Mental Workload: Definition and Measurement Review

The assessment of MWL is pivotal for understanding human performance limitations, optimizing task design, and enhancing overall system efficiency and safety across various domains, including aviation, healthcare, and technology interfaces. However, reaching an agreement on its definition, whether in technical or philosophical terms, is highly challenging. This study aims to critically examine the theories of MWL and offer a conceptual and operational definition for future researchers in the field. This paper also provides a review of MWL measurement by exploring the progress made in measuring MWL, including the development of novel techniques. We searched scientific databases covering the topic of limited and multiple resource theories, along with the measurement of MWL, covering the topics of performance, psychophysiological, and subjective techniques. A narrative review was applied to appraise the literature, particularly using the TIR approach. Based on our review, the definition of MWL consists of four elements: cognitive processing, task demand, performance and physiological changes, and subjective experience. Furthermore, our review provides a framework for measuring mental workload that encompasses the interplay between performance and psychological changes, demands, and subjective measures. Several theoretical and practical issues regarding the measurement approach are also discussed.

Definition; Limited resource theory; Measurement; Mental workload; Multiple resource theory

Aghajani, H, Garbey, M & Omurtag, A 2017, 'Measuring mental workload with EEG+fNIRS', Frontiers in Human Neuroscience, vol. 11, pp. 1–13, https://doi.org/10.3389/fnhum.2017.00359

Ahn, JW, Ku, Y & Kim, HC 2019, 'A novel wearable EEG and ECG recording system for stress assessment', Sensors, vol. 19, no. 9, pp. 1–13, https://doi.org/10.3390/s19091991

Ahn, S, Nguyen, T, Jang, H, Kim, JG & Jun, SC 2016, 'Exploring neuro-physiological correlates of drivers’ mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and fNIRS data', Frontiers in Human Neuroscience, vol. 10, pp. 1–10, https://doi.org/10.3389/fnhum.2016.00219

Appel, T, Scharinger, C, Gerjets, P & Kasneci, E 2018, 'Cross-subject workload classification using pupil-related measures', In: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications, pp. 1–8, https://doi.org/10.1145/3204493.3204531

Argyle, EM, Marinescu, A, Wilson, ML, Lawson, G & Sharples, S 2021, 'Physiological indicators of task demand, fatigue, and cognition in future digital manufacturing environments', International Journal of Human-Computer Studies, vol. 145, pp. 1–11, https://doi.org/10.1016/j.ijhcs.2020.102522

Astin, A & Nussbaum, MA 2002, 'Interactive effects of physical and mental workload on subjective workload assessment', Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 46, no. 13, pp. 1100–1104, https://doi.org/10.1177/154193120204601320

Atkin, C, Stacey, JE, Roberts, KL, Allen, HA, Henshaw, H & Badham, SP 2023, 'The effect of unisensory and multisensory information on lexical decision and free recall in young and older adults', Scientific Reports, vol. 13, no. 1, pp. 1–11, https://doi.org/10.1038/s41598-023-41791-1

Ayaz, H, Shewokis, PA, Bunce, S, Izzetoglu, K, Willems, B & Onaral, B 2012, 'Optical brain monitoring for operator training and mental workload assessment', NeuroImage, vol. 59, no. 1, pp. 36–47, https://doi.org/10.1016/j.neuroimage.2011.06.023

Babaei, E, Dingler, T, Tag, B, Velloso, E, 2025, ‘Should we use the NASA-TLX in HCI? A review of theoretical and methodological issues around Mental Workload Measurement’, International Journal of Human-Computer Studies, vol. 201, article 103515, https://doi.org/10.1016/j.ijhcs.2025.103515    

Battistone, MJ, Kemeyou, L & Varpio, L 2023, 'The theoretical integrative review—A researcher’s guide', Journal of Graduate Medical Education, vol. 15, no. 4, pp. 453–455, https://doi.org/10.4300/JGME-D-23-00266.1

