title: Statistical modelling of networked human-automation performance using working memory capacity.
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.
authors.affiliation: a Autonomous Systems Laboratory, Department of Mechanical and Aerospace Engineering , Cornell University , 155 Rhodes Hall, Ithaca , NY 14853 , USA.
authors.forename: Nisar
authors.initials: N
authors.lastname: Ahmed
authors.forename: Ewart
authors.initials: E
authors.lastname: de Visser
authors.forename: Tyler
authors.initials: T
authors.lastname: Shaw
authors.forename: Amira
authors.initials: A
authors.lastname: Mohamed-Ameen
authors.forename: Mark
authors.initials: M
authors.lastname: Campbell
authors.forename: Raja
authors.initials: R
authors.lastname: Parasuraman
mesh_terms: D000401:Aircraft; D001288:Attention; D001499:Bayes Theorem; D003198:Computer Simulation; D003657:Decision Making; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008328:Man-Machine Systems; D008570:Memory, Short-Term; D012044:Regression Analysis; D012371:Robotics; D018709:Statistics, Nonparametric; D013647:Task Performance and Analysis; D016526:Workload / Q000523:psychology