Calibrating adaptable automation to individuals

Jennifer E. Thropp, Tal Oron-Gilad, James L. Szalma, Peter A. Hancock

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

A detailed understanding of operator individual differences can serve as a foundation for developing a critical window on effective, adaptable, user-centered automation, and even for more autonomous systems. Adaptable automation that functions according to such principles and parameters has many potential benefits in increasing operator trust and acceptance of the automated system. Our current study provides an assessment of the way that individual differences in attentional control (AC) affect the preference for a selection of a desired level of automation (LOA). Participants who scored low or high on AC were either allowed to choose among four possible LOAs or restricted to a predetermined LOA. These manipulations were engaged while the operator was performing visual and auditory target detection tasks. The AC level was found to be inversely proportional to the LOA preference. Operators also performed better when they were preassigned to a fixed LOA rather than given a choice. Individual differences can thus be shown to affect the performance with the automated systems and should be considered in associated design processes. When deciding whether to give the operator control over LOA in a complex system, engineers should consider that the amount of control that operators may want does not necessarily reflect their actual needs.

Original languageEnglish
Article number8396314
Pages (from-to)691-701
Number of pages11
JournalIEEE Transactions on Human-Machine Systems
Volume48
Issue number6
DOIs
StatePublished - 1 Dec 2018

Keywords

  • Human-automation interaction
  • individual differences

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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