# Detecting expertise in decision making under pressure: a virtual reality assessment environment and empirical evaluation

## TL;DR

This paper introduces VR-DMX, a virtual reality tool that assesses decision-making under pressure, showing it can reliably measure individual differences in performance and decision quality.

## Contribution

The novel VR-DMX environment provides a reliable and valid method to assess decision-making expertise under time constraints in a virtual reality setting.

## Key findings

- VR-DMX metrics showed symmetrical distributions and comparable variation to established measures.
- The moderate correlation between Total Tickets and DMX score suggests they measure related but distinct constructs.
- VR-DMX effectively differentiates individual performance levels and captures decision-making quality distinct from general cognitive abilities.

## Abstract

Professions such as military, aviation, submarine operation, and emergency response require individuals to navigate complex environments characterized by limited information, stringent time constraints, and significant pressures. Effective decision making under pressure is crucial in safety–critical professions, yet measuring this expertise remains challenging. Inspired by the military context, this article introduces the virtual reality decision-making expertise (VR-DMX) environment, designed to evaluate decision-making expertise under time constraints within a virtual reality scenario. VR-DMX simulates an amusement arcade where users must decide how to allocate time across various games to maximize ticket earnings. Through two validation studies (N = 60 and N = 76), we examined two metrics: Total Tickets (measuring overall performance) and DMX score (isolating decision-making quality). Both metrics demonstrated symmetrical distributions without floor or ceiling effects, with coefficients of variation comparable to established individual difference measures (32.4–37.4% for Total Tickets; 20.8–27.6% for DMX score). The moderate correlation between metrics (meta-analysis r = 0.771, 95% CI [0.599, 0.943]) indicates they measure related but distinct constructs. Our findings indicate that VR-DMX effectively differentiates individual performance levels and captures a distinct decision-making component that is separate from general cognitive abilities. Comparing decision-making expertise between professionals in safety–critical fields with those without such experience would be a sensible next step to help validate the potential for selection and training applications. VR-DMX was designed to measure decision-making expertise in safety–critical contexts, and initial validation data demonstrating effective differentiation of individual performance levels suggest that continued development could fulfill this design intention for applications in selection, training, and performance prediction.

The online version contains supplementary material available at 10.1186/s41235-025-00695-6.

In many safety-critical professions—such as aviation, submarine operation, firefighting, and emergency response—effective decision-making under pressure can mean the difference between safety and disaster. Individuals in these roles must routinely make rapid high-stakes choices in stressful, unpredictable environments; yet accurately measuring and understanding the decision-making capabilities that facilitate optimal performance under these conditions remains challenging. To address this real-world need, we developed the Virtual Reality Decision-Making Expertise (VR-DMX) environment, a novel virtual reality assessment designed to measure an individual's ability to make effective decisions when facing time constraints. VR-DMX simulates a realistic, fast-paced scenario—an amusement arcade—where users strategically allocate their limited time across tasks to maximize reward. Validation testing indicates that VR-DMX reliably detects individual differences in decision-making expertise, demonstrating its ability to discriminate among varying levels of performance and decision quality clearly. Because VR-DMX appears to capture variations in the ability to allocate attention and prioritize tasks strategically under stress, initial validation data suggest that continued development could enable applications in training and selection within safety-critical domains, potentially guiding practical improvements in training and selection processes to enhance operational safety and effectiveness in sectors where poor decisions can have severe consequences.

The online version contains supplementary material available at 10.1186/s41235-025-00695-6.