Effects of Neuro-Cognitive Load on Learning Transfer Using a Virtual Reality-Based Driving System
Open accessAbstract
Understanding the ways different people perceive and apply acquired knowledge, especially when driving, is an important area of study. This study introduced a novel virtual reality (VR)-based driving system to determine the effects of neuro-cognitive load on learning transfer. In the experiment, easy and difficult routes were introduced to the participants, and the VR system is capable of recording eye-gaze, pupil dilation, heart rate, as well as driving performance data. So, the main purpose here is to apply multimodal data fusion, several machine learning algorithms, and strategic analytic methods to measure neurocognitive load for user classification. A total of ninety-eight (98) university students participated in the experiment, in which forty-nine (49) were male participants and forty-nine (49) were female participants. The results showed that data fusion methods achieved higher accuracy compared to other classification methods. These findings highlight the importance of physiological monitoring to measure mental workload during the process of learning transfer.
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AI assessments could employ multi-modal data fusion techniques that estimate and react to cognitive loads by adapting the learning environment to the learner's state.
“Empowered by AI, the assessments could employ multi-modal data fusion techniques that estimate and react to cognitive loads by adapting the learning environment to the state of the learner [128].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [128]
Use of performance metrics in assessments allows specific feedback and targeted adjustments to optimize numeric cognition.
“The use of performance metrics in assessments will allow specific feedback to be given and targeted adjustments to be made to optimize numeric cognition [128].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [128]
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Cites
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812 · see citing works
Cognitive Load During Problem Solving: Effects on Learning
Sweller, J. (1988) · Cognitive Science · doi:10.1016/0364-0213(88)90023-7 · Cited by 11,048 · see citing works
Cognitive load theory, learning difficulty, and instructional design
Sweller, J. (1994) · Learning and Instruction · doi:10.1016/0959-4752(94)90003-5 · Cited by 3,801 · see citing works
Cognitive 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 · see citing works
Multimodal fusion for multimedia analysis: a survey
Atrey, P. K., Hossain, M. A., El Saddik, A., & Kankanhalli, M. S. (2010) · Multimedia Systems · doi:10.1007/s00530-010-0182-0 · Cited by 1,279 · see citing works
Cognitive load theory, educational research, and instructional design: some food for thought
de Jong, T. (2010) · Instructional Science · doi:10.1007/s11251-009-9110-0 · Cited by 1,180 · see citing works
Instructional control of cognitive load in the training of complex cognitive tasks
Paas, F. G. W. C., & Van Merriënboer, J. J. G. (1994) · Educational Psychology Review · doi:10.1007/bf02213420 · Cited by 968 · see citing works
A Reconsideration of Cognitive Load Theory
Schnotz, W., & Kürschner, C. (2007) · Educational Psychology Review · doi:10.1007/s10648-007-9053-4 · Cited by 755 · see citing works
Physiological workload reactions to increasing levels of task difficulty
VELTMAN, J. A., & GAILLARD, A. W. K. (1998) · Ergonomics · doi:10.1080/001401398186829 · Cited by 433 · see citing works
Choi, H.-H., van Merriënboer, J. J. G., & Paas, F. (2014) · Educational Psychology Review · doi:10.1007/s10648-014-9262-6 · Cited by 413 · see citing works
Mehler, B. L., Reimer, B., Coughlin, J. F., & Dusek, J. A. (2009) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2138-02 · Cited by 394 · see citing works
Estimating cognitive load using remote eye tracking in a driving simulator
Palinko, O., Kun, A. L., Shyrokov, A., & Heeman, P. A. (2010) · ACM Symposium on Eye Tracking Research and Applications (ETRA) · doi:10.1145/1743666.1743701 · Cited by 382 · see citing works
Standardization of Automated Analyses of Oculomotor Fixation and Saccadic Behaviors
Komogortsev, O. V., Gobert, D. V., Jayarathna, S., Koh, D. H., & Gowda, S. M. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2057429 · Cited by 320 · see citing works
Driver workload and eye blink duration
Benedetto, S., Pedrotti, M., Minin, L., Baccino, T., Re, A., & Montanari, R. (2010) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2010.12.001 · Cited by 304 · see citing works
Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis
Engström, J., Markkula, G., Victor, T., & Merat, N. (2017) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720817690639 · Cited by 299 · see citing works
Glucose administration, heart rate and cognitive performance: effects of increasing mental effort
Kennedy, D. O., & Scholey, A. B. (2000) · Psychopharmacology · doi:10.1007/s002139900335 · Cited by 267 · see citing works
Jorna, P. G. A. M. (1992) · Biological Psychology · doi:10.1016/0301-0511(92)90017-o · Cited by 260 · see citing works
Backs, R. W., & Seljos, K. A. (1994) · International Journal of Psychophysiology · doi:10.1016/0167-8760(94)90042-6 · Cited by 207 · see citing works
Detecting short periods of elevated workload: A comparison of nine workload assessment techniques.
