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Bayesian Approach to Belief Updating and Anxiety in the Classic Beads Task

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

The tendency to accept a hypothesis based on fewer than normal pieces of information (“Jumping-to-Conclusions” (JTC) bias) is a probabilistic reasoning bias commonly observed in clinical populations with delusions. This tendency can be attributed to a relatively low decision threshold and overweighting of a piece of evidence. Whilst some highly anxious individuals demonstrate JTC bias, the implications of findings remain contentious. The contention stems from a lack of understanding about how anxiety interacts with the two factors in belief updating. It remains unexplored as to whether anxious individuals deviate from rationality in belief updating just as much as the healthy population or are simply less “over-cautious” in gathering information. The present study adopts a systematic approach utilising a Bayesian graphical model to answer these questions. Based on the classic beads task, the model illustrates how a rational agent would update their prior belief upon receiving new information and at what point that updated belief would cause them to act. Then, we investigate the impact of anxiety on decision threshold and evidence weights in the model, and eventually how belief updating would change. These steps allow for comparisons between a rational response and those exhibited by both healthy and anxious populations. By clearly illustrating the influence of anxiety on each parameter in the model, we can deepen the understanding of associations between anxiety and JTC bias. The properties of the model are demonstrated in a series of simulation studies. The implication of this model on real-life data will also be discussed.

Discussion
New

Great talk / poster. Very interesting take on the beads task. Do you also plan to create a model including both thresholds and evidence weights?

Prof. Gerit Pfuhl 1 comment