Decomposing conditioned avoidance performance with computational models.

Abstract:

:Avoidance towards innocuous stimuli is a key characteristic across anxiety-related disorders and chronic pain. Insights into the relevant learning processes of avoidance are often gained via laboratory procedures, where individuals learn to avoid stimuli or movements that have been previously associated with an aversive stimulus. Typically, statistical analyses of data gathered with conditioned avoidance procedures include frequency data, for example, the number of times a participant has avoided an aversive stimulus. Here, we argue that further insights into the underlying processes of avoidance behavior could be unraveled using computational models of behavior. We then demonstrate how computational models could be used by reanalysing a previously published avoidance data set and interpreting the key findings. We conclude our article by listing some challenges in the direct application of computational modeling to avoidance data sets.

journal_name

Behav Res Ther

authors

Krypotos AM,Crombez G,Meulders A,Claes N,Vlaeyen JWS

doi

10.1016/j.brat.2020.103712

subject

Has Abstract

pub_date

2020-08-15 00:00:00

pages

103712

eissn

0005-7967

issn

1873-622X

pii

S0005-7967(20)30166-2

journal_volume

133

pub_type

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