Ben Mrad, I, Ben Mrad, M, Besbes, B, Zairi, I, Ben Kahla, N, Kamoun, S, Mzoughi, K & Kraiem, S 2021, 'Heart rate variability as an indicator of autonomic nervous system disturbance in Behcet’s disease', International Journal of General Medicine, vol. 14, pp. 4877–4886, https://doi.org/10.2147/IJGM.S326549

Boele-Vos, MJ, Commandeur, JJF & Twisk, DAM 2017, 'Effect of physical effort on mental workload of cyclists in real traffic in relation to age and use of pedelecs', Accident Analysis & Prevention, vol. 105, pp. 84–94, https://doi.org/10.1016/j.aap.2016.11.025

Bruya, B & Tang, Y-Y 2018, ‘Is attention really effort? Revisiting Daniel Kahneman’s influential 1973 book attention and effort’, Frontiers in Psychology, vol. 9, article 1133, https://doi.org/10.3389/fpsyg.2018.01133 

Bui, L & Deakin, J 2021, 'What we talk about when we talk about vulnerability and youth crime: A narrative review', Aggression and Violent Behavior, vol. 58, article 101605, https://doi.org/10.1016/j.avb.2021.101605

Byrne, AJ, Murphy, A, McIntyre, O & Tweed, N 2013, 'The relationship between experience and mental workload in anaesthetic practice: an observational study', Anaesthesia, vol. 68, no. 12, pp. 1266-1272, https://doi.org/10.1111/anae.12455

Cabañero, L, Hervás, R, González, I, Fontecha, J, Mondéjar, T & Bravo, J 2019, 'Analysis of cognitive load using EEG when interacting with mobile devices', In: Proceedings of the 13th International Conference on Ubiquitous Computing and Ambient Intelligence UCAmI, vol. 31, no. 1, article 70, https://doi.org/10.3390/proceedings2019031070

Cain, B 2007, A review of the mental workload literature, Defense Research and Development Canada Toronto, Toronto

Causse, M, Chua, Z, Peysakhovich, V, Campo, ND & Matton, N 2017, 'Mental workload and neural efficiency quantified in the prefrontal cortex using fNIRS', Scientific Reports, vol. 7, article 5222, https://doi.org/10.1038/s41598-017-05378-x

Causse, M, Lepron, E, Mandrick, K, Peysakhovich, V, Berry, I, Callan, D & Rémy, F 2022, 'Facing successfully high mental workload and stressors: An fMRI study', Human Brain Mapping, vol. 43, no. 3, pp. 1011-1031, https://doi.org/10.1002/hbm.25703

Charles, RL & Nixon, J 2019, 'Measuring mental workload using physiological measures: A systematic review', Applied Ergonomics, vol. 74, pp. 221-232, https://doi.org/10.1016/j.apergo.2018.08.028

Chen, O, Castro-Alonso, J C, Paas, F & Sweller, J 2018, 'Extending cognitive load theory to incorporate working memory resource depletion: Evidence from the spacing effect', Educational Psychology Review, vol. 30, no. 2, pp. 483–501, https://doi.org/10.1007/s10648-017-9426-2

Chowdhury, A, Shankaran, R, Kavakli, M & Haque, MdM 2018, 'Sensor applications and physiological features in drivers’ drowsiness detection', IEEE Sensors Journal, vol. 18, no. 8, pp. 3055–3067, https://doi.org/10.1109/JSEN.2018.2807245

Cowan, N 2010, 'The magical mystery four: How is working memory capacity limited, and why?', Current Directions in Psychological Science, vol. 19, no. 1, pp. 51–57, https://doi.org/10.1177/0963721409359277

Dahlstrom, N, Nahlinder, S, Wilson, GF & Svensson, E 2011, 'Recording of psychophysiological data during aerobatic training', The International Journal of Aviation Psychology, vol. 21, no. 2, pp. 105–122, https://doi.org/10.1080/10508414.2011.556443

