Title

Statistical Modelling Of Networked Human-Automation Performance Using Working Memory Capacity

Keywords

Bayesian networks; inverse reasoning; linear and Gaussian process regression; networked human-automation systems; predictive statistical models; working memory

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. © 2014 © 2013 Taylor & Francis.

Publication Date

1-1-2014

Publication Title

Ergonomics

Volume

57

Issue

3

Number of Pages

295-318

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/00140139.2013.855823

Socpus ID

84897438445 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84897438445

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