Identifying psychophysiological indices of expert vs. novice performance in deadly force judgment and decision making - Johnson, Robin R.; Stone, Bradly T.; Miranda, Carrie M.; Vila, Bryan; James, Lois; James, Stephen M.; Rubio, Roberto F.; Berka, Chris (Frontiers in Human Neuroscience, 2014)
Objective: To identify psychophysiological indices of deadly force decision making in experts versus novices during simulator training. Background: Modern training techniques focus on improving decision-making skills with participative assessment between trainees and subject matter experts primarily through subjective observation. Objective metrics need to be developed. The current study explored the potential for psychophysiological metrics of decision making in deadly force judgment contexts. Method: Twenty-four participants (novice, expert) were recruited. All wore a wireless EEG device to collect psychophysiological data during high-fidelity simulated deadly force judgment and decision-making simulations using a modified Glock firearm. Participants were exposed to 27 video scenarios, one-third of which would have justified use of deadly force. Pass/fail was determined by whether the participant used deadly force appropriately. Results: Experts had a significantly higher pass rate compared to novices (p < 0.05). Multiple metrics were shown to distinguish novices from experts. Hierarchical regression analysis indicate that psycho-physiological variables are able to explain 72% of the variability in expert performance, but only 37% in novices. Discriminant function analysis using psychophysiological metrics was able to discern between experts and novices with 72.6% accuracy. Conclusion: Results suggest that expert performance is more tightly coupled with psychophysiology, compared with a weaker relationship in novices. Discriminant function measures may have the potential to objectively identify when expertise is obtained. Application: Psychophysiological metrics may create a performance model with the potential to optimize simulator-based DFJDM training. These performance models could be used for trainee feedback, and/or by the instructor to assess performance objectively.