Identification and Evaluation of Effective Strategies in a Dynamic Visual Task Using Eye Gaze Dynamics

Abstract

Simulation-based training utilising visual displays are common in many defence and civil domains. The performance of individuals in these tasks depends on their ability to employ effective visual strategies. Quantifying the performance of the trainees is vitally important when assessing training effectiveness and developing future training requirements. The approach, attitudes and processes of an individual’s learning varies from one to another. In this light, some visual strategies may be better suited to the dynamics of a task environment than others, the result of which could be observed in the superior performance outcomes of some individuals. In this study, eye gaze data is used to investigate the relationship between performance outcomes and visual strategies. In an attempt to emulate real operational settings, a challenging task environment using multiple targets that had minimal salient features was selected for the study. Eye gaze of participants performing a simulation-based unmanned aerial vehicle (UAV) refuelling task was used to facilitate the investigation. Cross recurrence quantification analysis (CRQA) and Epistemic network analysis (ENA) were employed on eye gaze data to provide spatial-temporal mapping of visual strategies. A CRQA measure of recurrence rate was used to observe participants’ fixation interest on various regions of the task environment. The recurrence behaviours were categorised into cases of visual strategies using an unsupervised clustering algorithm. This article discusses the relationship between the visual strategy cases and performance outcomes to observe which are the most effective. Using the relationship between recurrence rates and performance outcomes, we demonstrate and discuss a gaze-based measure that could objectively quantify performance.

The eyes and hearts of UAV pilots: observations of physiological responses in real-life scenarios

“…For example, in surveillance tasks, operators monitoring several screens can be supported by systems that track gaze behavior and automatically notify the operator to adjust their scanning pattern [4]. In a simulated task, Veerabhadrappa, et al [5] found that participants achieved higher performance on a simulated UAV refuelling task when they maintained a longer gaze on the relevant region of interest compared to less relevant regions. Further, in training scenarios, gazebased measures can identify operator attention allocation and quantify progress of novice operators [6].…”

“…We can see the ROI in green in the Figure 3. We use functions made by Dinu C. Gherman to define the ROI by giving points in an image 5 . Fig.…”