Implementing Lumberjacks and Black Swans Into Model-Based Tools to Support Human–Automation Interaction. (March 2017)

• Record Type: Journal Article
• Authors: Sebok, Angelia; Wickens, Christopher D.
• Other Names: Salas Eduardo guest-editor.; Marquez Jessica J. guest-editor.; Gore Brian F. guest-editor.
• Is Part Of: Human factors. Volume 59:Number 2(2017)
• Page Start: 189
• Page End: 203
• Publication Date: 2017-03
• Subjects: human–automation interaction -- human performance modeling -- levels of automation -- attentional processes -- situation awareness; Human engineering -- Periodicals; 620.82
• DOI: 10.1177/0018720816665201
• Languages: English
• ISSNs: 0018-7208

Abstract

Objective: The objectives were to (a) implement theoretical perspectives regarding human–automation interaction (HAI) into model-based tools to assist designers in developing systems that support effective performance and (b) conduct validations to assess the ability of the models to predict operator performance. Background: Two key concepts in HAI, the lumberjack analogy and black swan events, have been studied extensively. The lumberjack analogy describes the effects of imperfect automation on operator performance. In routine operations, an increased degree of automation supports performance, but in failure conditions, increased automation results in more significantly impaired performance. Black swans are the rare and unexpected failures of imperfect automation. Method: The lumberjack analogy and black swan concepts have been implemented into three model-based tools that predict operator performance in different systems. These tools include a flight management system, a remotely controlled robotic arm, and an environmental process control system. Results: Each modeling effort included a corresponding validation. In one validation, the software tool was used to compare three flight management system designs, which were ranked in the same order as predicted by subject matter experts. The second validation compared model-predicted operator complacency with empirical performance in the same conditions. The third validation compared model-predicted and empirically determined time to detect and repair faults in four automation conditions. Conclusion: The three model-based tools offer useful ways to predict operator performance in complex systems. Application: The three tools offer ways to predict the effects of different automation designs on operator performance.