The effects of threat on complex decision-making: evidence from a virtual environment

被引:0
|
作者
Laycock, Aaron [1 ]
Schofield, Guy [2 ]
McCall, Cade [1 ]
机构
[1] Univ York, Dept Psychol, York YO10 5DD, England
[2] Univ York, Dept Archaeol, York YO10 5GB, England
来源
SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期
基金
英国经济与社会研究理事会;
关键词
Threat; Virtual reality; Complex decision-making; Computational modelling; Iowa gambling task; Choice perseveration; Reward sensitivity; IOWA GAMBLING TASK; ACUTE STRESS; PERFORMANCE; UNCERTAINTY;
D O I
10.1038/s41598-024-72812-2
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Individuals living and working in dangerous settings (e.g., first responders and military personnel) make complex decisions amidst serious threats. However, controlled studies on decision-making under threat are limited given obvious ethical concerns. Here, we embed a complex decision-making task within a threatening, immersive virtual environment. Based on the Iowa Gambling Task (IGT), a paradigm widely used to study complex decision-making, the task requires participants to make a series of choices to escape a collapsing building. In Study 1 we demonstrate that, as with the traditional IGT, participants learn to make advantageous decisions over time and that their behavioural data can be described by reinforcement-learning based computational models. In Study 2 we created threatening and neutral versions of the environment. In the threat condition, participants performed worse, taking longer to improve from baseline and scoring lower through the final trials. Computational modelling further revealed that participants in the threat condition were more responsive to short term rewards and less likely to perseverate on a given choice. These findings suggest that when threat is integral to decision-making, individuals make more erratic choices and focus on short term gains. They furthermore demonstrate the utility of virtual environments for making threat integral to cognitive tasks.
引用
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页数:12
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