The Workload Curve

Estes, Steven

doi:10.1177/0018720815592752

Cited by 36 publications

(14 citation statements)

References 55 publications

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“…As a multidimensional construct, the mental workload has been generally defined in terms of the resources available to meet a task's demands [143][144][145][146][147][148][149][150][151][152][153]. Not only does excessively high workload reduce human performance, but a workload that is too low reduces the motivation and interest of the operator for the job [154].…”

Section: The Effect Of Mental Workloadmentioning

confidence: 99%

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

Ismail

Karwowski

2020

PLoS ONE

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Background Neuroergonomics combines neuroscience with ergonomics to study human performance using recorded brain signals. Such neural signatures of performance can be measured using a variety of neuroimaging techniques, including functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG). EEG has an excellent temporal resolution, and EEG indices are highly sensitive to human brain activity fluctuations. Objective The focus of this systematic review was to explore the applications of EEG indices for quantifying human performance in a variety of cognitive tasks at the macro and micro scales. To identify trends and the state of the field, we examined global patterns among selected articles, such as journal contributions, highly cited papers, affiliations, and high-frequency keywords. Moreover, we discussed the most frequently used EEG indices and synthesized current knowledge regarding the EEG signatures of associated human performance measurements. Methods In this systematic review, we analyzed articles published in English (from peer-reviewed journals, proceedings, and conference papers), Ph.D. dissertations, textbooks, and reference books. All articles reviewed herein included exclusively EEG-based experimental studies in healthy participants. We searched Web-of-Science and Scopus databases using specific sets of keywords. Results Out of 143 papers, a considerable number of cognitive studies focused on quantifying human performance with respect to mental fatigue, mental workload, mental effort, visual fatigue, emotion, and stress. An increasing trend for publication in this area was observed, with the highest number of publications in 2017. Most studies applied linear methods (e.g., EEG power spectral density and the amplitude of event-related potentials) to evaluate human cognitive performance. A few papers utilized nonlinear methods, such as fractal dimension, largest Lyapunov exponent, and signal entropy. More than 50% of the studies focused on evaluating an individual’s mental states while operating a vehicle. Several different methods of artifact removal have also been noted. Based on the reviewed articles, research gaps, trends, and potential directions for future research were explored. Conclusion This systematic review synthesized current knowledge regarding the application of EEG indices for quantifying human performance in a wide variety of cognitive tasks. This knowledge is useful for understanding the global patterns of applications of EEG indices for the analysis and design of cognitive tasks.

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Section: The Effect Of Mental Workloadmentioning

confidence: 99%

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

Ismail

Karwowski

2020

PLoS ONE

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“…Defining Attributes Andre (2001), Ayaz et al (2012), Crawford and Cacioppo (2002), de Winter (2014), Engström et al 2017, Estes (2015), Fairclough (2001), Gaillard (1993), Gopher and Donchin (1986), Hancock and Warm (1989), Hart and Staveland (1988), Hart and Wickens (2010), Hilburn and Jorna (2001), Hockey (1997), Johannsen (1979), Kramer et al (1987), Langner and Eickhoff (2013), Mandrick et al (2016), Matthews et al (2002Matthews et al ( , 2015, McGrath (1976), Moray (1979), Pickup et al (2005), Proctor and Vu (2012), Salvendy (2012), Schneider and Shiffrin (1977), Staal (2004), Stork and Schubö (2010), Warm et al (2008), Wickens (2002Wickens ( , 2008, Wickens and Carswell (2012), Wickens and Hollands (2000), Wickens and Tsang (2014), Xie and Salvendy (2000), Young and Stanton (2001), Young et al (2014), Zijlstra (1993) Antecedents Basil (2012), Hart and Staveland (1988), Kahneman (1973), Layer et al (2009), Mandler (1979), Moray (1979), Neerincx et al 2000,…”