Demiris, G, Oliver, DP & Washington, KT 2019, 'Defining and analyzing the problem', Behavioral Intervention Research in Hospice and Palliative Care, pp. 27–39, https://doi.org/10.1016/B978-0-12-814449-7.00003-X

Devlin, SP, Moacdieh, NM, Wickens, CD & Riggs, SL 2020, 'Transitions between low and high levels of mental workload can improve multitasking performance', IISE Transactions on Occupational Ergonomics and Human Factors, vol. 8, no. 2, pp. 72–87, https://doi.org/10.1080/24725838.2020.1770898

Di Nocera, F, Camilli, M & Terenzi, M 2007, 'A random glance at the flight deck: Pilots’ scanning strategies and the real-time assessment of mental workload', Journal of Cognitive Engineering and Decision Making, vol. 1, no. 3, pp. 271–285, https://doi.org/10.1518/155534307X255627

Eckstein, MK, Guerra-Carrillo, B, Singley, ATM & Bunge, SA 2017, ‘Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development?’, Developmental Cognitive Neuroscience, vol. 25, pp. 69-91, https://doi.org/10.1016/j.dcn.2016.11.001 

Ellinas, C, Allan, N & Johansson, A 2017, ‘Dynamics of organizational culture: Individual beliefs vs. social conformity’, PLOS ONE, vol. 12, no. 6, article e0180193,  https://doi.org/10.1371/journal.pone.0180193 

Fairclough, SH, Venables, L & Tattersall, A 2005, ‘The influence of task demand and learning on the psychophysiological response’, International Journal of Psychophysiology, vol. 56, pp. 171-184, https://doi.org/10.1016/j.ijpsycho.2004.11.003 

Fallahi, M, Motamedzade, M, Heidarimoghadam, R, Soltanian, AR & Miyake, S 2016b, ‘Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study’, Applied Ergonomics, vol. 52, pp. 95-103. https://doi.org/10.1016/j.apergo.2015.07.009 

Fallahi, M, Motamedzade, M, Heidarimoghadam, R, Soltanian, AR, Farhadian, M & Miyake, S 2016a, ‘Analysis of the mental workload of city traffic control operators while monitoring traffic density: A field study’, International Journal of Industrial Ergonomics, vol. 54, pp. 170-177, https://doi.org/10.1016/j.ergon.2016.06.005 

Fan, J & Smith, AP 2020, ‘Effects of occupational fatigue on cognitive performance of staff from a train operating company: a field study’, Frontiers in Psychology, vol. 11, article 558520, https://doi.org/10.3389/fpsyg.2020.558520 

Fista, B, Azis, HA, Aprilya, T, Saidatul, S, Sinaga, MK, Pratama, J, Syalfinaf, FA, Steven & Amalia, S 2019, ‘Review of cognitive ergonomic measurement tools’, IOP Conference Series: Materials Science and Engineering, vol. 598, article 012131, https://doi.org/10.1088/1757-899X/598/1/012131 

Foy, HJ, Runham, P & Chapman, P 2016, ‘Prefrontal cortex activation and young driver behaviour: a fNIRS study’, PLoS ONE, vol. 11, article e0156512, https://doi.org/10.1371/journal.pone.0156512 

Galoyan, T, Betts, K, Abramian, H, Reddy, P, Izzetoglu, K & Shewokis, PA 2021, ‘Examining mental workload in a spatial navigation transfer game via functional near infrared spectroscopy’, Brain Sciences, vol. 11, no. 1, article 45, https://doi.org/10.3390/brainsci11010045 

Galy, E, Paxion, J & Berthelon, C 2018, ‘Measuring mental workload with the NASA-TLX needs to examine each dimension rather than relying on the global score: an example with driving’, Ergonomics, vol. 61, pp. 517–527, https://doi.org/10.1080/00140139.2017.1369583 

Gathmann, B, Schiebener, J, Wolf, OT & Brand, M 2015, ‘Monitoring supports performance in a dual-task paradigm involving a risky decision-making task and a working memory task’, Frontiers in Psychology, vol. 6, article 142, https://doi.org/10.3389/fpsyg.2015.00142 

