Transitions Between Low and High Levels of Mental Workload can Improve Multitasking Performance

Devlin, Shannon P.; Moacdieh, Nadine Marie; Wickens, Christopher D.; Riggs, Sara L.

doi:10.1080/24725838.2020.1770898

Cited by 7 publications

(7 citation statements)

References 40 publications

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“…For the low workload condition, 3–5 UAVs were active at all times, while 13–16 UAVs were active at all times for the high workload condition. These numbers were validated using NASA-TLX and performance measurements (see Devlin et al (2020) for the full details). In each experimental condition, pairs completed two 15-minute trials, one with each of the two workload conditions.…”

Section: Methodsmentioning

confidence: 99%

“…Other metrics that have been utilized include gaze overlap, which measures the times that several users are viewing the same area simultaneously (Pietinen et al, 2010). This was used by Devlin et al (2020) to study the link between visual attention and pair performance during changes in workload. The phi coefficient (Φ; Bakeman & Gottman, 1997), which quantifies the lag between two time series, was also utilized by Devlin et al (2020) as a measure of the coordination between scanpaths.…”

Section: Background Team Performance and Attention Allocationmentioning

confidence: 99%

“…This would allow for a better understanding of how people collaborate, which would, in turn, lead to better design principles for collaborative interfaces. Eye tracking is one approach that can be used in this regard, given that it can provide a trace of where a person is looking—the person’s scanpath —which, in turn, can help shed further light on team performance (Devlin et al, 2020; Faulhaber & Friedrich, 2019). Scanpath analysis has been used in the past to assess mental workload (Maggi et al, 2019), cognitive capacity (Hayes & Henderson, 2017), task demand (Boot et al, 2009), and breast screening reading strategies (Chen et al, 2018), but these methods have not been implemented to assess team performance.…”

Section: Introductionmentioning

confidence: 99%

“…The sequence of fixations and saccades form the scanpath of each user and the areas that users look at on the screen are defined as areas of interest (AOIs). In recent years, eye tracking has been gaining interest as a means to assess and improve team performance (e.g., Daggett et al, 2017; D’Angelo & Begel, 2017; Devlin et al, 2020). Eye tracking provides a detailed, objective window into visual attention allocation and can also be used in real time (Lin et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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

Iskandarani

Atweh

Devlin

et al. 2023

Journal of Cognitive Engineering and Decision Making

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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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Section: Methodsmentioning

confidence: 99%

Section: Background Team Performance and Attention Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Iskandarani

Atweh

Devlin

et al. 2023

Journal of Cognitive Engineering and Decision Making

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, the nature of human beings is still to be discussed. Taking the mental workload as an example, contrary to traditional belief that lower workload leads to better performance, there has been study showing that transitions of mental workload could produce better primary and secondary task performance than duration of both high and low mental workload, which justifies appropriate secondary task engagement while driving [129]. At the same time, distinct characteristics of specific groups are not to be ignored in research as well.…”

Section: Significance Of Driving Distraction Researchmentioning

confidence: 99%

Understanding the domain of driving distraction with knowledge graphs

Feng

2022

PLoS ONE

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This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs were constructed to demonstrate bibliometric information and domain-specific research content respectively. In terms of bibliometric analysis, the evolution of publication and citation numbers reveals the accelerated development of this domain, and trends of multidisciplinary and global participation could be identified according to knowledge graphs from Vosviewer. In terms of research content analysis, a new framework consisting of five dimensions was clarified, including “objective factors”, “human factors”, “research methods”, “data” and “data science”. The main entities of this domain were identified and relations between entities were extracted using Natural Language Processing methods with Python 3.9. In addition to the knowledge graph composed of all the keywords and relationships, entities and relations under each dimension were visualized, and relations between relevant dimensions were demonstrated in the form of heat maps. Furthermore, the trend and significance of driving distraction research were discussed, and special attention was given to future directions of this domain.

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

et al. 2022

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Transitions Between Low and High Levels of Mental Workload can Improve Multitasking Performance

Cited by 7 publications

References 40 publications

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

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

Understanding the domain of driving distraction with knowledge graphs

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

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