Improving the Reliability of Computational Analyses: Model-Based Planning and Its Relationship With Compulsivity

Brown, Vanessa M.; Chen, J; Gillan, Claire M.; Price, Rebecca B.

doi:10.1016/j.bpsc.2019.12.019

Cited by 79 publications

(157 citation statements)

References 43 publications

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“…These could potentially be addressed by enhancing the number of trials 48 , by combining different behavioural read-outs (e.g. choices, reaction times) in a computational model 49 , or by optimizing model estimation approaches 50,51 . Recent approaches have also combined self-report of subjective well-being or pain with objective measures (task behaviour, fMRI 52,53 ).…”

Section: Limitationsmentioning

confidence: 99%

Neuro-cognitive processes as mediators of psychological treatment effects

Reiter

Atiya

Berwian

et al. 2021

Current Opinion in Behavioral Sciences

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Psychological interventions are first-line treatments of depression. Despite a rich theoretical background, the mediators of treatment effects remain only partially understood: it has been difficult to precisely delineate the targets psychological interventions engage, and even more difficult to differentiate amongst the targets engaged by different psychological interventions. Here, we outline these issues and discuss a surprisingly understudied approach, namely the study of cognitive and computational tasks to measure psychological treatment targets. Such tasks benefit from substantial advances in cognitive neuroscience over the past two decades, and have excellent face validity. We discuss two candidate tasks for backtranslation and conclude with a critical evaluation of potential problems associated with this neuro-cognitive approach. Highlights-Cognitive Behavioural Therapy (CBT) for depression has a rich theoretical background in cognitive and learning theories, but we still know little about the specific mechanisms engaged by different CBT modules.-Neuro-computational probes derived from modern computational cognitive neuroscience are specifically designed to characterise behaviours and cognitions that CBT aims to change-Cognitive computational tasks might be useful tools for research aimed at defining mechanisms and mediators brought about by different CBT modules.-We discuss metacognition as candidate mediators of Cognitive Restructuring, and Reward and Effort-based decision-making as candidate mediators for Behavioural Activation-The psychometric properties of tasks pose obstacles for their clinical use.

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Section: Limitationsmentioning

confidence: 99%

Neuro-cognitive processes as mediators of psychological treatment effects

Reiter

Atiya

Berwian

et al. 2021

Current Opinion in Behavioral Sciences

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“…Another recent example compared the reliability of a range of statistical and modelling approaches in a reinforcement learning task (10). They observed improved reliability when it was calculated within a hierarchical modelling framework, relative to both traditional behaviour and non-hierarchical versions of the same models.…”

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confidence: 99%

Task Reliability Considerations in Computational Psychiatry

Hedge

Bompas

Sumner

2020

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

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“…Furthermore, we suggest using a slightly more reliable model fitting technique for the two step task that utilises hierarchical Bayesian estimation methods, for instance hBayes package (Ahn, Haines, & Zhang, 2017). This approach has recently been shown to provide the most reliable estimation of model parameters for the two step decision-making task (Brown, Chen, Gillan, & Price, 2020).…”

Section: Discussionmentioning

confidence: 99%

The effect of body image dissatisfaction on goal-directed decision making in a population marked by negative appearance beliefs and disordered eating

Onysk

Seriès

2020

Preprint

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Eating disorders are associated with one of the highest mortality rates among all mental disorders, yet there is very little research about them within the newly emerging and promising field of computational psychiatry. As such, we focus on investigating a previously unexplored, yet a core aspect of eating disorders – body image preoccupation. We continue a freshly opened debate about model-based learning in eating disorders and perform a study that utilises a two-step decision-making task and a reinforcement learning model to understand the effect of body image preoccupation on model-based learning in a subclinical eating disorder population, as recruited using Prolific. We find a significantly reduced model-based contribution in the body image disturbance task condition in the eating disorder group as compared to a healthy control. We propose a new digital biomarker that significantly predicts disordered eating, and body image issues.

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Improving the Reliability of Computational Analyses: Model-Based Planning and Its Relationship With Compulsivity

Cited by 79 publications

References 43 publications

Neuro-cognitive processes as mediators of psychological treatment effects

Neuro-cognitive processes as mediators of psychological treatment effects

Task Reliability Considerations in Computational Psychiatry

The effect of body image dissatisfaction on goal-directed decision making in a population marked by negative appearance beliefs and disordered eating

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