Gilchrist, AL, Cowan, N & Naveh-Benjamin, M 2008, ‘Working memory capacity for spoken sentences decreases with adult aging: recall of fewer, but not smaller chunks in older adults’, Memory, vol. 16, pp. 773-787, https://doi.org/10.1080/09658210802261124 

Grant, RC, Carswell, CM, Lio, CH & Seales, WB 2013, ‘Measuring Surgeons’ mental workload with a time-based secondary task’, Ergonomics in Design, vol. 21, no. 1, pp. 7–11, https://doi.org/10.1177/1064804612466068 

Grinschgl, S, Papenmeier, F & Meyerhoff, HS 2023, ‘Mutual interplay between cognitive offloading and secondary task performance’, Psychonomic Bulletin & Review, vol. 30, pp. 2250–2261, https://doi.org/10.3758/s13423-023-02312-3 

Gullett, N, Zajkowska, Z, Walsh, A, Harper, R & Mondelli, V 2023, ‘Heart rate variability (HRV) as a way to understand associations between the autonomic nervous system (ANS) and affective states: A critical review of the literature’, International Journal of Psychophysiology, vol. 192, pp. 35-42, https://doi.org/10.1016/j.ijpsycho.2023.08.001 

Halford, GS, Cowan, N & Andrews, G 2007, ‘Separating cognitive capacity from knowledge: a new hypothesis’, Trends in Cognitive Sciences, vol. 11, pp. 236–242. https://doi.org/10.1016/j.tics.2007.04.001 

Hart, SG 2006, ‘Nasa-Task load index (NASA-TLX); 20 Years Later‘, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 50, pp. 904-908, https://doi.org/10.1177/154193120605000909 

He, D, Wang, Z, Khalil, EB, Donmez, B, Qiao, G & Kumar, S 2022, ‘Classification of driver cognitive load: exploring the benefits of fusing eye-tracking and physiological measures’, Transportation Research Record: Journal of the Transportation Research Board, vol. 2676, no. 10, pp. 670–681, https://doi.org/10.1177/03611981221090937 

Hollnagel, E 2021, ‘The changing nature of task analysis’, In: Salvendy, G., Karwowski, W. (Eds.), Handbook of Human Factors and Ergonomics. John Wiley & Sons, Ltd, Hoboken, NJ, pp. 358-367

Horrey, WJ & Wickens, CD 2006, ‘Examining the impact of cell phone conversations on driving using meta-analytic techniques’, Human Factors: The Journal of the Human Factors and Ergonomics Society, vol. 48, no. 1, pp. 196–205. https://doi.org/10.1518/001872006776412135 

Hsu, B-W, Wang, M-JJ, Chen, C-Y & Chen, F 2015, ‘Effective indices for monitoring mental workload while performing multiple tasks’, Perceptual and Motor Skills, vol. 121, pp. 94-117, https://doi.org/10.2466/22.PMS.121c12x5 

Huggins, A, Claudio, D 2018, ‘A performance comparison between the subjective workload analysis technique and the NASA-TLX in a healthcare setting’, IISE Transactions on Healthcare Systems Engineering, vol. 8, pp. 59–71. https://doi.org/10.1080/24725579.2017.1418765 

Jalali, M, Esmaeili, R, Habibi, E, Alizadeh, M & Karimi, A 2023, ‘Mental workload profile and its relationship with presenteeism, absenteeism and job performance among surgeons: The mediating role of occupational fatigue’, Heliyon, vol. 9, article e19258, https://doi.org/10.1016/j.heliyon.2023.e19258 

Kuchinsky, SE, Gallun, FJ & Lee, AKC 2024, ‘Note on the Dual-Task Paradigm and its Use to Measure Listening Effort’, Trends in Hearing, 28, article 23312165241292215, https://doi.org/10.1177/23312165241292215

Leggatt, A 2005, ‘Validation of the ISA (Instantaneous Self Assessment) Subjective Workload Tool’, in P.T. Bust and P.T. McCabe (eds) Contemporary Ergonomics 2005. Taylor & Francis

