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Task Demand Transition Rates of Change Effects on Mental Workload Measures Divergence

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Book cover Human Mental Workload: Models and Applications (H-WORKLOAD 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1107))

Abstract

Mental workload is a complex construct that may be indirectly inferred from physiological responses, as well as subjective and performance ratings. Since the three measures should reflect changes in task-load, one would expect convergence, yet divergence between the measures has been reported. A potential explanation could be related to the differential sensitivity of mental workload measures to rates of change in task-load transitions: some measures might be more sensitive to change than the absolute level of task demand. The present study aims to investigate whether this fact could explain certain divergences between mental workload measures. This was tested by manipulating task-load transitions and its rate of change over time during a monitoring experiment and by collecting data on physiological, subjective, and performance measures. The results showed higher pupil size and performance measure sensitivity to abrupt task-load increases: sensitivity to rates of change could partially explain mental workload dissociations and insensitivities between measures.

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Author information

Authors and Affiliations

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

    Enrique Muñoz-de-Escalona & José Juan Cañas

  2. Faculty of Psychology and Neuroscience, University of Maastricht, Maastricht, The Netherlands

    Jair van Nes

Authors
  1. Enrique Muñoz-de-Escalona
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  2. José Juan Cañas
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  3. Jair van Nes
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Corresponding author

Correspondence to Enrique Muñoz-de-Escalona .

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Editors and Affiliations

  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

  2. Technological University Dublin, Dublin, Ireland

    Maria Chiara Leva

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Muñoz-de-Escalona, E., Cañas, J.J., van Nes, J. (2019). Task Demand Transition Rates of Change Effects on Mental Workload Measures Divergence. In: Longo, L., Leva, M. (eds) Human Mental Workload: Models and Applications. H-WORKLOAD 2019. Communications in Computer and Information Science, vol 1107. Springer, Cham. https://doi.org/10.1007/978-3-030-32423-0_4

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  • DOI: https://doi.org/10.1007/978-3-030-32423-0_4

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  • Print ISBN: 978-3-030-32422-3

  • Online ISBN: 978-3-030-32423-0

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