Open Access
SHS Web Conf.
Volume 189, 2024
The 2nd International Conference on Ergonomics Safety, and Health (ICESH) and the 7th Ergo-Camp (ICESH & Ergo-Camp 2023)
Article Number 01043
Number of page(s) 13
Published online 09 April 2024
  1. Comstock, J. R. & Arnegard, R. J. The Multi-Attribute Task Battery for Human Operator Workload and Strategic Behavior Research. (1992). [Google Scholar]
  2. Santiago-Espada, Y., Myer, R. R., Latorella, K. A. & Comstock, J. R. The Multi-Attribute Task Battery II (MATB-II) Software for Human Performance and Workload Research: A User’s Guide. (2011). [Google Scholar]
  3. Sharples, S. & Megaw, T. Definition and measurement of mental workload. in Evaluation of Human Work Fourth Edition (eds. Wilson, J. R. & Sharples, S.) 515–548 (CRC Press, Boca Raton, FL, 2015). [Google Scholar]
  4. Bliss, J. P. Alarm reaction patterns by pilots as a function of reaction modality. International Journal of Aviation Psychology 7, 1–14 (1997). [Google Scholar]
  5. Caldwell, J. A. & Ramspott, S. Effects of task duration on sensitivity to sleep deprivation using the multi-attribute task battery. Behavior Research Methods Instruments & Computers 30, 651–660 (1998). [Google Scholar]
  6. Wilson, G. F. & Russell, C. A. Real-time assessment of mental workload using psychophysiological measures and artificial neural networks. Human Factors 45, 635–643 (2003). [Google Scholar]
  7. Valk, P. J. L., Van Roon, D. B., Simons, R. M. & Rikken, G. Desloratadine shows no effect on performance during 6 h at 8,000 ft simulated cabin altitude. Aviation Space and Environmental Medicine 75, 433–438 (2004). [Google Scholar]
  8. Fairclough, S. H., Venables, L. & Tattersall, A. The influence of task demand and learning on the psychophysiological response. International Journal of Psychophysiology 56, 171–184 (2005). [Google Scholar]
  9. Fairclough, S. H. & Venables, L. Prediction of subjective states from psychophysiology: A multivariate approach. Biological Psychology 71, 100–110 (2006). [Google Scholar]
  10. Phillips, C. A., Repperger, D. W., Kinsler, R., Bharwani, G. & Kender, D. A quantitative model of the human-machine interaction and multi-task performance: A strategy function and the unity model paradigm. Computers in Biology and Medicine 37, 1259–1271 (2007). [CrossRef] [Google Scholar]
  11. Wilson, G. F., Caldwell, J. A. & Russell, C. A. Performance and Psychophysiological Measures of Fatigue Effects on Aviation Related Tasks of Varying Difficulty. The International Journal of Aviation Psychology 17, 219–247 (2007). [Google Scholar]
  12. Brannon, N. G., Koubek, R. J. & Voss, D. Mechanisms of knowledge degradation in a resource management task. Theoretical Issues in Ergonomics Science 9, 25–44 (2008). [Google Scholar]
  13. Hardy, D. J. & Mitrovich, A. K. Preliminary Examination of Timesharing in United States Air Force ROTC Cadets. Perceptual and Motor Skills 107, 21–28 (2008). [Google Scholar]
  14. Miyake, S. et al. Physiological responses to workload change: A test/retest examination. Applied Ergonomics 40, 987–996 (2009). [CrossRef] [Google Scholar]
  15. Valk, P. J. L. & Simons, M. Effects of loratadine/montelukast on vigilance and alertness task Performance in a simulated cabin environment. Advances in Therapy 26, 89–98 (2009). [CrossRef] [Google Scholar]
  16. Carlozzi, N. E. et al. Personality and Reaction Time after Sleep Deprivation. Current Psychology 29, 24–33 (2010). [CrossRef] [Google Scholar]
  17. Takae, Y. et al. Effects of Alcohol Intoxication on a Multi-task Simulator Operation Performance. Review of Automotive Engineering 31, 150–155 (2010). [Google Scholar]
