Training Robust Decision Making in Immersive Environments

Patterson, Robert; Pierce, Byron J.; Bell, Herbert H.; Andrews, Dee H.; Winterbottom, Marc

doi:10.1518/155534309x12599553478836

Cited by 34 publications

(11 citation statements)

References 93 publications

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“…Intuitive cognition was largely at play when unreliable automation/high workload produced less impairment in performance at the information analysis, Stage 2, whereas analytical cognition was largely at play when unreliable automation/high workload produced a larger impairment at the decision/action selection, Stage 3. The differing degrees of impairment were likely due to the differential susceptibility of intuitive cognition (low susceptibility) versus analytical cognition (high susceptibility) to time pressure and workload (e.g., Evans, 2008;Evans & Stanovich, 2013;Patterson et al, 2009; see also Klein, 1998Klein, , 2008. This critical boundary likely arises from differential engagement of analytical versus intuitive cognition by the different types of automation used in different conditions of the studies.…”

Section: Consequences Of Neglecting Intuitive Cognitionmentioning

confidence: 99%

Intuitive Cognition and Models of Human–Automation Interaction

Patterson

2017

Hum Factors

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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.

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Section: Consequences Of Neglecting Intuitive Cognitionmentioning

confidence: 99%

Intuitive Cognition and Models of Human–Automation Interaction

Patterson

2017

Hum Factors

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“…Pattern recognition is based on implicit learning, which refers to learning without intention and without being able to verbalize easily what has been learned. 39,40 Pattern recognition underlies the ability to make intuitive decisions 14 as well as the development of expertise. 41 We now turn to eight principles of good display design based on the brief discussion of human cognition given above.…”

Section: Pattern Recognitionmentioning

confidence: 99%

“…One form of implicit learning entails the process of learning statistical regularities without intention and without being able to verbalize easily what has been learned. 39,40 It is a ubiquitous, robust process that likely provides a foundation for our ability to make intuitive decisions based on situational pattern recognition 14 (Fig. 1), which we do on a frequent basis every day of our lives.…”

mentioning

confidence: 99%

“…The cognitive processes reviewed in this section can be conceptualized as comprising part of a dual-process model for reasoning and decision making, as reviewed by Patterson et al14 That source should be consulted for a more thorough discussion of that model. In the present paper, a set of cognitive processes is represented as a functional flow diagram, shown inFig.…”

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confidence: 99%

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Cognitive engineering, cognitive augmentation, and information display

Patterson¹

2012

J Soc Info Display

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Abstract— From a perspective of cognitive engineering, this paper presents eight principles of good display design based on human cognition: (1) displays that divide attention will increase the cognitive load on working memory; (2) displays that continuously present the same information may compensate for the decay of short‐term memory; (3) displays that assist the viewer in mentally chunking information will decrease the cognitive load on working memory; (4) displays that present icons or images to direct attention to important visual stimuli will likely minimize inattentional blindness; (5) displays that help minimize attentional distractions and help focus attention on a given task will minimize the burden on working memory; (6) displays that present cues composed of strong singletons will capture and engage exogenous attention; (7) displays that help the viewer make a mental connection between an analogue and a target will assist in the induction of analogical reasoning; (8) displays that present over time multiple exposures consisting of statistical regularities may promote implicit learning and expertise development. Thus, information displays of the future can systematically engage high‐level human cognitive processes for purposes of improving human performance on a variety of tasks found in education, the military, and many other applications.

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“…In the dual-process framework of reasoning and decision making (e.g., Evans, 2008;Patterson, Pierce, Bell, Andrews & Winterbottom, 2009;Sloman, 1996), one mode of decision making is called intuitive. Intuitive decision making refers to implicit situational pattern recognition that is not thought to involve symbolic rules (Klein, 1998).…”

Section: Introductionmentioning

confidence: 99%

Modeling Intuitive Decision Making in ACT-R

Kennedy¹,

Patterson²

2012

PsycEXTRA Dataset

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One mode of human decision-making is considered intuitive, i.e., unconscious situational pattern recognition. Implicit statistical learning, which involves the sampling of invariances from the environment and is known to involve procedural (i.e., non-declarative) memory, has been shown to be a foundation of this mode of decision making. We present an ACT-R model of implicit learning whose implementation entailed a declarative memory-based learner of the classification of example strings of an artificial grammar. The model performed very well when compared to humans. The fact that the simulation of implicit learning could not be implemented in a straightforward way via a non-declarative memory approach, but rather required a declarative memorybased implementation, suggests that the conceptualization of procedural memory in the ACT-R framework may need to be expanded to include abstract representations of statistical regularities. Our approach to the development and testing of models in ACT-R can be used to predict the development of intuitive decision-making in humans.

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Training Robust Decision Making in Immersive Environments

Cited by 34 publications

References 93 publications

Intuitive Cognition and Models of Human–Automation Interaction

Intuitive Cognition and Models of Human–Automation Interaction

Cognitive engineering, cognitive augmentation, and information display

Modeling Intuitive Decision Making in ACT-R

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