Towards formal models of psychopathological traits that explain symptom trajectories
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OPINION
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Towards formal models of psychopathological traits that explain symptom trajectories Paul B. Sharp1,2*, Gregory A. Miller3,4, Raymond J. Dolan1,2 and Eran Eldar5,1
Abstract Background: A dominant methodology in contemporary clinical neuroscience is the use of dimensional self-report questionnaires to measure features such as psychological traits (e.g., trait anxiety) and states (e.g., depressed mood). These dimensions are then mapped to biological measures and computational parameters. Researchers pursuing this approach tend to equate a symptom inventory score (plus noise) with some latent psychological trait. Main text: We argue this approach implies weak, tacit, models of traits that provide fixed predictions of individual symptoms, and thus cannot account for symptom trajectories within individuals. This problem persists because (1) researchers are not familiarized with formal models that relate internal traits to within-subject symptom variation and (2) rely on an assumption that trait self-report inventories accurately indicate latent traits. To address these concerns, we offer a computational model of trait depression that demonstrates how parameters instantiating a given trait remain stable while manifest symptom expression varies predictably. We simulate patterns of mood variation from both the computational model and the standard self-report model and describe how to quantify the relative validity of each model using a Bayesian procedure. Conclusions: Ultimately, we would urge a tempering of a reliance on self-report inventories and recommend a shift towards developing mechanistic trait models that can explain within-subject symptom dynamics. Keywords: Psychiatric traits, Computational modeling, Bayesian inference, Self-report
Background Psychopathology, as conceived dimensionally, is typically assayed in both clinical research and service settings using self-reporting of symptoms. As long as a symptom inventory is shown as reliable in relevant samples and contexts, the instrument is assumed to be sufficient to make inferences that, for instance, an individual has “trait anxiety.” This type of inference commits to a weak, * Correspondence: [email protected] 1 Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK 2 Wellcome Centre for Human Neuroimaging, University College London, London, UK Full list of author information is available at the end of the article
stationary model of that trait: that symptom frequency suffices when denoting an individual is endowed with a given pathological trait. We advance an argument that mechanistic models of what causes symptom fluctuations greatly improve the validity of inferences regarding psychopathological traits by making predictions concerning symptom trajectories that indicate not only whether, but also when, clinical symptoms are likely to occur and recur. Pursuing mechanistic models of psychopathology is a core component of an ongoing effort in the psychological sciences to transiti
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