Abstract
This study validated a physiological workload tool during an investigation of information integration impacts on performance of a NASA electronic procedures task. It was hypothesized that as the level of integration of system and procedural information increased, situation awareness (SA) and usability would increase, and cognitive workload would decrease. To allow quantitative, continuous, and objective assessment of cognitive workload, Charles River Analytics designed a system for Cognitive Assessment and Prediction to Promote Individualized Capability Augmentation and Reduce Decrement (CAPT PICARD). This real-time workload tool was validated against the Bedford workload rating scale in this NASA operational study. The overall results for SA and cognitive workload were mixed; however, the CAPT PICARD workload and eye tracking measures (both real-time physiological measures), showed congruous results. The value of workload assessment during developmental testing of a new system is that it allows for early identification of features and designs that result in high workload. When issues are identified early, redesigns are more feasible and less costly. Real-time workload data could provide the inputs needed to drive future adaptive displays, (e.g., if an astronaut is experiencing high cognitive workload, this data could cue an option for simplified displays). A future vision for real-time, unobtrusive measurement of workload is also to use it in an operational spaceflight environment. Therefore, it is critically important to continue to test and mature tools for unobtrusive measures of human performance like CAPT PICARD.
E. Vincent Cross II—Formerly Leidos
M. Greene—Formerly KBRwyle
J. Lancaster—Formerly Honeywell
B. Munson—Formerly GeoLogics.
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References
Kerick, S.E., Oie, K.S., McDowell, K.: Assessment of EEG signal quality in motion environments. Army Research Lab Aberdeen Proving Ground MD Human Research and Engineering Directorate (2009)
Boas, D.A., Elwell, C.E., Ferrari, M., Taga, G.: Twenty years of functional near-infrared spectroscopy: introduction for the special issue. NeuroImage 85, 1–5 (2014). https://doi.org/10.1016/j.neuroimage.2013.11.033
Ferrari, M., Quaresima, V.: A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. NeuroImage 63(2), 921–935 (2012). https://doi.org/10.1016/j.neuroimage.2012.03.049
Ayaz, H., Shewokis, P.A., Bunce, S., Izzetoglu, K., Willems, B., Onaral, B.: Optical brain monitoring for operator training and mental workload assessment. NeuroImage 59(1), 36–47 (2012). https://doi.org/10.1016/j.neuroimage.2011.06.023
Bunce, S.C., et al.: Implementation of fNIRS for monitoring levels of expertise and mental workload. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) Foundations of Augmented Cognition. Directing the Future of Adaptive Systems, vol. 6780, pp. 13–22. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21852-1_2
Bracken, B.K., Elkin-Frankston, S., Palmon, N., Farry, M., de B Frederick, B.: A system to monitor cognitive workload in naturalistic high-motion environments (2017)
Bracken, B.K., Palmon, N., Elkin-Frankston, S., Silva, F.: Portable, Durable, Rugged, Functional Near-Infrared Spectroscopy (fNIRS) Sensor, February 2018
Molavi, B., Dumont, G.A.: Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiol. Meas. 33(2), 259 (2012)
Metz, A.J., Wolf, M., Achermann, P., Scholkmann, F.: A new approach for automatic removal of movement artifacts in near-infrared spectroscopy time series by means of acceleration data. Algorithms 8(4), 1052–1075 (2015)
Scholkmann, F., Spichtig, S., Muehlemann, T., Wolf, M.: How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation. Physiol. Meas. 31(5), 649–662 (2010). https://doi.org/10.1088/0967-3334/31/5/004
Cao, A., Chintamani, K.K., Pandya, A.K., Ellis, R.D.: NASA TLX: software for assessing subjective mental workload. Behav. Res. Methods 41(1), 113–117 (2009)
