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Cited by 13Human-Automation Interaction and SafetySleep and Work-Related FatigueEmotion and Mood Recognition

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Differentiating Cognitive Load Using a Modified Version of AttenD

Open access
Seppelt, Bobbie, Seaman, Sean, Angell, Linda, Mehler, Bruce, Reimer, Bryan·2017·Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
DOI 10.1145/3122986.3123019
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Abstract

Voice interfaces offer promise in allowing drivers to keep their eyes on-road and hands on-wheel. In relieving visualmanual demand, there is the potential for voice-enabled interfaces to inadvertently shift the burden of load to cognitive resources. Measurement approaches are needed that can identify when and to what extent cognitive load is present during driving. A modified form of the AttenD algorithm was applied to assess the amount of cognitive load present in a set of auditory-vocal task interactions. These tasks were subset from a larger on-road study conducted in the Boston area of driver response during use of an in-vehicle voice system [22]. The modified algorithm differentiated among the set of auditory-vocal tasks examined -- and may be useful to HMI practitioners who are working to develop and evaluate HMIs to support drivers in managing their attention to the road, and in the development of real-time driver attention monitoring systems.

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  • While some work showed feasible accuracy, other work argues that other modalities are superior.

    “While some work showed feasible accuracy [182,202], other work argues that other modalities are superior [87,201].”

    From A Survey on Measuring Cognitive Workload in Human-Computer Interaction (2023) · cites this paper as [201]

    The introductory passage (page unavailable) reports that the glance-based AttenD approach showed greater power than detection-response-task measures for differentiating mixed-mode interface characteristics. That supports a specific comparison between measurement approaches, but not the broader assertion that other modalities are generally superior. (LLM, conf=0.77)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

  • A Survey on Measuring Cognitive Workload in Human-Computer Interaction

    Kosch, T., Karolus, J., Zagermann, J., Reiterer, H., Schmidt, A., & Woźniak, P. W. (2023) · ACM Computing Surveys · doi:10.1145/3582272 · Cited by 304

    “…87, 99, 165, 184, 193, 228, 236, 258] Eye Blinks [4, 5, 27, 43–47, 76, 87, 99, 175, 182, 184, 236, 268] Fixations [1, 10, 54, 68, 99, 140, 153, 184, 219, 228] Smooth Pursuits and Ocular Movements [133] Cardiovascular Heart Rate [4, 10, 25, 27, 40, 41, 49, 50, 76, 78, 82, 85, 87, 99, 106, 146, 152,…”

Metadata

Authors
Seppelt, B., Seaman, S., Angell, L., Mehler, B., & Reimer, B.
Affiliations
  • Massachusetts Institute of Technology (US)
  • Touchstone Evaluations, Inc., Grosse Pointe, US
ORCID
Venue
Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Publisher
ACM
Published
2017-09-24
Pages
114-122
DOI
10.1145/3122986.3123019
OpenAlex
W2759314897
Cited by
13
References
1
Citations by year
2026: 2 · 2025: 3 · 2024: 3 · 2023: 3 · 2020: 1 · 2017: 1
FWCI
0.89
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) (publishedVersion)
Topics
  • Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
  • Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Keywords
cognitive load, speech interfaces, driver attention, human-machine interaction, driver attention monitoring

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