The Influence of Task Characteristics on Multiple Objective and Subjective Cognitive Load Measures

  • Seyed Mohammad Mahdi MirhoseiniEmail author
  • Pierre-Majorique Léger
  • Sylvain Sénécal
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 16)


Using Electroencephalography (EEG), this study aims at extracting three features from instantaneous mental workload measure and link them to different aspect of the workload construct. An experiment was designed to investigate the effect of two workload inductors (Task difficulty and uncertainty) on extracted features along with a subjective measure of mental workload. Results suggest that both subjective and objective measures of workload are able to capture the effect of task difficulty; however only accumulated load was found to be sensitive to task uncertainty. We discuss that the three EEG measures derived from instantaneous workload can be used as criteria for designing more efficient information systems.


NeuroIS Mental workload Accumulated load Instantaneous load Peak load Electroencephalography 


  1. 1.
    Gopher, D., Braune, R.: On the psychophysics of workload: Why bother with subjective measures? Hum. Factors J. Hum. Factors Ergon. Soc. 26, 519–532 (1984)Google Scholar
  2. 2.
    Paas, F., Sweller, J.: An evolutionary upgrade of cognitive load theory: Using the human motor system and collaboration to support the learning of complex cognitive tasks. Educ. Psychol. Rev. 24, 27–45 (2011)CrossRefGoogle Scholar
  3. 3.
    Paas, F., Tuovinen, J.E., Tabbers, H., Van Gerven, P.W.: Cognitive load measurement as a means to advance cognitive load theory. Educ. Psychol. 38, 63–71 (2003)CrossRefGoogle Scholar
  4. 4.
    Riedl, R., Léger, P.-M.: Tools in NeuroIS Research: An Overview. In: Fundamentals of NeuroIS, pp. 47–72. Springer, Berlin (2016)CrossRefGoogle Scholar
  5. 5.
    Ortiz De Guinea, A., Titah, R., Léger, P.-M.: Measure for measure: A two study multi-trait multi-method investigation of construct validity in IS research. Comput. Hum. Behav. 29, 833–844 (2013)CrossRefGoogle Scholar
  6. 6.
    de Guinea, A.O., Titah, R., Léger, P.-M.: Explicit and implicit antecedents of users’ behavioral beliefs in information systems: A neuropsychological investigation. J. Manag. Inf. Syst. 30, 179–210 (2014)CrossRefGoogle Scholar
  7. 7.
    Xie, B., Salvendy, G.: Prediction of mental workload in single and multiple tasks environments. Int. J. Cogn. Ergon. 4, 213–242 (2000)CrossRefGoogle Scholar
  8. 8.
    De Jong, T.: Cognitive load theory, educational research, and instructional design: Some food for thought. Instr. Sci. 38, 105–134 (2010)CrossRefGoogle Scholar
  9. 9.
    Ryu, K., Myung, R.: Evaluation of mental workload with a combined measure based on physiological indices during a dual task of tracking and mental arithmetic. Int. J. Ind. Ergon. 35, 991–1009 (2005)CrossRefGoogle Scholar
  10. 10.
    Wickens, C.D.: Multiple resources and performance prediction. Theor. Issues Ergon. Sci. 3, 159–177 (2002)CrossRefGoogle Scholar
  11. 11.
    DeStefano, D., LeFevre, J.-A.: Cognitive load in hypertext reading: A review. Comput. Hum. Behav. 23, 1616–1641 (2007)CrossRefGoogle Scholar
  12. 12.
    Mizuno, K., Tanaka, M., Yamaguti, K., Kajimoto, O., Kuratsune, H., Watanabe, Y.: Mental fatigue caused by prolonged cognitive load associated with sympathetic hyperactivity. Behav. Brain Funct. 7, 1 (2011)CrossRefGoogle Scholar
  13. 13.
    Gwizdka, J.: Distribution of cognitive load in web search. J. Am. Soc. Inf. Sci. Technol. 61, 2167–2187 (2010)CrossRefGoogle Scholar
  14. 14.
    Gopher, D., Donchin, E.: Workload: An Examination of the Concept. In: Boff, K.R., Kaufman, L., Thomas, J.P. (eds.) Handbook of Perception and Human Performance, Vol II, Cognitive Processes and Performance, pp. 41:1–41:49. Wiley, New York (1986)Google Scholar
  15. 15.
    O’Donnell, R.D., Eggemeier, F.T.: Workload Assessment Methodology. In: Boff, K., Kaufman, L., Thomas, J. (eds.) Handbook of Perception and Human Performance, Vol II, Cognitive Processes and Performance. Wiley, New York (1986)Google Scholar
  16. 16.
    Colle, H.A., Reid, G.B.: Double trade-off curves with different cognitive processing combinations: Testing the cancellation axiom of mental workload measurement theory. Hum. Factors J. Hum. Factors Ergon. Soc. 41, 35–50 (1999)CrossRefGoogle Scholar
  17. 17.
    Coyne, J.T., Baldwin, C., Cole, A., Sibley, C., Roberts, D.M.: Applying Real Time Physiological Measures of Cognitive Load to improve training. In: Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience, pp. 469–478. Springer, Berlin (2009)CrossRefGoogle Scholar
  18. 18.
    Garrett, D., Peterson, D.A., Anderson, C.W., Thaut, M.H.: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. Neural Syst. Rehabil. Eng. IEEE Trans. On. 11, 141–144 (2003)CrossRefGoogle Scholar
  19. 19.
    Brouwer, A.-M., Zander, T.O., van Erp, J.B., Korteling, J.E., Bronkhorst, A.W.: Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls. Front. Neurosci. 9, 136 (2015)CrossRefGoogle Scholar
  20. 20.
    Colle, H.A., Reid, G.B.: Estimating a mental workload redline in a simulated air-to-ground combat mission. Int. J. Aviat. Psychol. 15, 303–319 (2005)CrossRefGoogle Scholar
  21. 21.
    Venables, L., Fairclough, S.H.: The influence of performance feedback on goal-setting and mental effort regulation. Motiv. Emot. 33, 63–74 (2009)CrossRefGoogle Scholar
  22. 22.
    Aljukhadar, M., Senecal, S., Daoust, C.-E.: Using recommendation agents to cope with information overload. Int. J. Electron. Commer. 17, 41–70 (2012)CrossRefGoogle Scholar
  23. 23.
    Desrocher, C., Léger, P.-M., Sénécal, S., Pagé, S.-A., Mirhoseini, S.: The influence of product type, mathematical complexity, and visual attention on the attitude toward the website: The case of online grocery shopping. Presented at the Fourteenth Pre-ICIS SIG-HCI Workshop, Fort Worth, TX, Dec 2015Google Scholar
  24. 24.
    Cameron, A.-F.: Juggling Multiple Conversations with Communication Technology: Towards a Theory of Multi-communicating Impacts in the Workplace. Queen’s University, Kingston, ON (2007)Google Scholar
  25. 25.
    Libenson, M.H.: Practical Approach to Electroencephalography. Elsevier Health Sciences, Philadelphia, PA (2012)Google Scholar
  26. 26.
    DeLeeuw, K.E., Mayer, R.E.: A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load. J. Educ. Psychol. 100, 223 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Seyed Mohammad Mahdi Mirhoseini
    • 1
    Email author
  • Pierre-Majorique Léger
    • 1
  • Sylvain Sénécal
    • 1
  1. 1.HEC MontrealMontrealCanada

Personalised recommendations