Evidence map›Paper›PMID 42447162›Full record

Observational studyPLoS computational biology2026

Early reduction in aversive Pavlovian bias as a mediator of anhedonia improvement during Behavioural Activation in realistic treatment settings.

Lioba C S Berndt, Daisy Crawley, Ruslana Tymchyk, Anna Hall, Jakub Onysk, Tore Erdmann, Elliott Wimmer, Isabel M Berwian, Agnes Norbury, Quentin J M Huys

Abstract readObservational Study
In one paragraph

Observational study in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Lioba C S BerndtApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Department of Imaging Neuroscience, Queen Square Institute of Neurology, University College London, London, United Kingdom.ORCID https://orcid.org/0009-0006-2455-3031
Daisy CrawleyApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Department of Imaging Neuroscience, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Ruslana TymchykApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Department of Imaging Neuroscience, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Anna HallApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Department of Imaging Neuroscience, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Jakub OnyskApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Department of Imaging Neuroscience, Queen Square Institute of Neurology, University College London, London, United Kingdom.ORCID https://orcid.org/0000-0003-0876-5465
Tore ErdmannApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Department of Imaging Neuroscience, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Elliott WimmerMax Planck UCL Centre for Computational Psychiatry and Ageing Research, Department of Imaging Neuroscience, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Isabel M BerwianResearch Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom.
Agnes NorburyApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Department of Imaging Neuroscience, Queen Square Institute of Neurology, University College London, London, United Kingdom.
Quentin J M HuysApplied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Department of Imaging Neuroscience, Queen Square Institute of Neurology, University College London, London, United Kingdom.

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundBehavioral Activation therapy is an effective treatment for major depressive disorder. Conceptually, the mechanisms through which it acts are thought to involve alterations to reinforcement learning, and several recent studies have supported this in laboratory settings. However, it remains unclear whether reinforcement learning mechanisms are involved in a realistic treatment setting.

methodsIn a randomized controlled observational study in the UK NHS talking Therapy service, 152 participants with low mood received reinforcement learning assessments using an affective Go/No-Go task. The assessment timepoints were randomized between subjects, with assessments occurring either before (control; n = 78) or during BA therapy (active; n = 74). Changes in Pavlovian biases were quantified using computational modeling.

resultsAnhedonia improved during treatment, but not before (p = 0.047; d = 0.53). The active treatment group showed significant changes in Pavlovian parameters compared to controls (pFDR = 0.012; d = 0.65). Pavlovian biases became more positive in the active group (M = 0.44, SD = 0.81) and more negative in the control group ([Formula: see text], SD = 0.70). Changes in aversive Pavlovian bias statistically mediated treatment effects on symptoms of anhedonia ([Formula: see text], p = 0.036, bootstrap; p = 0.0280).

conclusionsBehavioral Activation modified Pavlovian biases early in treatment by simultaneously strengthening appetitive and diminishing aversive responses. Reduction in aversive bias statistically mediated improvements in anhedonia, suggesting a specific cognitive mechanism through which Behavioral Activation exerts its therapeutic effects. The study provides evidence that online measurements of cognitive function may have potential as mechanistic biomarkers.

Indexed as

AnhedoniaBehavior TherapyConditioning, ClassicalMajor Depressive DisorderAdultFemaleHumansMaleReinforcement, Psychology

Identifiers

PMID42447162
PMCPMC13387611

What OpenQuestion holds

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Registered trials

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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.