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

Moacdieh, Nadine Marie; Devlin, Shannon P.; Jundi, Hussein; Riggs, Sara L.

doi:10.1177/1555343419892184

Cited by 18 publications

(5 citation statements)

References 60 publications

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“…Moacdieh et. al [60] found that the preference-based workload allocation method has benefits on workload allocation, while Wigboldus et. al [61] proposed that capability-based workload has greater productivity and effectiveness.…”

Section: Discussionmentioning

confidence: 99%

Affective Workload Allocation for Multi-human Multi-robot Teams

Jo¹,

Wang²,

Yang³

et al. 2023

Preprint

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The interaction and collaboration between humans and multiple robots represent a novel field of research known as human multi-robot systems. Adequately designed systems within this field allow teams composed of both humans and robots to work together effectively on tasks such as monitoring, exploration, and search and rescue operations. This paper presents a deep reinforcement learning-based affective workload allocation controller specifically for multi-human multi-robot teams. The proposed controller can dynamically reallocate workloads based on the performance of the operators during collaborative missions with multi-robot systems. The operators' performances are evaluated through the scores of a self-reported questionnaire (i.e., subjective measurement) and the results of a deep learning-based cognitive workload prediction algorithm that uses physiological and behavioral data (i.e., objective measurement). To evaluate the effectiveness of the proposed controller, we use a multi-human multi-robot CCTV monitoring task as an example and carry out comprehensive real-world experiments with 32 human subjects for both quantitative measurement and qualitative analysis. Our results demonstrate the performance and effectiveness of the proposed controller and highlight the importance of incorporating both subjective and objective measurements of the operators' cognitive workload as well as seeking consent for workload transitions, to enhance the performance of multi-human multi-robot teams.

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Section: Discussionmentioning

confidence: 99%

Affective Workload Allocation for Multi-human Multi-robot Teams

Jo¹,

Wang²,

Yang³

et al. 2023

Preprint

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“…B. Moacdieh et al. 2020 ). Die unterschiedlichen Rahmenbedingungen der einzelnen Studien erschweren den direkten Vergleich der angewandten Messgeräte und -verfahren untereinander.…”

Section: Bausteine Der Experimentalaufgabeunclassified

Design and validation of a computer-based task for the induction of a mental workload spectrum

et al. 2022

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ZusammenfassungIm Rahmen des vom BMBF geförderten Projekts Fahrerkabine 4.0 wird eine adaptive Mensch-Maschine-Schnittstelle für Landmaschinen entwickelt, die das aktuelle Beanspruchungslevel mit Hilfe physiologischer Daten detektiert. Zu diesem Zwecke wird in dieser Arbeit eine Experimentalaufgabe entwickelt und evaluiert, die ein psychisches Belastungsspektrum von wenig bis sehr stark anstrengend in Versuchspersonen induzieren kann. In drei Laborstudien wird psychische Belastung mit einer Überwachungstätigkeit erzeugt, deren Bearbeitungsgeschwindigkeit randomisiert variiert. Die Komplexität der Tätigkeit wird abschnittsweise durch eine visuelle und/oder eine auditive Nebenaufgabe erhöht. Von den Versuchspersonen empfundene psychische Beanspruchung wird mit Hilfe der Rating Scale Mental Effort, der Reaktionszeiten und der Fehlerrate bewertet. Die Studien mit jeweils N = 17, N = 8 bzw. N = 21 Probanden zeigen, dass eine dynamische Kombination von Haupt- und Nebenaufgaben signifikant unterschiedliche Belastungsgrade induzieren kann (F (2,40) = 54.834, p < 0,001).Praktische Relevanz: Mithilfe der entwickelten Experimentalaufgabe wird in zukünftigen Arbeiten ein Messsystem zur Klassifizierung psychischer Beanspruchungszustände für Landmaschinen entworfen und erprobt. In beanspruchungsarmen Situationen (z. B. automatisierte Ernte) sollen zusätzliche Handlungsempfehlungen vorgeschlagen werden. Während stark beanspruchenden Abschnitten soll eine Überforderung der Nutzenden vermieden werden, indem lediglich die für die Durchführung der Arbeitstätigkeit notwendigen Informationen angezeigt werden.

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“…Whether the studies use touch screen, fNIRS, eye-tracking data to measure performance and subjective cognitive load, a further work is needed to better understand the relationship between cognitive load and predicting task performance. A recent study has investigated the difference between sudden and gradual workload transitions in terms of performance during a task but not at the initial of the task [41]. Being able to determine the cognitive load of a task at the initial of the task can be used to improve those interfaces that are sensitive to time.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Initial Cognitive Load to Predict User Response to Complex Visual Tasks

Albaghli¹,

Jandali²,

Almahmid³

et al. 2022

World Congress on Electrical Engineering and Computer Systems and Science

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In this study, we were able to show how cognitive load measurement during the initiation of a complex visual task can predict user response. We measured cognitive load using pupil size and microsaccade rate. The initial phase of task was defined as the first 25 percent of the trial Reaction Time (RT), which was variable up to 50 seconds. The complex visual task entailed a set of twelve words that could be grouped based into 1, 2, or 3 categories or sets, e.g., the words bed, pillow, headboard, etc. can be grouped into a single set that is bedroom. We found a significant correlation between initial cognitive load and final user response to the task. This study provides new insights into the initial cognitive processes that would have practical applications in adaptive user interface design, early warning controls, and detection in human performance.

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

Cited by 18 publications

References 60 publications

Affective Workload Allocation for Multi-human Multi-robot Teams

Affective Workload Allocation for Multi-human Multi-robot Teams

Design and validation of a computer-based task for the induction of a mental workload spectrum

Leveraging Initial Cognitive Load to Predict User Response to Complex Visual Tasks

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