Section: Key Referencesmentioning

confidence: 99%

Understanding mental workload: from a clarifying concept analysis toward an implementable framework

et al. 2018

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The growing need for mental workload (MWL) optimization on the shop floor yields an impressive increase in theoretical and applied references to the concept of mental workload (Young et al. 2014). However, do we really understand and agree upon what mental workload exactly is? Does it include emotional load? Can we rely upon an explanatory framework? The present account first runs a critical concept analysis on mental workload, based on the Walker and Avant (2011) method. Results show that existing definitions and theoretical accounts arbitrarily include and exclude defining variables and describe these variables on various levels of abstraction, misuse pivotal terms such as mediation and moderation, and do not theoretically explicitate the role of yet repeatedly operationalized emotional load variables such as frustration. We therefore clarify the concept by disentangling MWL into its antecedents, defining attributes and consequences. Next, we derive a clearcut conceptual definition and present a generic explanatory frameworkthe latter extended with insights from Cognitive Load Theory (Sweller 1988; 1994). We conclude with a set of suggestions for future research and practice. Next to contributing to the theoretical clarification of this hallmark concept, the concept analysis and derived explanatory framework, as proposed, can foster solid research practices and support practitioners in contextualizing MWL-assessment and in effectively optimizing MWL.

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“…There is a clear need for more cost-effective human performance modeling in the beginning of systems development and the necessity to develop an architecture that can support analysis of changes in critical human parameters. While some fields within systems engineering attempt to address this (for example, agile systems engineering), even these approaches tend to treat the user as external actor to the system, rather than a key and integrated component of a successful system (Douglass 2016).…”

Section: Previous Researchmentioning

confidence: 99%

“…With MBSE, the primary artifact of the life cycle process is a single coherent model as opposed to a series of documentbased products (e.g., a requirements specification) (Friedenthal et al 2008;Douglass 2016;Delligatti 2013). These documents may be produced using MBSE, but they are not the primary focus.…”

Section: Previous Researchmentioning

confidence: 99%

“…The final MBSE pillar, the modeling tool, provides a means to build a system model that is consistent with the modeling language. These three pillars, when combined, provide powerful benefits to the system modeler-especially in determining the impact of the inevitable changes in a system design (Douglass 2016). While MBSE is a useful technique, typical systems engineering techniques tend to treat the human user of the system as an external actor-as opposed to a detailed portion of the system that also needs to be incorporated in the system design (Delligatti 2013;Madni 2009).…”

Section: Previous Researchmentioning

confidence: 99%

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A method for the inclusion of human factors in system design via use case definition

Liaghati

Mazzuchi

Sarkani

2020

Hum.-Intell. Syst. Integr.

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The necessity of considering human factors in the early phases of intelligent systems engineering is increasing in tandem with overall system complexity and size. With this increased need, the capabilities and limitations of human-systems integration (HSI) are becoming more of a focus in the design of intelligent systems. Systems have been growing progressively more complex as the system is required to perform complicated tasks in an increasingly complex environment. However, the current state of system design and engineering processes is often insufficient or too late in the system life cycle to adequately address human-intelligent systems integration. As a result, emergent problems related to human factors arise late in the system life cycle, often even after system deployment, resulting in additional cost, time, and liability. This research proposes a method for including human factors early and throughout the systems engineering process utilizing use case definitions and associated diagrams that show relationships with external actors, including humans. Human performance, task analysis, and Goals, Operators, Methods, Selection rules (GOMS) models produce quantitative metrics for human factors to be included in the system design process. System use case definitions are a natural pathway for the inclusion of human factors early and throughout the systems life cycle, enabling the consideration of quantitative human-systems integration metrics in the system design process. Keywords Human-systems integration (HSI). Human factors (HF). Model-based systems engineering (MBSE). Systems Modeling Language (SysML). Artificial intelligence (AI)

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The Workload Curve

Abstract: Measures of subjective workload are used across a wide variety of domains and applications. These results bear on their interpretation, particularly as they relate to workload thresholds.

Cited by 36 publications

References 55 publications

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

Understanding mental workload: from a clarifying concept analysis toward an implementable framework

A method for the inclusion of human factors in system design via use case definition

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