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Objective The objective of this study was to develop a machine learning classifier to infer attentional tunneling through behavioral indices. This research serves as a proof of concept for a method for inferring operator state to trigger adaptations to user interfaces. Background Adaptive user interfaces adapt their information content or configuration to changes in operating context. Operator attentional states represent a promising class of triggers for these adaptations. Behavioral indices may be a viable alternative to physiological correlates for triggering interface adaptations based on attentional state. Method A visual search task sought to induce attentional tunneling in participants. We analyzed user interaction under tunnel and non-tunnel conditions to determine whether the paradigm was successful. We then examined the performance trade-offs stemming from attentional tunnels. Finally, we developed a machine learning classifier to identify patterns of interaction characteristics associated with attentional tunnels. Results The experimental paradigm successfully induced attentional tunnels. Attentional tunnels were shown to improve performance when information appeared within them, but to hinder performance when it appeared outside. Participants were found to be more tunneled in their second tunnel trial relative to their first. Our classifier achieved a classification accuracy similar to comparable studies (area under curve = 0.74). Conclusion Behavioral indices can be used to infer attentional tunneling. There is a performance trade-off from attentional tunneling, suggesting the opportunity for adaptive systems. Application This research applies to adaptive automation aimed at managing operator attention in information-dense work domains.

Objective The objective of this study was to develop and evaluate an adaptive user interface that could detect states of operator information overload and calibrate the amount of information on the screen. Background Machine learning can detect changes in operating context and trigger adaptive user interfaces (AUIs) to accommodate those changes. Operator attentional state represents a promising aspect of operating context for triggering AUIs. Behavioral rather than physiological indices can be used to infer operator attentional state. Method In Experiment 1, a network analysis task sought to induce states of information overload relative to a baseline. Streams of interaction data were taken from these two states and used to train machine learning classifiers. We implemented these classifiers in Experiment 2 to drive an AUI that automatically calibrated the amount of information displayed to operators. Results Experiment 1 successfully induced information overload in participants, resulting in lower accuracy, slower completion time, and higher workload. A series of machine learning classifiers detected states of information overload significantly above chance level. Experiment 2 identified four clusters of users who responded significantly differently to the AUIs. The AUIs benefited performance, completion time, and workload in three clusters. Conclusion Behavioral indices can successfully detect states of information overload and be used to effectively drive an AUI for some user groups. The success of AUIs may be contingent on characteristics of the user group. Application This research applies to domains seeking real-time assessments of user attentional or psychological state.

Objective: The authors seek to characterize the behavioral costs of attentional switches between points in a network map and assess the efficacy of interventions intended to reduce those costs. Background: Cybersecurity network operators are tasked with determining an appropriate attentional allocation scheme given the state of the network, which requires repeated attentional switches. These attentional switches may result in temporal performance decrements, during which operators disengage from one attentional fixation point and engage with another. Method: We ran two experiments where participants identified a chain of malicious emails within a network. All interactions with the system were logged and analyzed to determine if users experienced disengagement and engagement delays. Results: Both experiments revealed significant costs from attentional switches before (i.e., disengagement) and after (i.e., engagement) participants navigated to a new area in the network. In our second experiment, we found that interventions aimed at contextualizing navigation actions lessened both disengagement and engagement delays. Conclusion: Attentional switches are detrimental to operator performance. Their costs can be reduced by design features that contextualize navigations through an interface. Application: This research can be applied to the identification and mitigation of attentional switching costs in a variety of visual search tasks. Furthermore, it demonstrates the efficacy of noninvasive behavioral monitoring for inferring cognitive events.