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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
“Stress: appraisal and coping,”
Folkman, S. (2013) · Encyclopedia of Behavioral Medicine · doi:10.1007/978-1-4419-1005-9_215 · Cited by 27,833
Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research
(1988) · Advances in Psychology · doi:10.1016/s0166-4115(08)62386-9 · Cited by 15,003
Motivation through the design of work: test of a theory
Hackman, J. R., & Oldham, G. R. (1976) · Organizational Behavior and Human Performance · doi:10.1016/0030-5073(76)90016-7 · Cited by 8,962
Sweller, J., Ayres, P., & Kalyuga, S. (2011) · Elsevier eBooks · doi:10.1007/978-1-4419-1428-6_446 · Cited by 6,172
Csíkszentmihályi, M. (1975) · Cited by 5,690
Sweller, J. (2011) · Psychology of Learning and Motivation · doi:10.1016/b978-0-12-387691-1.00002-8 · Cited by 4,856
NASA-Task Load Index (NASA-TLX); 20 Years Later
Hart, S. G. (2006) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/154193120605000909 · Cited by 4,297 · cited 3×
“As indicated in Table 3 in Appendix , the NASA-TLX is the most used measure to assess mental workload, followed by the SWAT, the Rating Scale Mental Effort and the Workload Profile.”
BackgroundMultiple resources and performance prediction
Wickens, C. D. (2002) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220210123806 · Cited by 2,621 · cited 7×
“This theory explains the behavior of an operator as he or she concurrently performs multiple tasks that rely on the expenditure of multiple resources shared among these tasks (Wickens, 2002).”
BackgroundMethodInfluentialSelf-citationCognitive Load Measurement as a Means to Advance Cognitive Load Theory
Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003) · Educational Psychologist · doi:10.1207/s15326985ep3801_8 · Cited by 2,527
Hollan, J., Hutchins, E., & Kirsh, D. (2000) · ACM Transactions on Computer-Human Interaction · doi:10.1145/353485.353487 · Cited by 2,300 · cited 1×
“Instead, they are distributed across social groups, the environment, and the time of interaction (Hollan et al., 2000).”
Background
Flagged first, then by citations
Research works printed but not linked: 12
- Kahneman, 1973, Attention and Effort, Volume 1063
- Physiological metrics of mental workload: a review of recent progress KramerA. F. Multiple-Task Performance1991
- Mental workload of the vts operators by utilising heart rate KumS. FurushoM. DuruO. SatirT. TransNav, International Journal on Marine Navigation and Safety od Sea Transportation2007
- LazarusR. S. FolkmanS. 28833294Stress, Appraisal, and Coping. Springer Publishing Company1984
- Miller, 2001, Workload Measures
- Mizobuchi, 2005, Mobile text entry: relationship between walking speed and text input task difficulty, 122
- Moray, 2013, Mental Workload: Its Theory and Measurement, Vol. 8
- Smiley, 1989, Mental workload and information management, 435
- Stanton, 1995, Ecological ergonomics: understanding human action in context, Contemporary Ergon, 165, 62
- Woods, 2009, Rasmussen's srk 30 years later: Is human factors best in 3's?, 217
- Zajonc, 1984, The interaction of affect and cognition
- ZijlstraF. Efficiency in work behaviour: a design approach for modern tools (doctoral dissertation). Retrieved from Repository TU Delft (90-6275-918-1)1993
Cited by
Sorted by citations
Items 101–200 of 222
Touchscreens in Motion: Quantifying the Impact of Cognitive Load on Distracted Drivers
Shen, X., Hwang, S., Kong, J., Filipowicz, A. L. S., Best, A., Costa, J. M. D. R., Carter, S., Fogarty, J. A., & Wobbrock, J. O. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747683 · Cited by 4
Le Technostress et sa relation avec la charge mentale en contexte professionnel
Castillo, J. M., Galy, É., & Thérouanne, P. (2023) · Psychologie du Travail et des Organisations · doi:10.1016/j.pto.2023.06.001 · Cited by 3
Rea, D. J., & Young, J. E. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2023.1271337 · Cited by 3
Ciminaghi, F., Angioletti, L., Rovelli, K., & Balconi, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71707-9_27 · Cited by 3
