Framing Automation and Human Error in the Context of the Skill, Rule and Knowledge Taxonomy
article
Automation errors may result in human performance issues that are often difficult to grasp. Skraaning and Jamieson (2023) proposed a taxonomy for classifying automation errors into categories based on the visible symptoms of design problems, so as to benefit the design of training scenarios. In this paper, we propose a complementary classification that is based on the mechanisms of human-automation interaction guided by Rasmussen’s Skill, Rule and Knowledge (SRK) taxonomy. We identified four main failure classes and expect that this classification can support automation designers. (C) 2024, Human Factors and Ergonomics Society.
Topics
TNO Identifier
995003
ISSN
15553434
Source
Journal of Cognitive Engineering and Decision Making, 18(4), pp. 318-326.
Pages
318-326