Murphy, K.P.: Dynamic Bayesian Networks: Representation, Inference and Learning (Dissertation). University of California, Berkeley (2002). https://ibug.doc.ic.ac.uk/media/uploads/documents/courses/DBN-PhDthesis-LongTutorail-Murphy.pdf
Cox, Z., Pfautz, J.: Causal influence models: a method for simplifying construction of bayesian networks. Charles River Analytics Inc. (2007)
Pélegrin, C.: The never-ending story of proceduralization in aviation. In: Trapping Safety into Rules, pp. 31–44. CRC Press, Boca Raton (2017)
Palmer, E., Degani, A.: Electronic checklists: evaluation of two levels of automation. In: Proceedings of the Sixth Symposium on Aviation Psychology, pp. 178–183. The Ohio State University Columbus (1991)
Myers III, P.L.: Commercial aircraft electronic checklists: benefits and challenges (literature review). Int. J. Aviat. Aeronaut. Aerosp. 3(1), 1 (2016)
de Brito, G., Boy, G.: Situation awareness and procedure following. In: CSAPC, vol. 99, pp. 9–14 (1999)
Barnett, B.J., Wickens, C.D.: Display proximity in multicue information integration: the benefits of boxes. Hum. Factors 30(1), 15–24 (1988)
Garner, W.R.: The stimulus in information processing. In: Moskowitz, H.R., Scharf, B., Stevens, J.C. (eds.) Sensation and Measurement, pp. 77–90. Springer, Dordrecht (1974). https://doi.org/10.1007/978-94-010-2245-3_7
Schreckenghost, D., Milam, T., Billman, D.: Human performance with procedure automation to manage spacecraft systems. In: 2014 IEEE Aerospace Conference, pp. 1–16. IEEE (2014)
Durso, F.T., Dattel, A.R., Banbury, S., Tremblay, S.: SPAM: the real-time assessment of SA. A Cogn. Approach Situat. Aware. Theory Appl. 1, 137–154 (2004)
Endsley, M.R.: Measurement of situation awareness in dynamic systems. Hum. Factors 37(1), 65–84 (1995)
Ericsson, K.A., Hoffman, R.R., Kozbelt, A., Williams, A.M.: The Cambridge Handbook of Expertise and Expert Performance. Cambridge University Press, Cambridge (2018)
Kirchner, W.K.: Age differences in short-term retention of rapidly changing information. J. Exp. Psychol. 55(4), 352 (1958)
Moustafa, K., Luz, S., Longo, L.: Assessment of mental workload: a comparison of machine learning methods and subjective assessment techniques. In: Longo, L., Leva, M. (eds.) H-WORKLOAD 2017. CCIS, vol. 726, pp. 30–50. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61061-0_3
Vidulich, M.A., Tsang, P.S.: The confluence of situation awareness and mental workload for adaptable human–machine systems. J. Cogn. Eng. Decis. Making 9(1), 95–97 (2015)
Muñoz-de-Escalona, E., Cañas, J.J.: Latency differences between mental workload measures in detecting workload changes. In: Longo, L., Leva, M. (eds.) H-WORKLOAD 2018. CCIS, vol. 1012, pp. 131–146. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-14273-5_8
Herff, C., Heger, D., Fortmann, O., Hennrich, J., Putze, F., Schultz, T.: Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS. Front. Hum. Neurosci. 7 (2014). http://doi.org/10.3389/fnhum.2013.00935
Acknowledgements
This work was funded by the NASA Small Business Innovation and Research funding (contract numbers NNX15CJ17P and NNX16CJ08C) and the NASA Human Research Program (contract number NNJ15HK11B). The study, under the direction of Kritina Holden, was performed at the NASA Johnson Space Center. The authors would like to thank Debra Schreckenghost, Christopher Hamblin, Lee Morin, and Camille Peres for their technical contributions. A special thanks to Patrick Laport, who developed the procedures and simulation software for the study. Finally, the authors would like to thank Lee Morin for use of the Crew Interface Rapid Prototyping Laboratory at the NASA Johnson Space Center.
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Bracken, B. et al. (2019). Validation of a Physiological Approach to Measure Cognitive Workload: CAPT PICARD. In: Longo, L., Leva, M. (eds) Human Mental Workload: Models and Applications. H-WORKLOAD 2019. Communications in Computer and Information Science, vol 1107. Springer, Cham. https://doi.org/10.1007/978-3-030-32423-0_5
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