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. 2024 Jan 12.
doi: 10.1111/bjop.12693. Online ahead of print.

Understanding anxiety through uncertainty quantification

Friederike Elisabeth Hedley  1 , Emmett Larsen  2 , Aprajita Mohanty  2 , Jeremiah Zhe Liu  3   4 , Jingwen Jin  1   5
Affiliations

Understanding anxiety through uncertainty quantification

Friederike Elisabeth Hedley et al. Br J Psychol. .

Abstract

Uncertainty has been a central concept in psychological theories of anxiety. However, this concept has been plagued by divergent connotations and operationalizations. The lack of consensus hinders the current search for cognitive and biological mechanisms of anxiety, jeopardizes theory creation and comparison, and restrains translation of basic research into improved diagnoses and interventions. Drawing upon uncertainty decomposition in Bayesian Decision Theory, we propose a well-defined conceptual structure of uncertainty in cognitive and clinical sciences, with a focus on anxiety. We discuss how this conceptual structure provides clarity and can be naturally applied to existing frameworks of psychopathology research. Furthermore, it allows formal quantification of various types of uncertainty that can benefit both research and clinical practice in the era of computational psychiatry.

Keywords: Bayesian theory; anxiety; intolerance of uncertainty; research domain criteria; uncertainty quantification.

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References

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