.png){kind=icon}
.png){kind=icon}
Return
Decomposing conditioned avoidance performance with computational models
.png){kind=icon}
OA
AI
DOI：10.1016/j.brat.2020.103712.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Pain
Anxiety-related disorders
Fear
Escape
Computational modeling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.5
Papers:
6.3K
Citations:
2.0W