Towards formal models of psychopathological traits that explain symptom trajectories

Paul B. Sharp*, Gregory A. Miller, Raymond J. Dolan, Eran Eldar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageAmerican English
Article number264
JournalBMC Medicine
Issue number1
StatePublished - 28 Sep 2020

Bibliographical note

Publisher Copyright:
© 2020 The Author(s).


  • Bayesian inference
  • Computational modeling
  • Psychiatric traits
  • Self-report


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