Verwey, W. B., & Veltman, J. A. (1996) · Journal of Experimental Psychology Applied · doi:10.1037/1076-898x.2.3.270 · Cited by 196 · see citing works
Limitations of subjective cognitive load measures in simulation‐based procedural training
Naismith, L. M., Cheung, J. J. H., Ringsted, C. V., & Cavalcanti, R. B. (2015) · Medical Education · doi:10.1111/medu.12732 · Cited by 167 · see citing works
Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention
Zhang, L., Wade, J. W., Bian, D., Fan, J., Swanson, A. R., Weitlauf, A. S., Warren, Z. E., & Sarkar, N. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2582490 · Cited by 142 · see citing works
Handbook of Human Factors Testing and Evaluation
(2019) · CRC Press eBooks · doi:10.1201/9781003000815 · Cited by 141 · see citing works
Casali, J. G., & Wierwille, W. W. (1983) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/001872088302500602 · Cited by 139 · see citing works
Carroll, D., Turner, J. R., & Hellawell, J. C. (1986) · Psychophysiology · doi:10.1111/j.1469-8986.1986.tb00613.x · Cited by 137 · see citing works
Design of a Gaze-Sensitive Virtual Social Interactive System for Children With Autism
Lahiri, U., Warren, Z. E., & Sarkar, N. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2011.2153874 · Cited by 122 · see citing works
Validity of Cognitive Load Measures in Simulation-Based Training
Naismith, L. M., & Cavalcanti, R. B. (2015) · Academic Medicine · doi:10.1097/acm.0000000000000893 · Cited by 106 · see citing works
Cardiovascular response to Stroop: Effect of verbal response and task difficulty
Boutcher, Y. N., & Boutcher, S. H. (2006) · Biological Psychology · doi:10.1016/j.biopsycho.2006.04.005 · Cited by 86 · see citing works
Bulling, A., & Zander, T. O. (2014) · IEEE Pervasive Computing · doi:10.1109/mprv.2014.42 · Cited by 76 · see citing works
Multimodal Recognition of Cognitive Workload for Multitasking in the Car
Putze, F., Jarvis, J.-P., & Schultz, T. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.913 · Cited by 57 · see citing works
Ettenhofer, M. L., Guise, B. J., Brandler, B. J., Bittner, K., Gimbel, S. I., Cordero, E., Schmitt, S. N., Williams, K., Cox, D. J., Roy, M. J., et al. (2019) · Neurorehabilitation · doi:10.3233/nre-192718 · Cited by 53 · see citing works
Cognitive Workload Analysis of Fighter Aircraft Pilots in Flight Simulator Environment
Mohanavelu, K., Poonguzhali, S., Ravi, D. B., Singh, P. K., Mahajabin, M., Ramachandran, K., Singh, U. K., & Jayaraman, S. (2020) · Defence Science Journal · doi:10.14429/dsj.70.14539 · Cited by 48 · see citing works
Assessment of Drivers' Workload: Performance and Subjective and Physiological Indexes
Brookhuis, K., & de Waard, D. (2000) · CRC Press eBooks · doi:10.1201/b12791-2.5 · Cited by 48 · see citing works
Causse, M., Baracat, B., Pastor, J., & Dehais, F. (2011) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-011-9163-0 · Cited by 43 · see citing works
Look into my Eyes: Using Pupil Dilation to Estimate Mental Workload for Task Complexity Adaptation
Kosch, T., Hassib, M., Buschek, D., & Schmidt, A. (2018) · Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3170427.3188643 · Cited by 43 · see citing works
Driving problems and adaptive driving behaviour after brain injury: A qualitative assessment
Lundqvist, A., & Rönnberg, J. (2001) · Neuropsychological Rehabilitation · doi:10.1080/09602010042000240 · Cited by 41 · see citing works
Imhoff, S., Lavallière, M., Teasdale, N., & Fait, P. E. (2016) · Neurorehabilitation · doi:10.3233/nre-161354 · Cited by 38 · see citing works
Photoplethysmogram-based Cognitive Load Assessment Using Multi-Feature Fusion Model
