Using computational models of learning to advance cognitive behavioral therapy
Open accessAbstract
Many psychotherapy interventions have a large evidence base and can help a substantial number of people with symptoms of mental health conditions. However, we still have little understanding of why treatments work. Early advances in psychotherapy, such as the development of exposure therapy, built on theoretical and experimental evidence from Pavlovian and instrumental conditioning. More generally, all psychotherapy achieves change through learning. The past 25 years have seen substantial developments in computational models of learning, with increased computational precision and a focus on multiple learning mechanisms and their interaction. Now might be a good time to formalize psychotherapy interventions as computational models of learning to improve our understanding of mechanisms of change in psychotherapy. To advance research and help bring together a new joint field of theory-driven computational psychotherapy, we first review literature on cognitive behavioral therapy (exposure therapy and cognitive restructuring) and introduce computational models of reinforcement learning and representation learning. We then suggest a mapping of these learning algorithms on change processes presumably underlying the effects of exposure therapy and cognitive restructuring. Finally, we outline how the understanding of interventions through the lens of learning algorithms can inform intervention research.
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Human behavior is currently considered as the result of interaction of two learning lines: model-free reinforcement learning and model-based learning.
“В настоящее время поведение человека рассматривается как результат взаимодействия двух линий обучения -обучение с подкреплением без модели (model-free), которое опирается на накопление наблюдаемой обратной связи об исходах взаимодействия со средой, и обучение с моделью (model-based), при котором человек активно проверяет свои гипотезы о внутреннем устройстве среды и делает выводы о скрытых причинн”
From Когнитивные механизмы дисфункционального принятия решений при психических расстройствах (2025) · cites this paper as Berwian et al., 2025
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Williams, L., Bomyea, J. A., Howlett, J. R., Harlé, K. M., & Stout, D. M. (2026) · Journal of Mood and Anxiety Disorders · doi:10.1016/j.xjmad.2026.100206
Computational modelling of decision-making in suicide attemptersPreprint
Grandé, C., Zorzi, L. D., Abbar, M., Wagner, G., & Jollant, F. (2026) · medRxiv · doi:10.64898/2026.10.09.26365056
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Advances in the Neurobiological Mechanisms and Treatment of Post-Traumatic Stress Disorder
Zhao, S., Wu, Z., Liu, H., Li, X., Yang, Y., & Shi, X. (2026) · SN Comprehensive Clinical Medicine · doi:10.1007/s42399-026-02834-6
- Authors
- Berwian, I. M., Hitchock, P., Pisupati, S., Schoen, G., & Niv, Y.
- Affiliations
- Princeton University (US)
- Emory University (US)
- Geha Mental Health Center (IL)
- Corresponding author
- Berwian, Isabel M.
- Venue
- Communications Psychology
- Published
- 2025-04-27
- Volume
- 3
- Issue
- 1
- Pages
- 72-72
- ISSN
- 2731-9121 (ISSN-L)
- PMC
- PMC12034757
- OpenAlex
- W4409849574
- Cited by
- 16
- References
- 91
- Citations by year
- 2026: 10 · 2025: 4 · 2024: 1 · 2023: 1
- FWCI
- 2.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Communications Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- Funders
- Multi-Channel Psych Program, Wellcome Leap
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- computational models of learning, cognitive behavioral therapy, mechanisms of change, exposure therapy, cognitive restructuring, reinforcement learning, representation learning, psychotherapeutic interventions, learning algorithms
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