand mental health. In this field and related disciplines, there is a strong need to design, implement, and apply mathematical models of behaviour and cognition (such as reinforcement learning or Bayesian models). However, coding these models from scratch poses a significant challenge for researchers without extensive computational training, including clinicians and other stakeholders who are interested in understanding the underlying principles of mental disorders. Here, we describe the cpm toolbox, providing a step-by-step walkthrough of the current design, alongside links to related resources. We believe that building such an overarching, customisable, and user-friendly toolbox can facilitate access to computational modelling, and thus jump-start computational approaches in the field and beyond. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of PLoS Computational Biology is the property of Public Library of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1371/journal.pcbi.1014481 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 1 Subjects: – SubjectFull: Reinforcement learning Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Python programming language Type: general – SubjectFull: Signal detection Type: general – SubjectFull: Cognitive neuroscience Type: general – SubjectFull: Hierarchical Bayes model Type: general – SubjectFull: Computer simulation Type: general Titles: – TitleFull: cpm: A python library for theory-driven modelling in computational psychiatry. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dome, Lenard – PersonEntity: Name: NameFull: Hezemans, Frank H. – PersonEntity: Name: NameFull: Kadri, Kenza – PersonEntity: Name: NameFull: Wagner, Ben J. – PersonEntity: Name: NameFull: Webb, Andrew – PersonEntity: Name: NameFull: Hauser, Tobias U. IsPartOfRelationships: – BibEntity: Dates: – D: 13 M: 07 Text: 7/13/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1553734X Numbering: – Type: volume Value: 22 – Type: issue Value: 7 Titles: – TitleFull: PLoS Computational Biology Type: main |
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