Workload Is Multidimensional, Not Unitary: What Now?

Matthews, Gerald; Reinerman-Jones, Lauren; Wohleber, Ryan W.; Lin, Jie; Mercado, Joe; Abich, Julian

doi:10.1007/978-3-319-20816-9_5

Cited by 21 publications

(27 citation statements)

References 25 publications

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“…Recently, Young et al (2014) provided an essential overview of the concept's current state-of-science. As these authors and others (cf., Xie and Salvendy 2000;Staal 2004;Matthews et al 2015a;Mandrick et al 2016) however point out, the understanding of mental workload still vastly lacks theoretical coherence. As Longo (2015) notes, various definitions have been suggested, but all include different workload variables based on different fields of application, beliefs, and even intuition (see also, Young and Stanton 2002).…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…We cannot preclude the possibility that advances in methodology and measurement technology together with advancements in resource theory will eventually support a latent factor model resembling that of Figure 1. However, given the current lack of evidence for a unitary latent factor (Matthews et al 2015b), the present research question is how to interpret subjective workload measures in the absence of strong convergence with objective indices. If no single latent construct underpins the various subjective and objective workload measures that are available, they cannot all be considered interchangeable measures of the resource/demand balance.…”

Section: What If Workload Is Non-unitary?mentioning

confidence: 99%

“…Second, in experimental studies, multivariate patterns of workload response may vary substantially across different task domains. Matthews et al (2015b) summarized evidence from four simulation studies utilizing multiple sensors (see also Hancock and Matthews 2018, Table 2). Workload manipulations were appropriate to differing applied contexts such as nuclear power plant control and operations of unmanned ground and aerial vehicles.…”

Section: Divergence Of Subjective and Objective Workload Assessmentsmentioning

confidence: 99%

“…For example, low heart rate variability (HRV) and high-frequency EEG (beta) are commonly taken as indicators of high workload. However, the studies reviewed by Matthews et al (2015b) included instances of task demands increasing HRV and decreasing beta power, as well as studies showing the opposite effects. Some studies, of course, do show convergence between subjective, physiological and secondary task measures (e.g.…”

Section: Divergence Of Subjective and Objective Workload Assessmentsmentioning

confidence: 99%

“…A potential solution to the difficulties of psychological measurement (Michell 1999) is to base workload assessment on physiological attributes of brain-functioning, such as activity of brain regions activated by task demands. This approach is worth pursuing, but difficulties include the constructive nature of subjective experience (Annett 2002), the complex interconnectivity of different brain areas and the weakness of associations between subjective workload and psychophysiological measures in empirical data (Matthews et al 2015b). The foundational resource theory for workload (Young et al 2015) suggests that measurement units might be virtual rather than physical, i.e.…”

Section: Representational Aspects Of Subjective Workload Assessmentsmentioning

confidence: 99%

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What do subjective workload scales really measure? Operational and representational solutions to divergence of workload measures

Matthews

Winter

Hancock

2019

Theoretical Issues in Ergonomics Science

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We examine the continuing use of subjective workload responses to index an operator's state, either by themselves or as part of a collective suite of measurements. Lack of convergence of subjective scales with physiological and performance-based measures calls into question whether there is any unitary workload construct that underpins conscious experience, physiological state and the individual's profile of task-related performance. We examine philosophical and measurement perspectives on the divergence problem, and we consider three possible solutions. First, difficulties in reliable and valid measurement of workload may contribute to divergence but do not fully explain it. Second, workload may be treated operationally: use of specific measures is justified by demonstrating their pragmatic utility in predicting important outcomes. Third, further efforts may be made to develop representational workload measurements that correspond to real empirical phenomena. Application of formal standards for test validity can identify multiple latent constructs supporting subjective workload, including those defining self-regulation in performance contexts. Physiological and performance-based assessments may define additional, distinct constructs. A resolution of the diversity issue is crucial for ergonomics since the invalid application of workload measurement will threaten exposed operators as well as many others who are served by the complex technological systems they control. Relevance to human factors/Relevance to ergonomics theory Workload assessment is important for multiple applications of ergonomics, but subjective and objective workload measures often diverge. Current ergonomics theory fails to explain this divergence. This article provides two possible solutions to this challenge. First, specific workload measures may be treated as operational variables that predict important real-world outcomes. This approach eschews theory in favor of pragmatic utility. It is compatible with emerging "big data" approaches to performance prediction. Second, subjective measures ARTICLE HISTORY

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Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight

Qin

Liu

et al. 2021

Hum Ftrs & Erg Mfg Svc

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Pilot mental fatigue is a growing concern in the aviation field due to its significant contributions to human errors and aviation accidents. Long work hours, sleep loss, circadian rhythm disruption, and workload are well‐known reasons, but there is a need to accurately detect pilot mental fatigue to improve aviation safety. However, due to the highly restricted cockpit environment and the complex nature of mental fatigue, feasible in‐flight detection remains under‐investigated. The objective of this study is to define a promising approach for mental fatigue detection based on psychophysiological measurements in flying‐relevant environments. Eleven participants engaged in a simulated flight experiment, where several conventional heart rate variability (HRV) and ocular indices were examined to study their relevance to mental fatigue. Additionally, a Toeplitz Inverse Covariance‐Based Clustering (TICC) method was performed to determine the ground truth, after which supervised machine learning was adopted to enable automated mental fatigue detection using HRV and eye metrics. Results showed that HRV and eye metrics were sensitive to the mental fatigue induced by prolonged flight‐relevant tasks. The TICC method helped determine the ground truth for mental fatigue and identify its three distinct levels. Furthermore, a supervised learning‐based detection of mental fatigue was achieved using a support vector machine with the greatest detection accuracy of 91.8%. The findings and methodology of this study provide new insights into the fatigue countermeasures in restricted cockpit environment and lay the groundwork for further explorations into the mental fatigue induced by prolonged flight missions.

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Workload Is Multidimensional, Not Unitary: What Now?

Cited by 21 publications

References 25 publications

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

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

What do subjective workload scales really measure? Operational and representational solutions to divergence of workload measures

Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight

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