Real-Time Cognitive Load Measurement System Of Dual-Tasking for First Responders
Larraga-García, B., Bejerano, V. R., Álvarez, F., & Gutiérrez, Á. (2024) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb64556.2024.10805634 · Cited by 3
Exploring the Relationship Between Team Workload and Communication in Action Teams
Johnson, C. J., Gutzwiller, R. S., Holder, E. W., Amazeen, P. G., & Cooke, N. J. (2025) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434251328835 · Cited by 3
Chen, L. G. R., & Law, B. H. Y. (2023) · Frontiers in Pediatrics · doi:10.3389/fped.2023.1116893 · Cited by 3
EEG Prediction with Deep Recurrent Learning
Zou, J., & Zhang, Q. (2024) · International Conference on Consumer Electronics · doi:10.1109/icce59016.2024.10444183 · Cited by 3
Keikhosrokiani, P., Isomursu, M., Korhonen, O., & Sean, T. T. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-59080-1_24 · Cited by 3
Mateja, A., Subocz, D., Stępień–Słodkowska, M., & Nermend, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-78465-1_8 · Cited by 4
Wenger, M. J., Townsend, J. T., & Newbolds, S. F. (2025) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-025-01311-2 · Cited by 2
Assessment and Prediction of Remote Operators’ Mental Workload Through AoI Data in AV Scenarios
Valerio, A., Nguyen, H. P., Ihme, K. A., & Walocha, F. (2025) · Mensch und Computer (MuC) · doi:10.1145/3743049.3748583 · Cited by 2
Eikendal, T., Prudon, N., Kool, R. B., Jeurissen, P., Gaakeer, M. I., de Groot, B., & van Dulmen, S. A. (2025) · BMC Emergency Medicine · doi:10.1186/s12873-025-01334-5 · Cited by 2
Multimodal measurement of the mental workload during an assembly and disassembly task
Berthon, L., Fleury, S., Bernard, F., Paquin, R., & Richir, S. (2025) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2025.2568138 · Cited by 2
Ayas, S., He, D., & Donmez, B. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3633499 · Cited by 2
Zhang, P., Cheng, T., Jiang, Y., Liu, W., Chen, X., Zou, X., Sheng, Y., & Zhao, X. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3640416 · Cited by 2
Workload Does Not Work: On the Many Problems of the NASA-TLX
Taylor, S., & Bolton, M. L. (2026) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2026.3664262 · Cited by 2
Dynamic velocity scaling for industrial collaborative robots: a gaze-driven approach
Manzardo, M., Fraboni, F., Morandini, S., Gualtieri, L., Vidoni, R., & Pietrantoni, L. (2026) · Scientific Reports · doi:10.1038/s41598-025-34142-9 · Cited by 3
Alsanousi, M. M., & Prabhu, V. V. (2026) · Sensors · doi:10.3390/s26154848 · Cited by 2
Signals of Strain: Using Biosignals to Assess Fatigue in Video Conferencing
Pagel, L. V., Kojić, T., Schmitt, V., Wardah, W., Harnisch, P. L., Möller, S., & Spang, R. P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93718-7_8 · Cited by 2
An Adaptive system for single pilot operations triggered by human–machine system performance model
Li, M., Luo, Y., Wang, M., & Wang, G. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113647 · Cited by 2
Hsieh, C., Khau, A. H., & Li, H. (2025) · SAGE Open · doi:10.1177/21582440251358982 · Cited by 4
Cognitive Fatigue Assessment for Human-Robot Collaboration: Review & Open Challenges
Zand, M., Wood, S. L., Kakhki, F. D., & Kyrarini, M. (2025) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3733155.3736596 · Cited by 2
The Relationship of ECTS Credits with Study Time, Workload, and Achievement in Higher Education
Impola, J. (2025) · European Education · doi:10.1080/10564934.2025.2547918 · Cited by 2
Su, H., Jiaxin, J., Siping, F., Jian, W., Ruiying, X., Lifei, X., Wang, X., Xin, Q., & Yuqi, S. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2590088 · Cited by 2
Cosic, K., Kuljanac, A., & Palermo, S. (2026) · Frontiers in psychology · doi:10.3389/fpsyg.2026.1821242 · Cited by 3
Sjarifudin, D., Widyastuti, T., Istianingsih, I., & Suroso, S. (2023) · Formosa Journal of Sustainable Research · doi:10.55927/fjsr.v2i12.6963 · Cited by 1
