Using neural networks to assess human-automation interaction

Katlyn B. Sullivan, Karen M. Feigh, Francis T. Durso, Ute Fischer, Vlad L. Pop, Kathleen Mosier, Justin Blosch, Dan Morrow

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication30th Digital Avionics Systems Conference - Closing the Generation Gap
Subtitle of host publicationIncreasing Capability for Flight Operations among Legacy, Modern and Uninhabited Aircraft, DASC 2011
Pages6A41-6A410
DOIs
StatePublished - 2011
Event30th Digital Avionics Systems Conference - Closing the Generation Gap: Increasing Capability for Flight Operations among Legacy, Modern and Uninhabited Aircraft, DASC 2011 - Seattle, WA, United States
Duration: Oct 16 2011Oct 20 2011

Publication series

NameAIAA/IEEE Digital Avionics Systems Conference - Proceedings

Other

Other30th Digital Avionics Systems Conference - Closing the Generation Gap: Increasing Capability for Flight Operations among Legacy, Modern and Uninhabited Aircraft, DASC 2011
Country/TerritoryUnited States
CitySeattle, WA
Period10/16/1110/20/11

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Using neural networks to assess human-automation interaction'. Together they form a unique fingerprint.

Cite this