Relationship between virtual reality balloon analogue risk task and risky decision-making

PLoS One. 2023 Feb 22;18(2):e0282097. doi: 10.1371/journal.pone.0282097. eCollection 2023.

Abstract

The balloon analogue risk task (BART) is widely used to assess risk-taking tendencies on behavioral tests. However, biases or unstable results are sometimes reported, and there are concerns about whether the BART can predict risk behavior in the real world. To address this problem, the present study developed a virtual reality (VR) BART to enhance the reality of the task and narrow the gap between performance on the BART and risk behavior in the real world. We evaluated the usability of our VR BART through assessments of the relationships between BART scores and psychological metrics and additionally implemented an emergency decision-making VR driving task to investigate further whether the VR BART can predict risk-related decision-making in emergency situations. Notably, we found that the BART score significantly correlated with both sensation-seeking and risky driving behavior. Additionally, when we split participants into groups with high and low BART scores and compared their psychological metrics, we found that the high-score BART group included more male participants and exhibited higher sensation-seeking and more risky decision-making in an emergency situation. Overall, our study shows the potential of our new VR BART paradigm to predict risky decision-making in the real world.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Decision Making*
  • Humans
  • Male
  • Risk Factors
  • Risk-Taking
  • Virtual Reality*

Grants and funding

This work was supported by the National Research Foundation of Korea (grant numbers: NRF-2017M3C7A1041824, 2020R1I1A1A01057915, 2022R1F1A1060046), and two Institute of Information & Communications Technology Planning & Evaluation (IITP) grants funded by the Korean Government (MSIT) (No. 2017-0-00451: Development of BCI-based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning; No. 2019-0-00079: Artificial Intelligence Graduate School Program, Korea University). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.