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Specificity of the jumping-to-conclusion bias in social anxiety: An account using the Bayesian computational modelling approach

Authors
Ms. Nicole Tan
The Australian National University ~ Research School of Psychology
Dr. Yiyun Shou
National University of Singapore
Dr. Junwen Chen
The Australian National University
Bruce Christensen
The Australian National University, Australia
Abstract

To date, little is known about the role of social anxiety in the assignment of evidence weights which could contribute to the jumping-to-conclusion bias. The present study used a Bayesian computational method to understand the mechanism of jumping-to-conclusion bias in social anxiety, specifically through the assignment of weights to information sampled. The present study also investigated the specificity of the jumping-to-conclusion bias in social anxiety using three variations of beads tasks that consisted of neutral and socially threatening situations. A sample of 210 participants was recruited from online communities to complete the beads tasks and a set of questionnaires measuring the trait variables including social anxiety and the fears of positive and negative evaluation. The Bayesian model estimations indicated that social anxiety and fears of evaluation did not significantly bias the assignment of evidence weights to information received, except when mostly positive feedback was shown. Our results did not support a significant association between the jumping-to-conclusion bias and social anxiety/fears of evaluation.

Tags

Keywords

belief updating
jumping to conclusion bias; beads tasks
Bayesian computational modelling
reasoning bias; social anxiety
fears of evaluation
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Cite this as:

Tan, N., Shou, Y., Chen, J., & Christensen, B. (2022, July). Specificity of the jumping-to-conclusion bias in social anxiety: An account using the Bayesian computational modelling approach. Paper presented at Virtual MathPsych/ICCM 2022. Via mathpsych.org/presentation/794.