  18. Lopez, N., Previc, F. H., Fischer, J., Heitz, R. P. & Engle, R. W. Effects of sleep deprivation on cognitive performance by United States Air Force pilots. Journal of Applied Research in Memory and Cognition 1, 27–33 (2012). [Google Scholar]
  19. Wang, Z., Hope, R. M., Wang, Z., Ji, Q. & Gray, W. D. Cross-subject workload classification with a hierarchical Bayes model. Neuroimage 59, 64–69 (2012). [Google Scholar]
  20. Chiappe, D., Conger, M., Liao, J., Caldwell, J. L. & Vu, K.-P. L. Improving multi-tasking ability through action videogames. Applied Ergonomics 44, 278–284 (2013). [CrossRef] [Google Scholar]
  21. Hsu, B.-W., Wang, M.-J. J., Chen, C.-Y. & Chen, F. Effective Indices for Monitoring Mental Workload While Performing Multiple Tasks. Perceptual and Motor Skills 121, 94–117 (2015). [Google Scholar]
  22. Borghini, G. et al. Quantitative Assessment of the Training Improvement in a Motor-Cognitive Task by Using EEG, ECG and EOG Signals. Brain Topography 29, 149–161 (2016). [Google Scholar]
  23. Freiberger, J. J. et al. Assessment of the interaction of hyperbaric N-2, CO2, and O-2 on psychomotor performance in divers. Journal of Applied Physiology 121, 953–964 (2016). [Google Scholar]
  24. Gutzwiller, R. S., Wickens, C. D. & Clegg, B. A. The Role of Time on Task in Multi-task Management. Journal of Applied Research in Memory and Cognition 5, 176–184 (2016). [Google Scholar]
  25. Nelson, J. et al. The Effects of Transcranial Direct Current Stimulation (tDCS) on Multitasking Throughput Capacity. Frontiers in Human Neuroscience 10, 1–13 (2016). [Google Scholar]
  26. Roy, R. N., Bonnet, S., Charbonnier, S. & Campagne, A. Efficient Workload Classification based on Ignored Auditory Probes: A Proof of Concept. Frontiers in Human Neuroscience 10, 1–12 (2016). [Google Scholar]
  27. Valk, P. J. L., Simons, R., Jetten, A. M., Valiente, R. & Labeaga, L. Cognitive Performance Effects of Bilastine 20 mg During 6 Hours at 8000 ft Cabin Altitude. Aerospace Medicine and Human Performance 87, 622–627 (2016). [CrossRef] [Google Scholar]
  28. Wickens, C. D. et al. Time Sharing Between Robotics and Process Control: Validating a Model of Attention Switching. Human Factors 58, 322–343 (2016). [Google Scholar]
  29. Hefron, R. G., Borghetti, B. J., Christensen, J. C. & Kabban, C. M. S. Deep long short-term memory structures model temporal dependencies improving cognitive workload estimation. Pattern Recognition Letters 94, 96–104 (2017). [Google Scholar]
  30. Kim, J. H. & Yang, X. Applying fractal analysis to pupil dilation for measuring complexity in a process monitoring task. Applied Ergonomics 65, 61–69 (2017). [CrossRef] [Google Scholar]
  31. Nixon, J. & Charles, R. Understanding the human performance envelope using electrophysiological measures from wearable technology. Cogn Tech Work 19, 655–666 (2017). [CrossRef] [Google Scholar]
  32. Wang, P., Fang, W. & Guo, B. A colored petri nets based workload evaluation model and its validation through Multi-Attribute Task Battery-II. Applied Ergonomics 60, 260–274 (2017). [CrossRef] [Google Scholar]
  33. Hefron, R., Borghetti, B., Kabban, C. S., Christensen, J. & Estepp, J. Cross-participant EEG-based assessment of cognitive workload using multi-path convolutional recurrent neural networks. Sensors 18, 1–27 (2018). [Google Scholar]
  34. Liu, S. & Nam, C. S. Quantitative modeling of user performance in multitasking environments. Computers in Human Behavior 84, 130–140 (2018). [CrossRef] [Google Scholar]
  35. Miller, J. et al. An Updated Version of the U.S. Air Force Multi-Attribute Task Battery (AF-MATB). (2014). [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.