A Human Brain Disease Anatomy Framework on Communication and Rehabilitation
Sharma, A., Avasthi, S., Prakash, A., & Agarwal, K. (2023) · International Conference on Informatics (ICI) · doi:10.1109/ici60088.2023.10420886 · Cited by 1
Mapping and Characterizing Instruments for Assessing Family Nurses’ Workload: Scoping Review
Dias, A., Araújo, B., & de Jesus, E. H. (2024) · Nursing Reports · doi:10.3390/nursrep14030151 · Cited by 1
Catalini, A., Stacchini, L., Minutolo, G., Ancona, A., Caminiti, M., Cosma, C., Gallinoro, V., Nicolò, V. D., Cedrone, F., Barbadoro, P., et al. (2024) · Healthcare · doi:10.3390/healthcare12222299 · Cited by 2
Cognitive Workload Assessment of UAV Operators Using Synergy of Machine Learning and Reasoning
Shaposhnyk, O. A., Zahorska, D., Shmerko, V., Yanushkevich, S. N., Babenko, V. O., Настенко, Є. А., & Lyshevski, S. E. (2024) · IEEE Western New York Image and Signal Processing Workshop (WNYISPW) · doi:10.1109/wnyispw63690.2024.10786502 · Cited by 1
Sutarto, A. P., Izzah, N., & Kusumawardani, R. I. (2024) · Journal of Health Sciences and Medical Development · doi:10.56741/hesmed.v3i03.686 · Cited by 1
Assessment of mental workload states in monitors using functional near-infrared spectroscopy
Wang, Y., Yu, R., & Shi, T. (2025) · Work · doi:10.1177/10519815241306414 · Cited by 1
Exploration of biomechanics, mental workload and metabolism factors during prosthetic gait
Berettoni, A., Giuseppe, S. D., Mariani, G., Boccardo, N., Laffranchi, M., & Semprini, M. (2025) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/ae0f11 · Cited by 1
Jahanjoo, A., Wei, Y., Haghi, M., Schörpf, P., & TaheriNejad, N. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11251604 · Cited by 1
Attahir, M., Chutima, P., & Miralles, C. (2025) · Journal of Industrial and Production Engineering · doi:10.1080/21681015.2025.2594008 · Cited by 1
Zhang, S., Wang, L., & Xing, Z. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2611345 · Cited by 2
Tao, D., Jiang, M., Yang, H., Liu, Z., & Wang, T. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2611143 · Cited by 1
Shahin, M., Maghanaki, M., & Chen, F. F. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10070215 · Cited by 1
Jaiaue, T., Cooke, A., & Gallicchio, G. (2026) · Ergonomics · doi:10.1080/00140139.2026.2702629 · Cited by 1
Lachowicz, M., Wilk, I., Hrehorowicz, I., Bałamucki, S., Lisowski, M., Jamro, D., Żurek, G., & Andrzejewski, W. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2026.1917630 · Cited by 1
Rivas-Vidal, M., Córdoba, A. C., Farfán, F. D., & García-Cena, C. E. (2026) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2026.1909914 · Cited by 1
(2023) · Medicine & health · doi:10.17576/mh.2023.1802.14 · Cited by 2
Ciminaghi, F. (2024) · Neuropsychological Trends · doi:10.7358/neur-2024-035-cimi · Cited by 1
Nourollahi‐Darabad, M., Afshari, D., Karimi, N., & Seyedtabib, M. (2024) · Jundishapur Journal of Chronic Disease Care · doi:10.5812/jjcdc-145047 · Cited by 1
Research on the evaluation model of pilot workload under day night alternation conditions
Zhao, D., Shenyang, Z. L., Hu, L., Xin, C., & Zhou, Y. (2024) · International Conference on Information Systems and Computing Technology (ISCTech) · doi:10.1109/isctech63666.2024.10845595 · Cited by 1
ECTS credits and quality: evidence for public policy reform
Velázquez, L., Atenas, B., Hernández, N. C., & Castro-Palacio, J. C. (2025) · Studies in Higher Education · doi:10.1080/03075079.2025.2571640 · Cited by 1
Evaluation of Workload for Operators in the Aeronautical Sector
Borges, S. F. D. S., de Morais, P., Bovo, M. G., Castilho, D. S., & Cardoso, M. M. (2023) · European Safety and Reliability Conference (ESREL) · doi:10.3850/978-981-18-8071-1_p312-cd
Mahanta, P., & Bhattacharya, M. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-50192-0_24
Razakatiana, M., Kolski, C., Mandiau, R., & Mahatody, T. (2024) · Human-Centric Intelligent Systems · doi:10.1007/s44230-024-00063-5
Hubungan Antara Beban Belajar dengan Tingkat Stres Mahasiswa Fakultas Kedokteran UMI Angkatan 2020
