A cognitive decomposition to empirically study human performance in control room environments

Knisely, Benjamin M.; Joyner, Janell S.; Rutkowski, Anthony M.; Wong, Matthew C.; Barksdale, Samuel; Hotham, Hayden; Kharod, Kush C.; Vaughn-Cooke, Monifa

doi:10.1016/j.ijhcs.2020.102438

Cited by 11 publications

(14 citation statements)

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“…CTAWC provides a baseline model of workload for the task and opens opportunities to evaluate changes in workload in response to design changes. This application was demonstrated in [18] and will be used to support explanations of methodological steps in subsequent sections.…”

Section: Methodsmentioning

confidence: 98%

“…This methodology is introduced in the context of its prior application in a companion paper that includes detailed experimental results [18] . The use case scenario is a control room monitoring task from the perspective of the operator, which will be introduced first.…”

Section: Methodsmentioning

confidence: 99%

“…Depending on the complexity of the overarching tasks, there may be many more tasks than can be feasibly tested in a laboratory experiment due to resource and recruitment burden. In the companion paper [18] , the lowest-level of the decomposition resulted in 29 individual tasks, which is too many to control in an experiment. There are several practical considerations related to the study length and resources.…”

Section: Methodsmentioning

confidence: 99%

“…Excessive cognitive workload has been associated with poor human performance and error [4 , 14 , 15] , while moderate levels of elevated cognitive workload has been linked to increased performance [16 , 17] . One recent study found that large, sudden spikes in cognitive workload corresponded to decreased performance, whereas consistent, elevated cognitive workload corresponded to increased performance [18] . Regardless, understanding the level of workload that operators experience in complex systems is critical to optimize the system for human performance.…”

Section: Introductionmentioning

confidence: 99%

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Cognitive task analysis and workload classification

2021

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Automation can be utilized to relieve humans of difficult and repetitive tasks in many domains, presenting the opportunity for safer and more efficient systems. This increase in automation has led to new supervisory roles for human operators where humans monitor feedback from autonomous systems and provide input when necessary. Optimizing these roles requires tools for evaluation of task complexity and resulting operator cognitive workload. Cognitive task analysis is a process for modeling the cognitive actions required of a human during a task. This work presents an enhanced version of this process: Cognitive Task Analysis and Workload Classification (CTAWC). The goal of developing CTAWC was to provide a standardized process to decompose cognitive tasks in enough depth to allow for precise identification of sources of cognitive workload. CTAWC has the following advantages over conventional CTA methodology: Integrates standard terminology from existing taxonomies for task classification to describe expected operator cognitive workload during task performance. Provides a framework to evaluate adequate cognitive depth when decomposing cognitive tasks. Provides a standard model upon which to build an empirical study to evaluate task complexity.

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Section: Methodsmentioning

confidence: 98%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cognitive task analysis and workload classification

2021

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“…Typically, direct objective measurements of performance are utilized, including task accuracy, timing, and failure rates [9][10][11]. Neurophysiological data, such as pupillometry [12,13] and heart rate data [14], can be used as an indirect measure of human performance by serving as a measure of cognitive workload. Cognitive workload is a multidimensional construct used to represent the effort required by human working memory [15].…”

Section: Traditional Approaches To Quantify Human Performancementioning

confidence: 99%

Virtual Modeling of User Populations and Formative Design Parameters

Knisely

Vaughn-Cooke

2020

Systems

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Human variability related to physical, cognitive, socio-demographic, and other factors can contribute to large differences in human performance. Quantifying population heterogeneity can be useful for designers wishing to evaluate design parameters such that a system design is robust to this variability. Comprehensively integrating human variability in the design process poses many challenges, such as limited access to a statistically representative population and limited data collection resources. This paper discusses two virtual population modeling approaches intended to be performed prior to in-person design validation studies to minimize these challenges by: (1) targeting recruitment of representative population strata and (2) reducing the candidate design parameters being validated in the target population. The first approach suggests the use of digital human models, virtual representations of humans that can simulate system interaction to eliminate candidate design parameters. The second approach suggests the use of existing human databases to identify relevant human characteristics for representative recruitment strata in subsequent studies. Two case studies are presented to demonstrate each approach, and the benefits and limitations of each are discussed. This paper demonstrates the benefit of modeling prior to conducting in-person human performance studies to minimize resource burden, which has significant implications on early design stages.

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