Lei, S, Toriizuka, T & Roetting, M 2017, ’Driver adaptive task allocation: A field driving study’, Le Travail Humain, vol. 80, pp. 93–112

Lenneman, JK & Backs, RW 2018, ‘A psychophysiological and driving performance evaluation of focal and ambient visual processing demands in simulated driving’, Transportation Research Part F: Traffic Psychology and Behaviour, vol. 57, pp. 84-96, https://doi.org/10.1016/j.trf.2017.11.001  

Li, X, Zhu, W, Sui, X, Zhang, A, Chi, L & Lv, L 2021, ‘Assessing workplace stress among nurses using heart rate variability analysis with wearable ecg device-a pilot study’, Front Public Health, vol. 9, article 810577, https://doi.org/10.3389/fpubh.2021.810577 

Lobjois, R, Faure, V, Désiré, L & Benguigui, N 2021, ‘Behavioral and workload measures in real and simulated driving: Do they tell us the same thing about the validity of driving simulation? Safety Science’, vol. 134, article 105046. https://doi.org/10.1016/j.ssci.2020.105046 

Longo, L, Wickens, CD, Hancock, G & Hancock, PA 2022, ‘Human mental workload: a survey and a novel inclusive definition’, Frontiers in Psychology, vol. 13, article 883321, https://doi.org/10.3389/fpsyg.2022.883321 

López-López, ML, Balanza-Galindo, S, Vera-Catalán, T, Gallego-Gómez, JI, González-Moro, MTR, Rivera-Caravaca, JM & Simonelli-Muñoz, AJ 2018, ‘Risk factors for mental workload: influence of the working environment, cardiovascular health and lifestyle. A cross-sectional study’, BMJ Open, vol. 8, article e022255, https://doi.org/10.1136/bmjopen-2018-022255 

Louis, L-EL, Moussaoui, S, Van Langhenhove, A, Ravoux, S, Le Jan, T, Roualdes, V & Milleville-Pennel, I 2023, ‘Cognitive tasks and combined statistical methods to evaluate, model, and predict mental workload’, Frontiers in Psychology, vol. 14, article 1122793. https://doi.org/10.3389/fpsyg.2023.1122793 

Maior, HA, Wilson, ML & Sharples, S 2018, ‘Workload alerts—using physiological measures of mental workload to provide feedback during tasks’, ACM Transactions on Computer-Human Interaction, vol. 25, no. 9, pp. 1-30, https://doi.org/10.1145/3173380 

Makishita, H & Matsunaga, K 2008, ‘Differences of drivers’ reaction times according to age and mental workload’, Accident Analysis & Prevention, vol. 40, no. 2, pp. 567-575, https://doi.org/10.1016/j.aap.2007.08.012 

Mansikka, H, Virtanen, K, Harris, D & Simola, P 2016, ‘Fighter pilots’ heart rate, heart rate variation and performance during an instrument flight rules proficiency test’, Applied Ergonomics, vol. 56, pp. 213-219, https://doi.org/10.1016/j.apergo.2016.04.006 

Marinescu, AC, Sharples, S, Ritchie, AC, Sánchez López, T, McDowell, M & Morvan, HP 2018. ‘Physiological parameter response to variation of mental workload’, Human Factors: The Journal of the Human Factors and Ergonomics Society, vol. 60, pp. 31–56. https://doi.org/10.1177/0018720817733101 

Midha, S, Maior, HA, Wilson, ML & Sharples, S 2021, ‘Measuring mental workload variations in office work tasks using fNIRS’, International Journal of Human-Computer Studies, vol. 147, article 102580, https://doi.org/10.1016/j.ijhcs.2020.102580 

Mohammadian, M, Parsaei, H, Mokarami, H & Kazemi, R 2022, ‘Cognitive demands and mental workload: A filed study of the mining control room operators’, Heliyon, vol. 8, no. 2, article e08860. https://doi.org/10.1016/j.heliyon.2022.e08860 

Moustafa, K & Longo, L 2019, ‘Analysing the impact of machine learning to model subjective mental workload: a case study in third-level education’, In: L. Longo and M.C. Leva (eds) Human Mental Workload: Models and Applications. Cham: Springer International Publishing, pp. 92–111, https://doi.org/10.1007/978-3-030-14273-5_6.

