This paper presents the utility of using a neural network to model a human-automation interaction taxonomy. Automation, context, and operator features which are believed to influence human-automation interaction are identified, and the effect of changing these features on human-automation interaction are transformed from a conceptual model linkage to a computational model in the form of a neural network. The theoretical requirements of transforming the model into a computational neural network capable of analysis are discussed, and ongoing efforts to collect the required data are outlined. Additionally, the various analyses which the computational modeling enables are described. As a case study, the work uses pilots and their use of automation in the flight deck.
Using neural networks to assess human-automation interaction
2011 IEEE/AIAA 30th Digital Avionics Systems Conference ; 6A4-1-6A4-10
2011-10-01
143752 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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