The purpose of this research was two-fold: (1) further validate the Virtual Reality Stroop Task HMMWV [VRST; Stroop stimuli embedded within a virtual high mobility multipurpose wheeled vehicle] via a comparison of the 3-dimensional VRST factor structure to that of a 2-dimensional computerized version of the Stroop task; and (2) model the performance of machine learning [ML] classifiers and hyper-parameters for an adaptive version of the VRST. Both the 3-D VRST and 2-D computerized Stroop tasks produced two-factor solutions: an accuracy factor and a reaction time factor. The factors had low correlations suggesting participants may be focusing on either responding …
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The purpose of this research was two-fold: (1) further validate the Virtual Reality Stroop Task HMMWV [VRST; Stroop stimuli embedded within a virtual high mobility multipurpose wheeled vehicle] via a comparison of the 3-dimensional VRST factor structure to that of a 2-dimensional computerized version of the Stroop task; and (2) model the performance of machine learning [ML] classifiers and hyper-parameters for an adaptive version of the VRST. Both the 3-D VRST and 2-D computerized Stroop tasks produced two-factor solutions: an accuracy factor and a reaction time factor. The factors had low correlations suggesting participants may be focusing on either responding to stimuli accurately or swiftly. In future studies researchers may want to consider throughput, a measure of correct responses per unit of time. The assessment of naive Bayes (NB), k-nearest neighbors (kNN), and support vector machines (SVM) machine learning classifiers found that SVM classifiers tended to have the highest accuracies and greatest areas under the curve when classifying users as high or low performers. NB also performed well but kNN algorithms did not. As such, SVM and NB may be the best candidates for creation of an adaptive version of the VRST.
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