Statistical modelling of networked human-automation performance using working memory capacity
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<title>Statistical modelling of networked human-automation performance using working memory capacity</title>
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<abstract displayLabel="Summary">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.</abstract>
<note type="statement of responsibility">Nisar Ahmed...[et.al]</note>
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<title>Ergonomics : the international journal of research and practice in human factors and ergonomics</title>
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<publisher>Oxon [United Kingdom] : Taylor & Francis, 2010-</publisher>
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<identifier type="issn">0014-0139</identifier>
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<text>03/03/2014 Volumen 57 Número 3 - marzo 2014 </text>
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