Intuitive Cognition and Models of Human-Automation Interaction

Abstract

Objective: The aim of this study was to provide an analysis of the implications of the dominance of intuitive cognition in human reasoning and decision making for conceptualizing models and taxonomies of human-automation interaction, focusing on the Parasuraman et al. model and taxonomy. Background: Knowledge about how humans reason and make decisions, which has been shown to be largely intuitive, has implications for the design of future human-machine systems. Method: One hundred twenty articles and books cited in other works as well as those obtained from an Internet search were reviewed. Works were deemed eligible if they were published within the past 50 years and common to a given literature. Results: Analysis shows that intuitive cognition dominates human reasoning and decision making in all situations examined. The implications of the dominance of intuitive cognition for the Parasuraman et al. model and taxonomy are discussed. A taxonomy of human-automation interaction that incorporates intuitive cognition is suggested. Application: Understanding the ways in which human reasoning and decision making is intuitive can provide insight for future models and taxonomies of human-automation interaction.