Statistical modelling of networked human-automation performance using working memory capacity

    Authors

    N. Ahmed; E. de Visser; T. Shaw; A. Mohamed-Ameen; M. Campbell;R. Parasuraman

    Comments

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    Abbreviated Journal Title

    Ergonomics

    Keywords

    networked human-automation systems; predictive statistical models; linear and Gaussian process regression; Bayesian networks; inverse; reasoning; working memory; SUPERVISORY CONTROL; SITUATION AWARENESS; ATTENTION; WORKLOAD; VEHICLES; SYSTEMS; Engineering, Industrial; Ergonomics; Psychology, Applied; Psychology

    Abstract

    This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity.Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

    Journal Title

    Ergonomics

    Volume

    57

    Issue/Number

    3

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    295

    Last Page

    318

    WOS Identifier

    WOS:000333877900002

    ISSN

    0014-0139

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