TY - JOUR
T1 - Computational Models of Anxiety
T2 - Nascent Efforts and Future Directions
AU - Sharp, Paul B.
AU - Eldar, Eran
N1 - Publisher Copyright:
© The Author(s) 2019.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
KW - anxiety
KW - computational psychiatry
KW - decision making
KW - quantitative theories
UR - http://www.scopus.com/inward/record.url?scp=85061728908&partnerID=8YFLogxK
U2 - 10.1177/0963721418818441
DO - 10.1177/0963721418818441
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AN - SCOPUS:85061728908
SN - 0963-7214
VL - 28
SP - 170
EP - 176
JO - Current Directions in Psychological Science
JF - Current Directions in Psychological Science
IS - 2
ER -