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 Therjournal_title
Behaviour research and therapyauthors
Krypotos AM,Crombez G,Meulders A,Claes N,Vlaeyen JWSdoi
10.1016/j.brat.2020.103712subject
Has Abstractpub_date
2020-08-15 00:00:00pages
103712eissn
0005-7967issn
1873-622Xpii
S0005-7967(20)30166-2journal_volume
133pub_type
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