Developing scenarios that evoke shoot/don't-shoot errors

Trait-level predictors of human performance outcomes in personnel engaged in stressful laboratory and field tasks

Introduction

Personnel performance under stress hinges on various factors, including individual traits, training, context, mental and physiological states, and task demands. This study explored the link between the traits of military personnel and their performance outcomes in five domains: move, shoot, communicate, navigate, and sustain.

Methods

A total of 387 U.S. Army soldiers participated in this study, undergoing trait assessments covering physical, cognitive, social-emotional, demographic/lifestyle, and health domains. Performance was measured through lab and field events assessing a broad range of individual and team-level skills under conditions demanding resilience to acute cognitive and physical stress exposure. Analysis used feature selection and elastic net regression.

Results

Analyses revealed complex associations between traits and performance, with physical, cognitive, health-related, social-emotional, and lifestyle traits playing roles in guiding and constraining performance. Measures of resilience, emotion regulation, grit, and mindfulness were identified as relevant predictors of several performance-related outcomes.

Discussion

Results carry implications for the selection, training, and operational effectiveness of personnel in high-stakes occupations including military and first response. Further research is necessary to explore the mechanisms underlying these associations and inform targeted interventions to boost personnel effectiveness.

Heart rate of fire: exploring direct implementation of physiological measurements in realistic shoot/don't-shoot simulations

Introduction

Shooting simulations provide an excellent opportunity to train use-of-force decisions in controlled environments. Recently, military and law enforcement organizations have expressed a growing desire to integrate physiological measurement into simulations for training and feedback purposes. Although participants can easily wear physiological monitors in these scenarios, direct implementation into training may not be simple. Theoretical problems exist in the ultra-short heart rate variability windows associated with use-of-force training, and practical problems emerge as existing scenario libraries at training organizations were not designed for physiological monitoring.

Methods

The current study explored the challenges and possibilities associated with direct implementation of physiological monitoring into an existing library of firearms training scenarios. Participants completed scenarios in a shooting simulator using existing military training scenarios while wearing a device to monitor their heart rate.

Results

The results revealed lower heart rate variability (approximately 6%) occurred in scenarios where participants did not have to fire weapons, indicating that don't-shoot scenarios may actually impose more cognitive stress on shooters. Additional evidence further demonstrated how both behavioral and physiological factors could be used concomitantly to predict unintentionally firing on non-hostile actors. However, behavioral measures were more predictive (e.g., β = .221) than physiological measures (e.g., β = -.132) when the latter metrics were limited to specific scenarios. Qualitative results suggest that simply applying physiological monitoring to existing shooting simulations may not yield optimal results because it would be difficult to directly integrate physiological measurement in a meaningful way without re-designing some elements of the simulations, the training procedure, or both.

Discussion

Future use-of-force shooting simulations should consider designing novel scenarios around the physiological measurement rather than directly implementing physiological assessments into existing libraries of scenarios.

Not according to plan: Cognitive failures in marksmanship due to effects of expertise, unknown environments, and the likelihood of shooting unintended targets

Shooting errors have multi-faceted causes with contributing factors that include sensorimotor activity and cognitive failures. Empirical investigations often assess mental errors through threat identification, yet other cognitive failures could contribute to poor outcomes. The current study explored several possible sources of cognitive failures unrelated to threat identification with live fire exercises. Experiment 1 examined a national shooting competition to compare marksmanship accuracy, expertise, and planning in the likelihood of hitting no-shoot or unintended targets. Experts demonstrated an inverse speed/accuracy trade-off and fired upon fewer no-shoot targets than lesser skilled shooters, yet overall, greater opportunity to plan produced more no-shoot errors, thereby demonstrating an increase in cognitive errors. Experiment 2 replicated and extended this finding under conditions accounting for target type, location, and number. These findings further dissociate the roles of marksmanship and cognition in shooting errors while suggesting that marksmanship evaluations should be re-designed to better incorporate cognitive variables.

How speed impacts threat assessment in lethal force decisions

Despite the importance of being both fast and accurate in lethal force decisions, there is little empirical evidence to identify how speed impacts threat-related decisions and perception. Two experiments used speeded and unspeeded manipulations to determine how the speed imperative impacted threat assessments. Experiment 1 used drift diffusion modeling to quantify decision parameters, including rate of information processing, decision threshold, bias, and non-decisional processes. Speeded conditions reduced the information threshold needed to make decisions and shortened non-decisional processes, yet this manipulation had no impact on the rate of information processing or starting bias. Experiment 2 explored perceptual differences in threat assessment. Participants confidently made threat assessments despite only 30 ms exposure to stimuli with little impact on their subjective threat ratings based on exposure duration. Taken together, these results document the influence of speed on decision-making parameters of threat assessments while demonstrating little impact on threat perception.

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.