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. 2023 Jan 17;7(1):1-13.
doi: 10.5334/cpsy.91. eCollection 2023.

Catastrophizing and Risk-Taking

Alexandra C Pike #  1   2   3 , Ágatha Alves Anet #  3 , Nina Peleg  3 , Oliver J Robinson  3
Affiliations

Catastrophizing and Risk-Taking

Alexandra C Pike et al. Comput Psychiatr. .

Abstract

Background: Catastrophizing, when an individual overestimates the probability of a severe negative outcome, is related to various aspects of mental ill-health. Here, we further characterize catastrophizing by investigating the extent to which self-reported catastrophizing is associated with risk-taking, using an online behavioural task and computational modelling.

Methods: We performed two online studies: a pilot study (n = 69) and a main study (n = 263). In the pilot study, participants performed the Balloon Analogue Risk Task (BART), alongside two other tasks (reported in the Supplement), and completed mental health questionnaires. Based on the findings from the pilot, we explored risk-taking in more detail in the main study using two versions of the Balloon Analogue Risk task (BART), with either a high or low cost for bursting the balloon.

Results: In the main study, there was a significant negative relationship between self-report catastrophizing scores and risk-taking in the low (but not high) cost version of the BART. Computational modelling of the BART task revealed no relationship between any parameter and Catastrophizing scores in either version of the task.

Conclusions: We show that increased self-reported catastrophizing may be associated with reduced behavioural measures of risk-taking, but were unable to identify a computational correlate of this effect.

Keywords: Anxiety/anxiety disorders; CBT/Cognitive Behavioural Therapy; Cognition; Web-based.

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Conflict of interest statement

O.J.R.’s MRC senior fellowship is partially in collaboration with Cambridge Cognition (who plan to provide in-kind contribution) and he is running an investigator-initiated trial with medication donated by Lundbeck (escitalopram and placebo, no financial contribution). He also holds an MRC-Proximity to discovery award with Roche (who provide in-kind contributions and have sponsored travel for A.C.P.) regarding work on heart rate variability and anxiety. He has also completed consultancy work on affective bias modification for Peak and online CBT for IESO digital health. A.C.P. received funding from the Wellcome Trust (Grant ref: 226694/Z/22/Z). Á.A.A. and N.P. declare no other conflicts of interest. The MRC (who provided funding) had no role in the study design, collection, analysis or interpretation of data.

Figures

A schematic of the BART task, showing different possible outcomes
Figure 1
Our modified BART task required participants to press a button labelled “Air” (or, in the main study, use a keyboard press) to pump up a balloon and earn points. The balloon grew in response to every pump. The task was divided into two blocks, each with 30 trials (30 balloons). Participants were told how far through each block they were (e.g. Balloon 1 of 30). Participants were instructed to pump the balloon as many times as they wished and to collect their points at any time, but were warned that the more they pumped the balloon up, the more likely it was to burst. This burst was associated with a penalty – either the loss of all the points for that balloon (low-cost block), or all the points for that balloon and an additional 200 points (high-cost block, pilot study) or 1000 points (high-cost block, main study). Once they had chosen to collect the points, they were presented with a screen containing the number of points they had earned from that balloon (see ‘Point Collection’ screen), or if the balloon burst, they received the ‘Negative Feedback’ screen that corresponded to the block they were in.
Three scatter plots with regression slopes, showing relationships between Catastrophizing Questionnaire scores and data from the BART task in the pilot study
Figure 2
Relationships between measures derived from the BART task and Catastrophizing scores in the pilot study (x axis), displaying the per-participant mean on each variable, and with a regression slope fitted using the ‘lm’ method from ggplot2. A) There was no significant relationship between the transformed number of times each participant pumped the balloon up and their Catastrophizing scores, in either block (LC or HC) of the BART task. B) There was no significant relationship between Catastrophizing scores and risk-taking in a computational model of the BART task. C) There was a significant relationship between Catastrophizing scores and learning rate in a computational model of the BART task, but only in the ‘high-cost’ block.
Two scatter plots with regression slopes, showing relationships between Catastrophizing Questionnaire scores and the number of pumps in each block of the BART task in the main study
Figure 3
Relationships between measures derived from the BART and Catastrophizing scores in the main study (x axis), displaying the per-participant mean on each variable, and with a regression slope fitted using the ‘lm’ method from ggplot2. A) A significant negative correlation between the transformed mean number of pumps in the LC block of the BART task and Catastrophizing scores. B) No correlation between the transformed mean number of pumps in the HC block and Catastrophizing scores.
Two scatter plots with regression slopes, showing relationships between Catastrophizing Questionnaire scores and parameters from a computational model of the BART task in the main study
Figure 4
Relationships between computational parameters derived from the BART and Catastrophizing scores in the main study (x axis), displaying the per-participant mean on each variable, and with a regression slope fitted using the ‘lm’ method from ggplot2. A) No significant relationship between the risk-taking parameter and Catastrophizing scores. B) No significant relationship between the prior belief parameter and Catastrophizing scores.
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