Abdullah, A. A. P., Royani, I., Hamzah, P. N., Amir, S. P., & Jaya, . A. (2024) · FAKUMI MEDICAL JOURNAL Jurnal Mahasiswa Kedokteran · doi:10.33096/fmj.v4i2.393
Kurnia, F. (2024) · Industrika Jurnal Ilmiah Teknik Industri · doi:10.37090/indstrk.v8i2.1434
Logical Interference: Using AI to Correct Flaws in Human Judgment
Cassenti, D. N., & Hawkins, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60615-1_22
de Moura, R. A., Marílio, J. W. O., Marques, D. J. R., de Oliveira, M. R., & Silva, M. B. (2024) · Revista SODEBRAS · doi:10.29367/5bge8b53
Comprehensible AI for Multimodal State Detection
Foltyn, A., & Oppelt, M. P. (2024) · Springer eBooks · doi:10.1007/978-3-031-64832-8_11
Xiang, L.-S., Zhang, J.-N., Xie, F., Fei, X., Wang, Y., Shi, Y., & Zhang, Y. (2024) · BMC Neuroscience · doi:10.1186/s12868-024-00886-9
Iani, C., Bori, L., & Rubichi, S. (2024) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813241277537
Defining and Measuring Crewmember Operational State for Spaceflight Operations
Zero, M., Klaus, D. M., Arquilla, K. S., & Fanchiang, C. (2024) · Aerospace Medicine and Human Performance · doi:10.3357/amhp.6405.2024
Workload in cockpit work on high-speed workboats: How to improve crew performancePreprint
Lehtimäki, M., & Teperi, A.-M. (2024) · Research Square · doi:10.21203/rs.3.rs-5438272/v1
MonChitro: Unveiling The Stress and Depression About Future Among Undergraduate Students
Hossain, S. Z., Miami, A. T., & Noor, J. (2024) · International Conference on Networking, Systems and Security (NSysS) · doi:10.1145/3704522.3704531
Gamage, S. M., & Keikhosrokiani, P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5083116
Affecting factors to mental workload of workers (case study at process station of PT XXX)
Lucitasari, D. R., & Widiatmoko, D. P. (2025) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1454/1/012068
Guo, M., Jin, X., Zhang, J., Huang, Q., & Wei, Y. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2464908
Pupillometric Analysis of Cognitive Load in Relation to Relevance and Confirmation Bias
Shi, L., Jayawardena, G., & Jacek Gwizdka, A. (2025) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3698204.3716458
Rahman, N. I. A., & Ismail, M. (2025) · Interdisciplinary Description of Complex Systems · doi:10.7906/indecs.23.2.5
Communication and Rehabilitation Interface for Human Brain Disease
Sharma, A., Avasthi, S., Agarwal, K., Jain, K., & Abraham, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-78937-3_1
Enhancing Adaptive Human-Agent Teaming Systems with Functional Near-Infrared Spectroscopy
Eloy, L., Doherty, E. J., Spencer, C. A., & Hirshfield, L. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2524490 · Cited by 1
Kucukosmanoglu, M., Brooks, J., Neubauer, C. E., & Krausman, A. (2025) · Research Square · doi:10.21203/rs.3.rs-7285350/v1
Takahashi, S., Kosugi, W., Kagesawa, J., Mizuno, S., & Matsui, T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.21.677562
From Neurocognitive Functions and Individual Differences to AI-Driven Human-Robot Collaboration
Ciminaghi, F., Mastrogiacomo, L., Gervasi, R., Allegretta, R. A., Acconito, C., & Balconi, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97781-7_2
Work-Life Balance and Workload on Employee Work Stress
Khotimah, A. H., & Nardo, R. (2025) · CAKRAWALA Management Science Journal · doi:10.63541/e7f9rc75
Moore, J., Morris, P., McCloskey, R. M., Furlong, K. E., & McNulty, S. K. (2025) · Canadian Journal of Nursing Research · doi:10.1177/08445621251385021
Isoghie, E. J., & Saleem, J. J. (2025) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813251366286
Kontopodis, E., Serafeim, C., Cavouras, D. A., Kalatzis, I. K., Ventouras, E.-C., Kakkos, I., & Skouroliakou, A. (2025) · Advances in experimental medicine and biology · doi:10.1007/978-3-032-03394-9_52
Scientometric Analysis of Research Work on Mental Workload
Huerta, C. N. T., Araya-Castillo, L., & Boada‐Grau, J. (2025) · Administrative Sciences · doi:10.3390/admsci15110450
Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