Moustafa, K, Luz, S & Longo, L 2017, ‘Assessment of mental workload: a comparison of machine learning methods and subjective assessment techniques’, In: L. Longo and M.C. Leva (eds) Human Mental Workload: Models and Applications. Cham: Springer International Publishing, pp. 30–50. Available at: https://doi.org/10.1007/978-3-319-61061-0_3 

Nealley, MA & Gawron, VJ 2015, ‘The effect of fatigue on air traffic controllers’, The International Journal of Aviation Psychology, vol. 25, pp. 14–47 https://doi.org/10.1080/10508414.2015.981488 

Nino, V, Monfort, SM & Claudio, D 2023, ‘Exploring the influence of individual factors on the perception of mental workload and body postures’, Ergonomics, pp. 1-16, https://doi.org/10.1080/00140139.2023.2243406 

Oberauer, K 2019, ‘Working memory and attention - a conceptual analysis and review’, Journal of Cognition, vol. 2, article 36, https://doi.org/10.5334/joc.58 

Pearson, A, Pallas, LO, Thomson, D, Doucette, E, Tucker, D, Wiechula, R, Long, L, Porritt, K & Jordan, Z 2006, ‘Systematic review of evidence on the impact of nursing workload and staffing on establishing healthy work environments’, International Journal of Evidence-Based Healthcare, vol. 4, pp. 337–384, https://doi.org/10.1111/j.1479-6988.2006.00055.x 

Pei, H, Ma, Y, Li, W, Liu, X & Zhang, C 2023, ‘Mental workload evaluation model of receiver aircraft pilots based on multiple resource theory’, Human Factors and Ergonomics in Manufacturing & Service Industries, vol. 34, no. 2, article 21018, https://doi.org/10.1002/hfm.21018 

Pickup, L, Wilson, JR, Sharpies, S, Norris, B, Clarke, T & Young, MS 2005, ‘Fundamental examination of mental workload in the rail industry’, Theoretical Issues in Ergonomics Science, vol. 6, pp. 463–482, https://doi.org/10.1080/14639220500078021 

Piranveyseh, P, Kazemi, R, Soltanzadeh, A & Smith, A 2022, ‘A field study of mental workload: conventional bus drivers versus bus rapid transit drivers’, Ergonomics, vol. 65, pp. 804-814, https://doi.org/10.1080/00140139.2021.1992021 

Puspita, MA, Muslim, E, Moch, BN & Aristides, A 2015, ‘Fatigue measurement in car driving activity using physiological, cognitive, and subjective approaches fatigue’, International Journal of Technology, vol. 6, pp. 971–975, http://dx.doi.org/10.14716/ijtech.v6i6.1446 

Puusepp, I, Tammi, T, Linnavalli, T, Huotilainen, M, Laine, S, Kuusisto, E & Tirri, K 2024, ‘Changes in physiological arousal during an arithmetic task: profiles of elementary school students and their associations with mindset, task performance and math grade, Scientific Reports, vol. 14, article 1606, https://doi.org/10.1038/s41598-024-51683-7 

Rann, JC & Almor, A 2022, ‘Effects of verbal tasks on driving simulator performance’, Cognitive Research: Principles and Implications, vol. 7, no. 12, https://doi.org/10.1186/s41235-022-00357-x 

Remington, R & Loft, S 2015, ‘Attention and multitasking’, In: Boehm-Davis, DA, Durso, FT, Lee, JD (Eds.), APA Handbook of Human System Integration. American Psychological Association, pp. 261-276

