On the effectiveness of performance-based adaptive automation

ABSTRACT

This article examines the effectiveness of different forms of performance-based adaptive automation (PBAA). Using data from three experiments (N = 10, N = 38, N = 40), different models of algorithm design were compared for their effectiveness in driving PBAA. The following components were varied: type of task (i.e. primary or secondary tasks), baseline of performance data (e.g. moving average), and triggering criterion (i.e. level of deviation from standard performance). The data were generated by operators working with a computer-based simulation of a process control environment. The results showed that none of the models enjoyed a convincing level of effectiveness. The automation algorithms generally achieved higher levels of miss prevention than false alarm prevention. Surprisingly, primary task performance was generally better at driving PBAA than secondary task performance. The results suggest that it may be difficult to design an effective algorithm of PBAA if the work environment is highly complex.

KEYWORDS:

• Automation
• performance
• modelling
• signal detection

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Swiss National Science Foundation [grant number 116012], [grant number 134566].

Notes on contributors

Juergen Sauer

Juergen Sauer is Full Professor of Cognitive Ergonomics at the Department of Psychology at the University of Fribourg. He received an MSc in Occupational Psychology from the University of Sheffield, UK, in 1990 and a PhD in Psychology from the University of Hull, UK, in 1997.

Alain Chavaillaz

Alain Chavaillaz received his PhD in Psychology from the University of Fribourg in 2010. He is currently a senior researcher in cognitive ergonomics and a lecturer at the University of Fribourg. His main research interest is concerned with work in highly automated systems.

David Wastell

David Wastell is Emeritus Professor of Information Systems at Nottingham University Business School. His previous career involved positions at Manchester University, Salford University and UMIST.