Zhang, X., Lyu, Y., Qu, T., Qiu, P., Luo, X., Zhang, J., Fan, S., & Shi, Y. (2019) · ACM Transactions on Applied Perception · doi:10.1145/3340962 · Cited by 28 · see citing works
Lenneman, J. K., Shelly, A. R., & Backs, R. W. (2005) · International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design (Driving Assessment Conference) · doi:10.17077/drivingassessment.1203 · Cited by 26 · see citing works
Cognitive load measurement while driving
Gabaude, C., Baracat, B., Jallais, C., & Bonniaud, M. (2012) · doi:10.13140/2.1.1071.4888 · Cited by 13 · see citing works
Multivariate Data Analytics to Identify Driver’s Sleepiness, Cognitive load, and Stress
Barua, S. (2019) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 6 · see citing works
Flagged first, then by citations
3 of the 43 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- Si, J.; Dongsik, K. How do instructional sequencing methods affect cognitive load, learning transfer, and learning time. Educ. Res. 2011, 2, 1362-1372.
- Meshkati, N.; Hancock, P.A.; Rahimi, M.; Dawes, S.M. Techniques in Mental Workload Assessment; Taylor & Francis: Abingdon-on- Thames, UK, 1995.
- Pomplun, M.; Sunkara, S. Pupil dilation as an indicator of cognitive workload in human-computer interaction. In Proceedings of the International Conference on HCI, Crete, Greece, 22-27 June 2003.
- Hussain, S.; Chen, S.; Calvo, R.A.; Chen, F. Classification of cognitive load from task performance & multichannel physiology during affective changes. In Proceedings of the Conference on Multimodal Interaction, Alicante, Spain, 14-18 November 2011.
Cited by
Sorted by citations
Halkiopoulos, C., & Gkintoni, E. (2024) · Electronics · doi:10.3390/electronics13183762 · Cited by 328 · see citing works
“The use of performance metrics in assessments will allow specific feedback to be given and targeted adjustments to be made to optimize numeric cognition [128].”
A review study on eye-tracking technology usage in immersive virtual reality learning environments
Shadiev, R., & Li, D. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104681 · Cited by 110 · see citing works
Online aviation learning experience during the COVID‐19 pandemic in Hong Kong and Mainland China
Ng, D. T. K. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13185 · Cited by 53 · see citing works
Blackmore, K. L., Smith, S. P., Bailey, J. D., & Krynski, B. (2024) · Simulation & Gaming · doi:10.1177/10468781241236688 · Cited by 32 · see citing works
Exploring Eye Tracking to Detect Cognitive Load in Complex Virtual Reality Training
Nasri, M., Kosa, M., Chukoskie, L., Moghaddam, M., & Harteveld, C. (2024) · IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) · doi:10.1109/ismar-adjunct64951.2024.00022 · Cited by 32 · see citing works
Design and evaluation of an adaptive virtual reality training system
Reyes, C. I. A., Wozniak, D., Ham, A., & Zahabi, M. (2023) · Virtual Reality · doi:10.1007/s10055-023-00827-7 · Cited by 23 · see citing works
Makarova, I., Mustafina, J., Boyko, A., Fatikhova, L., Parsin, G., Buyvol, P. A., & Shepelev, V. (2023) · Information · doi:10.3390/info14030163 · Cited by 26 · see citing works
A Review of Factors Affecting Cognitive Load in Immersive Virtual Learning Environment
Zhang, L., & Liu, G. (2023) · International Conference on Educational and Information Technology · doi:10.1109/iceit57125.2023.10107848 · Cited by 14 · see citing works
Cognitive workload evaluation of landmarks and routes using virtual reality
Abdurrahman, U. A., Zheng, L., & Yeh, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268399 · Cited by 9 · see citing works
Self-citationÇakıt, E., & Karwowski, W. (2025) · IEEE Access · doi:10.1109/access.2025.3585773 · Cited by 8 · see citing works
