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Mental Workload: Assessment, Prediction and Consequences

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Human Mental Workload: Models and Applications (H-WORKLOAD 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 726))

Abstract

I describe below the manner in which workload measurement can be used to validate models that predict workload. These models in turn can be employed to predict the decisions that are made, which select a course of action that is of lower effort or workload, but may also be of lower expected value (or higher expected cost). We then elaborate on four different contexts in which these decisions are made, with non-trivial consequences to performance and learning: switching attention, accessing information, studying material, and behaving safety. Each of these four is illustrated by a series of examples.

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Author information

Authors and Affiliations

  1. University of Illinois at Urbana-Champaign, Chicago, USA

    Christopher D. Wickens

  2. Colorado State University, Fort Collins, USA

    Christopher D. Wickens

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  1. Christopher D. Wickens
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Correspondence to Christopher D. Wickens .

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Editors and Affiliations

  1. Dublin Institute of Technology, Dublin, Ireland

    Luca Longo

  2. Dublin Institute of Technology, Dublin, Ireland

    M. Chiara Leva

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Wickens, C.D. (2017). Mental Workload: Assessment, Prediction and Consequences. In: Longo, L., Leva, M. (eds) Human Mental Workload: Models and Applications. H-WORKLOAD 2017. Communications in Computer and Information Science, vol 726. Springer, Cham. https://doi.org/10.1007/978-3-319-61061-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-61061-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61060-3

  • Online ISBN: 978-3-319-61061-0

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