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Latency Differences Between Mental Workload Measures in Detecting Workload Changes

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Human Mental Workload: Models and Applications (H-WORKLOAD 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1012))

Abstract

Mental workload has traditionally been measured by three different methods corresponding to its primary reflections: performance, subjective and physiological measures. Although we would expect a certain degree of convergence, research has shown that the emergence of disassociations and insensitivities between measures is very frequent. One possible explanation could be related to the differing latencies between each workload assessment method. We tested this explanation by manipulating task complexity through time spent performing a simulated air-traffic control task. In the experimental session, we collected physiological (pupil size), performance and subjective data. Our results showed two periods of bad performance caused by high traffic density and aircraft configurations. Those periods corresponded to higher mental workload as detected by subjective and physiological measures. However, subjective mental workload reacted sooner than physiological mental workload to task demands. These results suggest that the differences in latency could partially explain mental workload dissociations and insensitivities between measures.

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Authors and Affiliations

  1. Mind, Brain and Behaviour Research Centre, University of Granada, Granada, Spain

    Enrique Muñoz-de-Escalona & José Juan Cañas

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  1. Enrique Muñoz-de-Escalona
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  2. José Juan Cañas
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Correspondence to Enrique Muñoz-de-Escalona .

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  1. Dublin Institute of Technology, Dublin, Ireland

    Luca Longo

  2. Dublin Institute of Technology, Dublin, Ireland

    M. Chiara Leva

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Muñoz-de-Escalona, E., Cañas, J.J. (2019). Latency Differences Between Mental Workload Measures in Detecting Workload Changes. In: Longo, L., Leva, M. (eds) Human Mental Workload: Models and Applications. H-WORKLOAD 2018. Communications in Computer and Information Science, vol 1012. Springer, Cham. https://doi.org/10.1007/978-3-030-14273-5_8

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  • DOI: https://doi.org/10.1007/978-3-030-14273-5_8

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