Heart Rate and Pupil Dilation As Reliable Measures of Neuro-Cognitive Load Classification
Abdurrahman, U. A., Zheng, L., Sharifai, A. G., & Muraina, I. D. (2022) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic55218.2022.10088296 · Cited by 7 · see citing works
Self-citationCan Virtual AI Agents Improve Cognitive Assessment? A Virtual Reality Perspective
Hebri, A., Pavel, H. R., Nikanfar, S., Farahanipad, F., & Makedon, F. S. (2025) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3733155.3734917 · Cited by 1 · see citing works
Zhou, W., Hu, X., & Wei, W. (2025) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt64023.2025.00033 · Cited by 1 · see citing works
Fidler, M., Palmberg, R. C. O., & Susilo, Y. O. (2026) · Cognitive Systems Research · doi:10.1016/j.cogsys.2026.101446 · Cited by 1 · see citing works
Assessing the Effects of Landmarks and Routes on Neuro-Cognitive Load Using Virtual Environment
Abdurrahman, U. A., Zheng, L., & Haruna, U. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-1607-6_57 · Cited by 1 · see citing works
Self-citationA Virtual Reality Simulation Pipeline for Online Mental Workload ModelingPreprint
Wilson, R. L., Browne, D., Wagstaff, J. M., & McGuire, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.03977 · Cited by 1 · see citing works
Yang, Q., Huang, Y. D., Wang, R., & Xu, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4860802
Transfer Learning with CLIP Model for Multitasking Workload Prediction in Mixed Reality Environments
Abbas, S., Jeong, H., & He, D. (2025) · Artificial intelligence · doi:10.5772/intechopen.1011699
The Impact of Data Aggregation: Advocating for Individualized Analysis in Wearable Sensor Research
König, J. L., Penaredondo, J., Lim, N., Hinze, A., & Bowen, J. (2024) · Australian Conference on Human-Computer Interaction (OzCHI) · doi:10.1145/3726986.3727031
Volk, I., Аrtyukhova, N., Аrtyukhov, А., Dluhopolskyi, O. V., Миколаївна, П. О., & Chorna, N. (2025) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs68557.2025.11322050
Abdelrahman, O., Chew, X., Malik, E. F., Keikhosrokiani, P., Khaw, K. W., Teoh, W. L., & Chong, Z. L. (2026) · SAGE Open · doi:10.1177/21582440261469601
Zhou, W., Hu, X., Ng, J. T.-D., & Wei, W. (2026) · PLoS ONE · doi:10.1371/journal.pone.0355596
Abdullah, N. H. I., Phon, D. N. E., Zulkifli, N. S. A., Pudjoatmodjo, B., Roedavan, R., & Prasetyanto, F. (2026) · IEEE International Conference on Innovation, Ethics & Emerging Tech in Engineering and Computing Education (IE2C) · doi:10.1109/ie2c69620.2026.11663046
Bonhoure, T., Verger, M., & Lavoué, É. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37982-5_8
- Authors
- Abdurrahman, U. A., Yeh, S.-C., Wong, Y., & Wei, L.
- Affiliations
- Fudan University (CN)
- National Central University (TW)
- School of Information Science and Technology, Fudan University, Shanghai 200433, China
- Corresponding author
- Abdurrahman, Usman Alhaji
- Venue
- Big Data and Cognitive Computing
- Publisher
- MDPI AG
- Published
- 2021-10-13
- Volume
- 5
- Issue
- 4
- Pages
- 54
- ISSN
- 2504-2289 (ISSN-L)
- OpenAlex
- W3205197991
- Cited by
- 24
- References
- 43
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 5 · 2023: 3 · 2022: 5 · 2021: 1
- FWCI
- 3.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Big Data and Cognitive Computing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Chinese Government Scholarship (2018GXZ021733)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- transfer of learning, virtual reality driving, mental workload, multimodal fusion, gaze, pupil dilation, heart rate, driving performance, user classification, driving routes
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