Advertisement

Cognitive Metrics Profiling: A Model-Driven Approach to Predicting and Classifying Workload

  • Christopher A. StevensEmail author
  • Christopher R. Fisher
  • Megan B. Morris
  • Christopher Myers
  • Sarah Spriggs
  • Allen Dukes
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 780)

Abstract

Workload management is integral to the success of human-machine teams, and involves measuring and predicting workload and implementing proactive interventions to mitigate the adverse effects of degraded performance. Common approaches to workload measurement rely on the use of subjective, behavioral, and physiological metrics. These approaches suffer from two important limitations. First, the mapping between workload, subjective ratings, behavior, and physiology is complex and noisy, resulting in high uncertainty. Second, metrics based on subjective ratings, behavior, and physiology often fail to explain why performance degrades, and consequentially does not inform the development of mitigation strategies. As an alternative, we propose using cognitive metrics profiling (CMP) to improve the measurement and prediction of workload. This approach uses computational cognitive models to simulate the activity within individual cognitive systems, such as vision, audition, memory, and motor, to measure and understand workload. We discuss how CMP can be used in an unmanned vehicle control task.

Keywords

Cognitive workload Physiology ACT-R Computational cognitive models 

References

  1. 1.
    Maslach, C., Schaufeli, W.B., Leiter, M.P.: Job burnout. Annu. Rev. Psychol. 52(1), 397–422 (2001)CrossRefGoogle Scholar
  2. 2.
    Cox-Fuenzalida, L.E.: Effect of workload history on task performance. Hum. Factors 49(2), 277–291 (2007)CrossRefGoogle Scholar
  3. 3.
    Miller, S.: Workload Measures. National Advanced Driving Simulator, Iowa City (2001)Google Scholar
  4. 4.
    Lean, Y., Shan, F.: Brief review on physiological and biochemical evaluations of human mental workload. Hum. Factors Ergon. Manuf. Serv. Ind. 22(3), 177–187 (2012)CrossRefGoogle Scholar
  5. 5.
    Matthews, G., Reinerman-Jones, E., Barber, D.J., Abich, J.I.: The psychometrics of mental workload: multiple measures are sensitive but divergent. Hum. Factors 57, 125–143 (2015)CrossRefGoogle Scholar
  6. 6.
    Gray, W.D., Schoelles, M.J., Myers, C.W.: Profile before optimizing: a cognitive metrics approach to workload analysis. In: CHI 2005 Extended Abstracts on Human Factors in Computing Systems, pp. 1411–1414. ACM (2005)Google Scholar
  7. 7.
    Rowe, A., Spriggs, S., Hooper, D.: Fusion: a framework for human interaction with flexible-adaptive automation across multiple unmanned systems. In: Proceedings of the 18th Symposium on Aviation Psychology, pp. 464–469 (2015)Google Scholar
  8. 8.
    Draper, M., Calhoun, G., Hansen, M., Douglass, S., Spriggs, S., Patzek, M., Rowe, A., Evans, D., Ruff, H., Behymer, K., et al.: Intelligent multi-unmanned vehicle planner with adaptive collaborative control technologies (impact). In: International Symposium of Aviation Psychology (2017)Google Scholar
  9. 9.
    Task Force of the European Society of Cardiology: Heart rate variability, standards of measurement, physiological interpretation, and clinical use. Circulation 93, 1043–1065 (1996)CrossRefGoogle Scholar
  10. 10.
    Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., Babiloni, F.: Measuring neuro-physiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci. Biobehav. Rev. 44, 58–75 (2014)CrossRefGoogle Scholar
  11. 11.
    Klimesch, W.: EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29(2–3), 169–195 (1999)CrossRefGoogle Scholar
  12. 12.
    Kamzanova, A.T., Kustubayeva, A.M., Matthews, G.: Use of eeg workload indices for diagnostic monitoring of vigilance decrement. Hum. Factors 56(6), 1136–1149 (2014)CrossRefGoogle Scholar
  13. 13.
    Fogarty, C., Stern, J.A.: Eye movements and blinks: their relationship to higher cognitive processes. Int. J. Psychophysiol. 8(1), 35–42 (1989)CrossRefGoogle Scholar
  14. 14.
    Ahlstrom, U., Friedman-Berg, F.J.: Using eye movement activity as a correlate of cognitive workload. Int. J. Ind. Ergon. 36(7), 623–636 (2006)CrossRefGoogle Scholar
  15. 15.
    Beatty, J.: Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychol. Bull. 91(2), 276 (1982)CrossRefGoogle Scholar
  16. 16.
    Porter, G., Troscianko, T., Gilchrist, I.D.: Effort during visual search and counting: insights from pupillometry. Q. J. Exp. Psychol. 60(2), 211–229 (2007)CrossRefGoogle Scholar
  17. 17.
    Anderson, J.R.: How Can the Human Mind Occur in the Physical Universe? Oxford University Press, Oxford (2007)Google Scholar
  18. 18.
    Anderson, J.R., Bothell, D., Lebiere, C., Matessa, M.: An integrated theory of list memory. J. Mem. Lang. 38(4), 341–380 (1998)CrossRefGoogle Scholar
  19. 19.
    Salvucci, D.D.: Modeling driver behavior in a cognitive architecture. Hum. Factors: J. Hum. Factors Ergon. Soc. 48(2), 362–380 (2006)CrossRefGoogle Scholar
  20. 20.
    Anderson, J.R.: Problem solving and learning. Am. Psychol. 48(1), 35 (1993)CrossRefGoogle Scholar
  21. 21.
    Gunzelmann, G., Gross, J.B., Gluck, K.A., Dinges, D.F.: Sleep deprivation and sustained attention performance: integrating mathematical and cognitive modeling. Cogn. Sci. 33(5), 880–910 (2009)CrossRefGoogle Scholar
  22. 22.
    Fisher, C.R., Myers, C., Reem, H.M., Stevens, C., Hack, C.E., Gearhart, J., Gunzelmann, G.: A cognitive-pharmacokinetic computational model of the effect of toluene on performance. In: Gunzelmann, G., Howes, A., Tenbrink, T., Davelaar, E. (eds.) Proceedings of the 39th Annual Conference of the Cognitive Science Society. Cognitive Science Society, Austin, TX (2017)Google Scholar
  23. 23.
    Stevens, C.A., Taatgen, N.A., Cnossen, F.: Instance-based models of metacognition in the prisoner’s dilemma. Top. Cogn. Sci. 8(1), 322–334 (2016)CrossRefGoogle Scholar
  24. 24.
    Jo, S., Myung, R., Yoon, D.: Quantitative prediction of mental workload with the ACT-R cognitive architecture. Int. J. Ind. Ergon. 42(4), 359–370 (2012)CrossRefGoogle Scholar
  25. 25.
    Fisher, C.R., Walsh, M.M., Blaha, L.M., Glunzelmann, G., Veksler, B.: Efficient parameter estimation of cognitive models for real-time performance monitoring and adaptive interfaces (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature (outside the USA) 2019

Authors and Affiliations

  • Christopher A. Stevens
    • 1
    Email author
  • Christopher R. Fisher
    • 1
  • Megan B. Morris
    • 1
    • 2
  • Christopher Myers
    • 1
  • Sarah Spriggs
    • 1
  • Allen Dukes
    • 1
  1. 1.Air Force Research LaboratoryWright-Patterson Air Force BaseUSA
  2. 2.Ball Aerospace and Technologies CorporationFairbornUSA

Personalised recommendations