Rodemer, M, Karch, J & Bernholt, S 2023, ‘Pupil dilation as cognitive load measure in instructional videos on complex chemical representations’, Frontiers in Education, vol. 8, https://doi.org/10.3389/feduc.2023.1062053 

Safari, M, Shalbaf, R, Bagherzadeh, S & Shalbaf, A 2024, ‘Classification of mental workload using brain connectivity and machine learning on electroencephalogram data’, Scientific Reports, vol. 14, article 9153,  https://doi.org/10.1038/s41598-024-59652-w 

Sahaï, A, Barré, J & Bueno, M 2021, ‘Urgent and non-urgent takeovers during conditional automated driving on public roads: The impact of different training programmes’, Transportation Research Part F: Traffic Psychology and Behaviour, vol. 81, pp. 130–143. https://doi.org/10.1016/j.trf.2021.06.001 

Salma, SA, Widyanti, A, Muslim, K, Wijayanto, T, Trapsilawati, F, Arini, HM & Wibawa, AD 2024, ‘The influence of trust, health beliefs, and technology acceptance on the intent to use an mhealth in indonesia: an empirical study of users and non-users, International Journal of Technology, vol. 15, no. 5, pp. 1247-1257, https://doi.org/10.14716/ijtech.v15i5.5291 

Schoedel, R, Hilbert, S, Bühner, M & Stachl, C 2018, ‘One way to guide them all: Wayfinding strategies and the examination of gender-specific navigational instructions in a real-driving context’, Transportation Research Part F: Traffic Psychology and Behaviour, vol. 58, pp. 754–768, https://doi.org/10.1016/j.trf.2018.06.030 

Scott, JJ & Gray, R 2008 ‘A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving’, Human Factors: The Journal of the Human Factors and Ergonomics Society, vol. 50, no. 2, pp. 264–275, https://doi.org/10.1518/001872008X250674 

Serences, J, Scolari, M & Awh, E 2009, ‘Online response-selection and the attentional blink: Multiple-processing channels’, Visual Cognition, vol. 17, pp. 531–554, https://doi.org/10.1080/13506280802102541 

Sharples, S & Megaw, T 2015, ‘Definition and measurement of mental workload’, In: Wilson, JR, Sharples, S (Eds.), Evaluation of Human Work Fourth Edition. CRC Press, Boca Raton, FL, pp. 515–548 

Silva, FPda 2014, ‘Mental workload, task demand and driving performance: what relation?’, Procedia - Social and Behavioral Sciences, 162, pp. 310–319. https://doi.org/10.1016/j.sbspro.2014.12.212 

Smith, BT & Hess, TM 2015, ‘The impact of motivation and task difficulty on resource engagement: differential influences on cardiovascular responses of young and older adults’, Motivation Science, vol. 1, pp. 22–36, https://doi.org/10.1037/mot0000012 

Sudiarno, A, Dewi, RS, Widyaningrum, R, Akbar, RA, Sudianto, Y, Prastyabudi, WA & Ahmadi, A 2024, ‘Analysis of human performance and potential application of Virtual Reality (VR) shooting games as a shooting training simulator for military personnel’, International Journal of Technology, vol. 15, no. 1, pp. 87-98, https://doi.org/10.14716/ijtech.v15i1.5303 

Sukhera, J 2022, ‘Narrative reviews: flexible, rigorous, and practical’, Journal of Graduate Medical Education, vol. 14, no. 4, pp. 414–417, https://doi.org/10.4300/JGME-D-22-00480.1 

Takae, Y, Yamamura, T, Kuge, N, Miyake, S, Yamada, S & Hagiwara, C 2010, ‘Effects of alcohol intoxication on a multi-task simulator operation performance’, Review of Automotive Engineering, vol. 31, pp. 150–155, https://doi.org/10.11351/jsaereview.31.150 

Tao, D, Tan, H, Wang, H, Zhang, X, Qu, X & Zhang, T 2019, ‘A systematic review of physiological measures of mental workload’, International Journal Environmental Research Public Health, vol. 16, no. 15, article 2716, https://doi.org/10.3390/ijerph16152716 

