Computational Models of Anxiety: Nascent Efforts and Future Directions

Paul B. Sharp*, Eran Eldar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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.

Original languageAmerican English
Pages (from-to)170-176
Number of pages7
JournalCurrent Directions in Psychological Science
Volume28
Issue number2
DOIs
StatePublished - 1 Apr 2019

Bibliographical note

Publisher Copyright:
© The Author(s) 2019.

Keywords

  • anxiety
  • computational psychiatry
  • decision making
  • quantitative theories

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