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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Maslach, C., Schaufeli, W.B., Leiter, M.P.: Job burnout. Annu. Rev. Psychol. 52(1), 397–422 (2001)
Cox-Fuenzalida, L.E.: Effect of workload history on task performance. Hum. Factors 49(2), 277–291 (2007)
Miller, S.: Workload Measures. National Advanced Driving Simulator, Iowa City (2001)
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)
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)
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)
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)
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)
Task Force of the European Society of Cardiology: Heart rate variability, standards of measurement, physiological interpretation, and clinical use. Circulation 93, 1043–1065 (1996)
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)
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)
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)
Fogarty, C., Stern, J.A.: Eye movements and blinks: their relationship to higher cognitive processes. Int. J. Psychophysiol. 8(1), 35–42 (1989)
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)
Beatty, J.: Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychol. Bull. 91(2), 276 (1982)
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)
Anderson, J.R.: How Can the Human Mind Occur in the Physical Universe? Oxford University Press, Oxford (2007)
Anderson, J.R., Bothell, D., Lebiere, C., Matessa, M.: An integrated theory of list memory. J. Mem. Lang. 38(4), 341–380 (1998)
Salvucci, D.D.: Modeling driver behavior in a cognitive architecture. Hum. Factors: J. Hum. Factors Ergon. Soc. 48(2), 362–380 (2006)
Anderson, J.R.: Problem solving and learning. Am. Psychol. 48(1), 35 (1993)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature (outside the USA)
About this paper
Cite this paper
Stevens, C.A., Fisher, C.R., Morris, M.B., Myers, C., Spriggs, S., Dukes, A. (2019). Cognitive Metrics Profiling: A Model-Driven Approach to Predicting and Classifying Workload. In: Cassenti, D. (eds) Advances in Human Factors in Simulation and Modeling. AHFE 2018. Advances in Intelligent Systems and Computing, vol 780. Springer, Cham. https://doi.org/10.1007/978-3-319-94223-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-94223-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94222-3
Online ISBN: 978-3-319-94223-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)