Accelerating Understanding of Human Response to Automation Failure

Abstract

Firstly, I comment on the lack of support for the predictions of the lumberjack model to professionally qualified operators in high-fidelity work simulations (Jamieson & Skraaning, 2020a). I highlight the advantages that Bayesian statistics provide for qualifying the degree of evidence for the null hypotheses, issues concerning situation awareness measurement, and the alternative techniques available to study experts. Secondly, I comment on the innovative taxonomy of automation failure presented by Skraaning and Jamieson (2024), pointing out some issues with overlapping definitions and lack of cause-effect relationships. I then discuss the substantial opportunity this taxonomy presents to guide future research, such as the design of transparent automation. To conclude, I identify some other key problems regarding how we currently study human-automation teaming (e.g. presenting randomized automation failure unlinked to task context) and invite discussion from the research community on the relevance of computational modelling to this field of research.