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.


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    Title :

    Using neural networks to assess human-automation interaction


    Contributors:


    Publication date :

    2011-10-01


    Size :

    143752 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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