Computational Models of Anxiety: Nascent Efforts and Future Directions. (April 2019)

• Record Type: Journal Article
• Authors: Sharp, Paul B.; Eldar, Eran
• Is Part Of: Current directions in psychological science. Volume 28:Number 2(2019)
• Page Start: 170
• Page End: 176
• Publication Date: 2019-04
• Subjects: computational psychiatry -- anxiety -- quantitative theories -- decision making; Psychology -- Periodicals; 150
• DOI: 10.1177/0963721418818441
• Languages: English
• ISSNs: 0963-7214

Abstract

Computational approaches to understanding the algorithms of the mind are just beginning to pervade the field of clinical psychology. In the present article, we seek to explain in simple terms why this approach is indispensable to pursuing explanations of psychological phenomena broadly, and we review nascent efforts to use this lens to understand anxiety. We conclude with future directions that will be required to advance algorithmic accounts of anxiety. Ultimately, the surplus explanatory value of computational models of anxiety, above and beyond existing neurobiological models of anxiety, impugns the naively reductionist claim that neurobiological models are sufficient to explain anxiety.