Examining Temporal Awareness Through Interpretable Clustering

Abstract

An accurate mental model of the temporal characteristics of complex task environments, or temporal awareness (TA), presumably underlies human performance when interacting with dynamic systems. Nevertheless, objective methods for characterizing TA from observable behavior remain elusive. Observable data from human-system interactions may be used to estimate operators’ TA. We applied an interpretable optimal clustering tree model to temporal data collected from participant interactions with a modified NASA Multi-Attribute Task Battery (MATB)-II to determine whether distinct behavioral patterns associated with differing levels of TA could be recovered from objective data. The model, constructed using two temporal measures theorized to reflect covert TA, time-since-last-interaction and the temporal response index, recovered behavioral patterns associated with nontrivial differences in participants’ task performance. These findings highlight the importance of temporal cognition in human performance, provide means for objective performance monitoring in complex, dynamic task environments, and ways to improve operator training and interface development.