The DOI matches its Crossref record; 228 of the 229 references it cites were found in a registry or an index, and 1 are not checked yet.
Human Mental Workload: A Survey and a Novel Inclusive Definition
Open accessAbstract
Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.
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“Assessing mental workload as a multidimensional context-dependent construct is generally done by a combination of self-report, performance, and physiological indicators rather than by any single measure alone (Young et al., 2015; Longo et al., 2022; Hart and Staveland, 1988).”
From Enhancement of modern defense capabilities by convergence of cognitive neuroscience and artificial intelligence (2026) · cites this paper as Longo et al., 2022
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Metadata
- Authors
- Longo, L., Wickens, C. D., Hancock, G., & Hancock, P. A.
- Affiliations
- Technological University Dublin (IE)
- Colorado State University (US)
- University of Central Florida (US)
- Corresponding author
- Longo, Luca
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2022-06-02
- Volume
- 13
- Pages
- 883321-883321
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC9201728
- OpenAlex
- W4281746451
- Cited by
- 222
- References
- 222
- Citations by year
- 2026: 53 · 2025: 72 · 2024: 67 · 2023: 28 · 2022: 2
- FWCI
- 6.33
- Type
- journal article
- Language
- en
- Update notices
- corrigendum (2022-07-26)
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Arrow - TU Dublin (Technological University Dublin) (submittedVersion, cc-by-nc-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Lenus, The Irish Health Repository (Dr Steevens Hospital Library) (submittedVersion)
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Human Factors and Ergonomics › Ergonomics and Human Factors
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- mental workload, operational definitions, mental workload assessment, self-report measures, physiological indices, neuroergonomics, human performance prediction
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