Automated Classification of Cognitive Workload Levels Based on Psychophysiological and Behavioural Variables of Ex-Gaussian Distributional Features

Abstract

The study is focused on applying ex-Gaussian parameters of eye-tracking and cognitive measures in the classification process of cognitive workload level. A computerised version of the digit symbol substitution test has been developed in order to perform the case study. The dataset applied in the study is a collection of variables related to eye-tracking: saccades, fixations and blinks, as well as test-related variables including response time and correct response number. The application of ex-Gaussian modelling to all collected data was beneficial in the context of detection of dissimilarity in groups. An independent classification approach has been applied in the study. Several classical classification methods have been invoked in the process. The overall classification accuracy reached almost 96%. Furthermore, the interpretable machine learning model based on logistic regression was adapted in order to calculate the ranking of the most valuable features, which allowed us to examine their importance.

The initial Polish adaptation of the Mind Wandering Questionnaire: translation and verification of the scale internal consistency

“…related to personal thoughts or memories [7]. It is also postulated that MW is mirrored in the distribution of response times (RTs) in tasks programmed to experimentally manipulate response-stimulus intervals [8] and in experiments the construction of which is to cause cognitive overload [9]. In these circumstances, a distribution of the RTs series is characterized by the presence of an increased proportion of rare but substantially prolonged RTs which are considered indicators of attentional lapses resulting from an increased tendency to mind wander.…”