Decomposing conditioned avoidance performance with computational models

Angelos-Miltiadis Krypotos*, Geert Crombez, Ann Meulders, Nathalie Claes, Johan W S Vlaeyen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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.

Original languageEnglish
Article number103712
Number of pages6
JournalBehaviour Research and Therapy
Publication statusPublished - Oct 2020


  • Anxiety-related disorders
  • Computational modeling
  • Escape
  • FEAR
  • Fear
  • PAIN
  • Pain


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