Tjolleng, A, Jung, K, Hong, W, Lee, W, Lee, B, You, H, Son, J & Park, S 2017, ‘Classification of a Driver’s cognitive workload levels using artificial neural network on ECG signals’, Applied Ergonomics, vol. 59, pp. 326–332, https://doi.org/10.1016/j.apergo.2016.09.013 

Van Acker, BB, Parmentier, DD, Vlerick, P & Saldien, J 2018, ‘Understanding mental workload: from a clarifying concept analysis toward an implementable framework’ Cognition, Technology & Work, vol. 20, pp. 351-365, https://doi.org/10.1007/s10111-018-0481-3 

Verdière, KJ, Roy, RN & Dehais, F 2018, ‘Detecting pilot’s engagement using fNIRS connectivity features in an automated vs. manual landing scenario’, Frontiers in Human Neuroscience, vol. 12, pp. 1-14, https://doi.org/10.3389/fnhum.2018.00006 

Vitense, HS, Jacko, JA & Emery, VK 2003, ‘Multimodal feedback: an assessment of performance and mental workload’, Ergonomics, vol. 46, pp. 68–87, https://doi.org/10.1080/00140130303534 

Wanyan, X, Zhuang, D, Lin, Y, Xiao, X & Song, J-W 2018, ‘Influence of mental workload on detecting information varieties revealed by mismatch negativity during flight simulation’, International Journal of Industrial Ergonomics, vol. 64, pp. 1-7, https://doi.org/10.1016/j.ergon.2017.08.004 

Warvel, L, Scerbo, MW 2015, ‘Measurement of mental workload changes during laparoscopy with a visual-spatial task’, In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol.  59, pp. 503–507. https://doi.org/10.1177/1541931215591108 

Waxenbaum, JA, Reddy, V & Varacallo, M 2021, ‘Anatomy, autonomic nervous system’, In: StatPearls. StatPearls Publishing, Treasure Island, FL.

Whulanza, Y, Kusrini, E, Sangaiah, AK, Hermansyah, H, Sahlan, M, Asvial, M, Harwahyu, R & Fitri, IR 2024, ‘Bridging human and machine cognition: advances in brain-machine interface and reverse engineering the brain’, International Journal of Technology, vol. 15, no. 5, pp. 1194-1202, https://doi.org/10.14716/ijtech.v15i5.7297 

Wickens, CD, Hollands, JG, Banbury, S & Parasuraman, R 2012, ‘Engineering psychology and human performance’, Taylor & Francis Group, London

Widyanti, A, Sofiani, NF, Soetisna, HR & Muslim, K 2017, ‘Eye blink rate as a measure of mental workload in a driving task: convergent or divergent with other measures?’, International Journal of Technology, vol. 8, no. 2, pp. 283–291. https://doi.org/10.14716/ijtech.v8i2.6145 

Wilson, GF 2002, ‘An analysis of mental workload in pilots during flight using multiple psychophysiological measures’, The International Journal of Aviation Psychology, vol. 12, pp. 3-18, https://doi.org/10.1207/S15327108IJAP1201_2 

Young, MS, Brookhuis, KA, Wickens, CD & Hancock, PA 2015, ‘State of science: mental workload in ergonomics’, Ergonomics, vol. 58, pp. 1–17, https://doi.org/10.1080/00140139.2014.956151 

Zakeri, Z, Arif, A, Omurtag, A, Breedon, P & Khalid, A 2023, ‘Multimodal assessment of cognitive workload using neural, subjective and behavioural measures in smart factory settings’, Sensors, vol. 23, article 8926, https://doi.org/10.3390/s23218926 

Zhang, J-Y, Liu, S-L, Feng, Q-M, Gao, J-Q & Zhang, Q 2017, ‘Correlative evaluation of mental and physical workload of laparoscopic surgeons based on surface electromyography and eye-tracking signals’, Scientific Reports, vol. 7, article 11095, https://doi.org/10.1038/s41598-017-11584-4