ArticleComputational psychiatry (Cambridge, Mass.)2018
Computation in Psychotherapy, or How Computational Psychiatry Can Aid Learning-Based Psychological Therapies.
Article in Computational psychiatry (Cambridge, Mass.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed.
- Trial
- 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
- Prefrontal and parieto-occipital neural signatures of evidence accumulation and response to computerised Cognitive Behavioural Therapy in depression.Npj mental health research · 2025Article
- How mood shapes belief updating bias in depression.Cognitive, affective & behavioral neuroscience · 2025Review
- Using computational models of learning to advance cognitive behavioral therapy.Communications psychology · 2025Review
- Review
- Lack of optimistic bias during social evaluation learning reflects reduced positive self-beliefs in depression and social anxiety, but via distinct mechanisms.Scientific reports · 2024Article
- Understanding Cognitive Behavioral Therapy for Psychosis Through the Predictive Coding Framework.Biological psychiatry global open science · 2024Review
- Different components of cognitive-behavioral therapy affect specific cognitive mechanisms.Science advances · 2024Article
- Neural Response to Expecting a Caloric Sweet Taste Stimulus Predicts Body Mass Index Longitudinally Among Young Adult Women With Anorexia Nervosa.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2024Article
- Computational Psychotherapy System for Mental Health Prediction and Behavior Change with a Conversational Agent.Neuropsychiatric disease and treatment · 2024Article
- 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
- A neuro-computational social learning framework to facilitate transdiagnostic classification and treatment across psychiatric disorders.Neuroscience and biobehavioral reviews · 2023Review
- Latent cause inference during extinction learning in trauma-exposed individuals with and without PTSD.Psychological medicine · 2022Article
- Belief Updating and Paranoia in Individuals With Schizophrenia.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2022Article
- Computational Methods in Psychotherapy: A Scoping Review.International journal of environmental research and public health · 2022Article
- Altered predictive control during memory suppression in PTSD.Nature communications · 2022Article
- Self-esteem depends on beliefs about the rate of change of social approval.Scientific reports · 2022Article
- Computational Psychiatry Needs Time and Context.Annual review of psychology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Learning-based therapies, such as cognitive-behavioral therapy, are used worldwide, and their efficacy is endorsed by health and research funding agencies. However, the mechanisms behind both their strengths and their weaknesses are inadequately understood. Here we describe how advances in computational modeling may help formalize and test hypotheses regarding how patients make inferences, which are core postulates of these therapies. Specifically, we highlight the relevance of computations with regard to the development, maintenance, and therapeutic change in psychiatric disorders. A Bayesian approach helps delineate which apparent inferential biases and aberrant beliefs are in fact near-normative, given patients' current concerns, and which are not. As examples, we formalize three hypotheses. First, high-level dysfunctional beliefs should be treated as beliefs over models of the world. There is a need to test how, and whether, people apply these high-level beliefs to guide the formation of lower level beliefs important for real-life decision making, conditional on their experiences. Second, during the genesis of a disorder, maladaptive beliefs grow because more benign alternative schemas are discounted during belief updating. Third, we propose that when patients learn within therapy but fail to benefit in real life, this can be accounted for by a mechanism that we term overaccommodation, similar to that used to explain fear reinstatement. Beyond these specifics, an ambitious collaborative research program between computational psychiatry researchers, therapists, and experts-by-experience needs to form testable predictions out of factors claimed to be important for therapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.