Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics.

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees' safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm's performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

The Activate Test of Embodied Cognition (ATEC): Reliability, concurrent validity and discriminant validity in a community sample of children using cognitively demanding physical tasks related to executive functioning.

Embodied cognition assessment may be more closely related to how children function than standard measures of executive functioning (EF) that require little body movement. Activate Test of Embodied Cognition (ATEC) measures cognitive functioning based on cognitively demanding physical tasks assessed using an automated administration with motion capture technology. This study evaluated the psychometrics of ATEC.Children ages 5-11 years were recruited from the community (N = 55). ATEC was performed twice for a subsample, approximately 2 weeks apart. Motion capture data were collected and converted into ATEC Total Score. Concurrent measures included scores from NIH Toolbox for EF (Flanker, Working Memory, Go/No-Go task, Balloon Analogue Risk Task (BART)), and parent reports (Child Behavior Checklist (CBCL), Behavioral Rating Inventory of Executive Function (BRIEF-2) and Swanson, Nolan, and Pelham Rating Scale (SNAP-IV) for ADHD).ATEC Total Score was significantly correlated with concurrent measures of EF and showed significant discriminant validity between At-Risk children and Normal Range children on CBCL Competency, CBCL ADHD Combined score, BRIEF-2 Global Executive Composite, BRIEF-2 Cognitive Regulation Index and SNAP-IV ADHD Combined Score. Regression analyses showed that ATEC Total score was a better predictor of CBCL Competency than any of the standard EF assessments. ATEC Total Score had excellent test-retest reliability, (ICC = .945, df = 27, p < .001) with a small practice effect (Cohen's d = 0.33). ATEC Total Score correlated with age (r = .42, p < .003) suggesting improvement with normal development. ATEC produces reliable scores that may identify children at risk for EF impairments.