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Risk propensity, or individuals’ attitude toward risk, can highly impact individuals’ decision-making in high-risk environments since those who merely focus on positive consequences associated with high-risk acts are more likely to engage in risk-taking behaviors. Previous studies identified activation in the prefrontal cortex during decision-making under risk to be a sign of an individual’s attitude toward risks. To investigate whether such past work—prevalent in behavioral research domains—translates into construction safety, this study conducted an experiment in a mixed-reality environment using functional near-infrared spectroscopy (fNIRS) technology to examine whether positive risk attitudes cause individuals to adopt risky construction behaviors and whether the activation of the prefrontal cortex of the brain can represent such risk attitudes. The results show that participants with a higher risk propensity had a higher brain activation during the risky electrical tasks; these individuals merely focused on gains, which motivated them to increase their risk-taking behavior and consequently experience more electrical accidents. Understanding workers’ attitudes toward risk will thus influence future understandings of decision behavior under risk.
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A cognitive modeling analysis of risk in sequential choice tasks
There are many ways to measure how people manage risk when they make decisions. A standard approach is to measure risk propensity using self-report questionnaires. An alternative approach is to use decision-making tasks that involve risk and uncertainty, and apply cognitive models of task behavior to infer parameters that measure people's risk propensity. We report the results of a within-participants experiment that used three questionnaires and four decision-making tasks. The questionnaires are the Risk Propensity Scale, the Risk Taking Index, and the Domain Specific Risk Taking Scale. The decision-making tasks are the Balloon Analogue Risk Task, the preferential choice gambling task, the optimal stopping problem, and the bandit problem. We analyze the relationships between the risk measures and cognitive parameters using Bayesian inferences about the patterns of correlation, and using a novel cognitive latent variable modeling approach. The results show that people's risk propensity is generally consistent within different conditions for each of the decision-making tasks. There is, however, little evidence that the way people manage risk generalizes across the tasks, or that it corresponds to the questionnaire measures.
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- Award ID(s):
- 1850849
- PAR ID:
- 10274449
- Date Published:
- Journal Name:
- Judgment and decision making
- Volume:
- 15
- ISSN:
- 1930-2975
- Page Range / eLocation ID:
- 823-850
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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