Lyu, X., Wu, J., Ma, Z., Su, Y., Zhao, S., Liu, T., Liu, X., & Qin, J. (2025) · Journal of Visualized Experiments · doi:10.3791/69621
Estimating Workload for Supervisory Human–Robot Teams: An Initial Analysis of Meta-Learning
Smith, J. B., & Adams, J. A. (2025) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2025.3640359
Nursing Management of Cognitive Overload in High-Acuity Clinical Environments
Alenezi, A. M., Alenezi, G. A. T., Alanazi, I. T. N., Alanazi, F. S., Alruwaili, N. Q. F., Alanazi, R. S. K., Alruwaili, M. F. A., Almutairi, F. A. M., Alobaid, A. M. S., Almohamdi, W. A. S., et al. (2024) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.4736
Yunsheng, X., Shenzhen, P., Feiyu, L., Mengtao, L., Xiaofei, J., & Xinyu, Y. (2026) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1177/09544062251413044
Wan, C. L., Leow, W. F., & Khor, W. M. (2025) · Journal of Information System and Technology Management · doi:10.35631/jistm.1041022
A Comparison of Experimental Methods to Induce Mental Fatigue
Bach, M. M., Brandstetter, K., Stins, J. F., & Beek, P. J. (2026) · Perceptual and Motor Skills · doi:10.1177/00315125261420694
Exoskeletons and Prosthetic Limbs
Tessari, F., & Barresi, G. (2025) · Springer eBooks · doi:10.1007/978-3-031-40858-8_50-1
Identificação de Esforço Cognitivo com Auxílio de Dispositivos Vestíveis e Inteligência Artificial
Franceschina, M. A., Zeiser, F. A., da Costa, C. A., Roehe, A. V., & Zeiser, M. H. (2025) · Escola Regional de Engenharia de Software (ERES) · doi:10.5753/eres.2025.15429
Eroğlu, Ş. G., DEMİRCİ, U., & TOROS, F. (2026) · Süleyman Demirel Üniversitesi Vizyoner Dergisi · doi:10.21076/vizyoner.1665098
Arad, L. (2026) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3776734.3794546
Yang, F., Sun, X., Ma, J., Zhou, S., Liu, B., Li, P., Lyu, W., & Wen, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-42814-3
Berthon, L., Bernard, F., Fleury, S., Paquin, R., & Richir, S. (2026) · Human Factors and Ergonomics in Manufacturing & Service Industries · doi:10.1002/hfm.70041
The Heart of Resilience: Trauma-Informed Leadership in a Burnout Era
Jacobs, C. M. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1014797 · Cited by 1
A Review of Neuroergonomics Approaches for Evaluating Mental Workload
Teymourian, K., Kumar, U., Adoul, M. A., & Karim, R. (2026) · Lecture notes in mechanical engineering · doi:10.1007/978-3-032-03725-1_10
Rey-Becerra, E., Tegtmeier, P., Hartwig, M., & Wischniewski, S. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1698078
From Screen to Presence: How Robot Embodiment Shapes Cognitive and Physiological Responses
Rihet, M., Clodic, A., Sarthou, G., & Roy, R. N. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3799297
Mental Workload Prediction Using Physiological Signals: Balancing Performance and Interpretability
Hochgeschurz, S., Schwarz, J., & Witte, T. E. F. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790359
Designing Novel Visual Physiologic Monitor Displays for Patient Care
Cummins, M. L., Sicard, C., Yang, S., Schlesinger, J. J., & Shive, J. (2026) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208261445900
Mustile, M., Kompatsiari, K., Tommaso, D. D., Russi, N. S., Dafarra, S., Novembre, G., Pucci, D., & Wykowska, A. (2026) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/4528417
Matulewski, J., Meszyński, S., Sokołov, O., & Barańska, A. (2026) · ACM Symposium on Eye Tracking Research and Applications (ETRA) · doi:10.1145/3797246.3804832
Tetei, M., Patel, T., & BASU, S. (2026) · Journal of Agricultural Engineering (India) · doi:10.52151/jae2026632.2014
Dias, R. D., Michael, S. H., Harari, R. E., Yule, S., McGinnis, L. J., Skipworth, R., Mann-Salinas, E. A., & Davis, W. T. (2026) · Sensors · doi:10.3390/s26113596
Montañés, P., Argüello‐Gutiérrez, C., Burgos, I. V. M., & Hernández, F. L. C. (2026) · Health & Social Work · doi:10.1093/hsw/hlag019
Gamage, S., Keikhosrokiani, P., & Isomursu, M. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-28819-6_25
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review