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 humanautomation 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 humanautomation 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.

A systematic review of algorithm aversion in augmented decision making

“…Essentially, this entails an augmented decision making system in which the human user semisupervises the algorithm by having opportunities to intervene, provide input, and have the final say. As described in the reviewed literature, such decision making systems can take shape in a variety of ways, such as interactive support systems (Lim & O'Connor, 1996), humanautomation systems (Patterson, 2017), engaged systems (Pagano et al, 2016), constructive dialog in expert systems (Eining, Jones, & Loebbecke, 1997), judgmental systems (Prahl & Van Swol, 2017), or procedural presentation in interfaces (Lamberti & Wallace, 1990). Nevertheless, Dietvorst et al (2016) highlight an important new consideration: that people are relatively insensitive to the amount by which they can modify the imperfect algorithm's forecasts as long as they are able to incorporate their own input and participate in the ultimate decision (p. 1161).…”

Modelling human cognition of abnormal machine behaviour

“…In the management domain, intuition is defined as Ba capacity for attaining direct knowledge or understanding without the apparent intrusion of rational thoughts or logical inference^(Saddler- Smith and Shefy 2004) and as Baffectively-charged judgments that arise through rapid, non-conscious, and holistic associations^ (Dane and Pratt 2007). Despite the number of definitions, recent studies (Evans and Stanovich 2013;Patterson 2017) agree on certain factors in intuitive cognition:…”

“…Reyna 2012); & It has autonomous processing capabilities (e.g. Evans and Stanovich 2013;Patterson 2017); & It has the ability to process multiple cues simultaneously (e.g. Evans and Stanovich 2013); & It has a higher propensity to make biased responses (e.g.…”

Seeing the woods for the trees: the problem of information inefficiency and information overload on operator performance

“…Again, this has parallels with the theoretical framework that later stages of information processing are more dependent on effortful, conscious problem-solving and decision-making (Patterson 2017), and are more prone to being slowed or misdirected by spurious information, even if that information is potentially predictive in supporting future alarm handling episodes (Level 3; Dadashi et al 2014). The stages of alarm handling that are more likely to be skilled, perceptual and pattern-matching are less prone to the effects of information overload and spurious information (Patterson 2017).…”

“…In particular, notification supports information acquisition, diagnosis supports decision selection, and clearance is a form of action implementation that has been selected, and automation or technological support for alarm handling can be described within the Parasuraman et al (2000) framework. Recent work (Patterson 2017) has put forward the view that different stages of information processing are underpinned by different cognitive systems-specifically that information acquisition and information analysis, the first two stages of the Parasuraman et al (2000) model are underpinned by skill, pattern-matching, and largely heuristic processes. The later stages of decision selection and action implementation are more effortful than rule and knowledge-based cognitive processes.…”