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1. How Coordinated Visual Attention on a Target Area of Interest is Impacted by a Change in Workload Over Time

   https://doi.org/10.1177/1071181320641083  

   Devlin, Shannon P.; Flynn, Jake R.; Riggs, Sara L. (December 2020, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   null (Ed.)

   Data-rich environments rely on operators to collaborate, especially in light of workload changes. This work explores the relationship between the operators’ shared visual attention patterns on a target area of interest (AOI), i.e. the AOI causing a workload change, and how it affects collaborative performance. Eye tracking data was collected from ten pairs of participants who completed two scenarios, the first being low workload and the second being high workload, in an unmanned aerial vehicle (UAV) command and control testbed. Then, best and worst performing pairs were compared in terms of two shared visual attention metrics: (1) percent gaze overlap and (2) the phi coefficient for the target AOI. The results showed that coordinated visits to and from the target AOI were associated with better performance during high workload. These results suggest including quantitative measures of visual attention can be indicators of the adaptation process in real- time. 

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2. Does What We See Shape History? Examining Workload History as a Function of Performance and Ambient/Focal Visual Attention

   https://doi.org/10.1145/3449066  

   Devlin, Shannon P.; Byham, Jennifer K.; Riggs, Sara Lu (June 2021, ACM Transactions on Applied Perception)

   null (Ed.)

   Changes in task demands can have delayed adverse impacts on performance. This phenomenon, known as the workload history effect, is especially of concern in dynamic work domains where operators manage fluctuating task demands. The existing workload history literature does not depict a consistent picture regarding how these effects manifest, prompting research to consider measures that are informative on the operator's process. One promising measure is visual attention patterns, due to its informativeness on various cognitive processes. To explore its ability to explain workload history effects, participants completed a task in an unmanned aerial vehicle command and control testbed where workload transitioned gradually and suddenly. The participants’ performance and visual attention patterns were studied over time to identify workload history effects. The eye-tracking analysis consisted of using a recently developed eye-tracking metric called coefficient K , as it indicates whether visual attention is more focal or ambient. The performance results found workload history effects, but it depended on the workload level, time elapsed, and performance measure. The eye-tracking analysis suggested performance suffered when focal attention was deployed during low workload, which was an unexpected finding. When synthesizing these results, they suggest unexpected visual attention patterns can impact performance immediately over time. Further research is needed; however, this work shows the value of including a real-time visual attention measure, such as coefficient K , as a means to understand how the operator manages varying task demands in complex work environments. 

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3. Does It MultiMatch? What Scanpath Comparison Tells us About Task Performance in Teams

   https://doi.org/10.1177/15553434231171484  

   El Iskandarani, Mohamad; Atweh, Jad A.; McGarry, Shannon P.; Riggs, Sara L.; Moacdieh, Nadine Marie (June 2023, Journal of Cognitive Engineering and Decision Making)

   Teamwork and collaboration form the cornerstones of organizational performance and success. It is important to understand how the attention allocation of team members is linked to performance. One approach to studying attention allocation in a team context is to compare the scanpath similarity of two people working in teams and to explore the link between scanpath similarity and team performance. In this study, participants were recruited to work in pairs on an unmanned aerial vehicle (UAV) task that included low and high workload conditions. An eye tracker was used to collect the eye movements of both participants in each team. The scanpaths of two teammates were compared in low and high workload conditions using MultiMatch, an established scanpath comparison algorithm. The obtained scanpath similarity values were correlated with performance measures of response time and accuracy. Several MultiMatch measures showed significant strong correlations across multiple dimensions, providing insight into team behavior and attention allocation. The results suggested that the more similar each team member’s scanpath is, the better their performance. Additional research and consideration of experimental variables will be necessary to further understand how best to use MultiMatch for scanpath similarity assessment in complex domains. 

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4. Effects of Workload and Workload Transitions on Attention Allocation in a Dual-Task Environment: Evidence From Eye Tracking Metrics

   https://doi.org/10.1177/1555343419892184  

   Moacdieh, Nadine_Marie; Devlin, Shannon_P; Jundi, Hussein; Riggs, Sara_Lu (January 2020, Journal of Cognitive Engineering and Decision Making)

   High mental workload, in addition to changes in workload, can negatively affect operators, but it is not clear how sudden versus gradual workload transitions influence performance and visual attention allocation. This knowledge is important as sudden shifts in workload are common in multitasking domains. The objective of this study was to investigate, using performance and eye tracking metrics, how constant versus variable levels of workload affect operators in the context of a dual-task paradigm. An unmanned aerial vehicle command and control simulation varied task load between low, high, gradually transitioning from low to high, and suddenly transitioning from low to high. Performance on a primary and secondary task and several eye tracking measures were calculated. There was no significant difference between sudden and gradual workload transitions in terms of performance or attention allocation overall; however, both sudden and gradual workload transitions changed participants’ strategy in dealing with the primary and secondary task as compared to low/high workload. Also, eye tracking metrics that are not frequently used, such as transition rate and stationary entropy, provided more insight into performance differences. These metrics can potentially be used to better understand operators’ strategies and could form the basis of an adaptive display. 

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5. Scan-based eye tracking measures are predictive of workload transition performance

   Shannon P Devlin, Noelle L (November 2022, Applied Ergonomics)

   Given there is no unifying theory or design guidance for workload transitions, this work investigated how visual attention allocation patterns could inform both topics, by understanding if scan-based eye tracking metrics could predict workload transition performance trends in a context-relevant domain. The eye movements of sixty Naval flight students were tracked as workload transitioned at a slow, medium, and fast pace in an unmanned aerial vehicle testbed. Four scan-based metrics were significant predictors across the different growth curve models of response time and accuracy. Stationary gaze entropy (a measure of how dispersed visual attention transitions are across tasks) was predictive across all three transition rates. The other three predictive scan-based metrics captured different aspects of visual attention, including its spread, directness, and duration. The findings specify several missing details in both theory and design guidance, which is unprecedented, and serves as a basis of future workload transition research. 

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