ArticleFrontiers in psychiatry2020
Under the Hood: Using Computational Psychiatry to Make Psychological Therapies More Mechanism-Focused.
Article in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- PAD-S/CSA as a candidate shared representation layer for computational psychotherapy: minimal architecture and a staged validation roadmap.Frontiers in psychiatry · 2026Article
- Perceive-Assess-Dose-Safeguard: a safety-gated state-action grammar for psychotherapy micro-decisions in computational psychiatry.Frontiers in psychiatry · 2026Article
- Attenuated learning rates for negative outcomes in substance use disorders: A replication and extension of prior longitudinal computational modeling results.Drug and alcohol dependence · 2026Article
- Prefrontal and parieto-occipital neural signatures of evidence accumulation and response to computerised Cognitive Behavioural Therapy in depression.Npj mental health research · 2025Article
- Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders.Frontiers in psychiatry · 2025Article
- Changes in Learning From Social Feedback After Web-Based Interpretation Bias Modification: Secondary Analysis of a Digital Mental Health Intervention Among Individuals With High Social Anxiety Symptoms.JMIR formative research · 2023Article
- From Computation to Clinic.Biological psychiatry global open science · 2023Review
- Computational models of subjective feelings in psychiatry.Neuroscience and biobehavioral reviews · 2023Review
- Reliability of Decision-Making and Reinforcement Learning Computational Parameters.Computational psychiatry (Cambridge, Mass.) · 2023Article
- Disordered Decision Making: A Cognitive Framework for Apathy and Impulsivity in Huntington's Disease.Movement disorders : official journal of the Movement Disorder Society · 2022Review
- Self-esteem depends on beliefs about the rate of change of social approval.Scientific reports · 2022Article
- Slower Learning Rates from Negative Outcomes in Substance Use Disorder over a 1-Year Period and Their Potential Predictive Utility.Computational psychiatry (Cambridge, Mass.) · 2022Article
- A Computational Model of Hopelessness and Active-Escape Bias in Suicidality.Computational psychiatry (Cambridge, Mass.) · 2022Article
- Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders.Frontiers in psychiatry · 2022Review
- Tracking and changing beliefs during social interaction: Where computational psychiatry meets cognitive behavioral therapy.Frontiers in psychology · 2022Article
- Acute stress blunts prediction error signals in the dorsal striatum during reinforcement learning.Neurobiology of stress · 2021Article
- Regenerative Virtual Therapy: The Use of Multisensory Technologies and Mindful Attention for Updating the Altered Representations of the Bodily Self.Frontiers in systems neuroscience · 2021Article
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3 authors.
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Abstract
Psychological therapies, such as CBT, are an important part of the treatment of a range of psychiatric disorders such as depression and anxiety. There is a growing desire to understand the mechanisms by which such therapies effect change so as to improve treatment outcomes. Here we argue that adopting a computational framework may be one such approach. Computational psychiatry aims to provide a theoretical framework for moving between higher-level psychological states (like emotions, decisions and beliefs) to neural circuits, by modeling these constructs mathematically. These models are explicit hypotheses that contain quantifiable variables and parameters derived from each individual's behavior. This approach has two advantages. Firstly, some of the variables described by these models appears to reflect the neural activity of specific brain regions. Secondly, the parameters estimated by these models may offer a unique description of a patient's symptoms which can be used to both tailor therapy and track its effect. In doing so this approach may offer some additional granularity in understanding how psychological therapies, such as CBT, are working. Although this field shows significant promise, we also highlight several of the key hurdles that must first be overcome before clinical translation of computational insights can be realized.
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