Modeling trait anxiety: from computational processes to personality

James G. Raymond, J. Douglas Steele, Peggy Seriès (Lead / Corresponding author)

    Research output: Contribution to journalReview articlepeer-review

    37 Citations (Scopus)
    245 Downloads (Pure)


    Computational methods are increasingly being applied to the study of psychiatric disorders. Often, this involves fitting models to the behavior of individuals with subclinical character traits which are known vulnerability factors for the development of psychiatric conditions. Anxiety disorders can be examined with reference to the behavior of individuals high in ‘trait’ anxiety, which is a known vulnerability factor for the development of anxiety and mood disorders. However, it is not clear how this self-report measure relates to neural and behavioral processes captured by computational models. This paper reviews emerging computational approaches to the study of trait anxiety, specifying how interacting processes susceptible to analysis using computational models could drive a tendency to experience frequent anxious states and promote vulnerability to the development of clinical disorders. Existing computational
    studies are described in the light of this perspective and appropriate targets for future studies are discussed.
    Original languageEnglish
    Article number1
    Pages (from-to)1-19
    Number of pages19
    JournalFrontiers in Psychiatry
    Publication statusPublished - 23 Jan 2017


    • anxiety
    • trait anxiety
    • anxiety disorders
    • computational modeling
    • associative learning
    • avoidance
    • attentional control


    Dive into the research topics of 'Modeling trait anxiety: from computational processes to personality'. Together they form a unique fingerprint.

    Cite this