Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-24T16:17:28.061Z Has data issue: false hasContentIssue false
  • English
  • Français

Mental Workload and Visual Impairment: Differences between Pupil, Blink, and Subjective Rating

Published online by Cambridge University Press:  10 April 2014

Miguel Ángel Recarte ,
Elisa Pérez ,
Ángela Conchillo  and
Luis Miguel Nunes
Miguel Ángel Recarte*
Affiliation:
Universidad Complutense (Spain)
Elisa Pérez
Affiliation:
Universidad Complutense (Spain)
Ángela Conchillo
Affiliation:
Universidad Complutense (Spain)
Luis Miguel Nunes
Affiliation:
Dirección General de Tráfico (Spain)
*
Correspondence concerning this article should be adressed to Miguel Angel Recarte, Facultad de Psicología, Universidad Complutense de Madrid, Campus de Somosaguas, 28223-Madrid, Spain. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This research has two aims: (a) To study the concurrent validity of three measures of mental workload, NASA TLX rating scale, pupil dilation and blink rate, testing the hypothesis that they will provide convergent results using a single-task, and dissociative results for dual-task; and (b) To analyse their capability to predict visual search impairment. These three measures were analyzed in the same cognitive tasks in single-task and dual-task (cognitive task and visual search) conditions in a within-subjects experiment with twenty-nine participants. Mental workload measures showed concurrent validity under single-task condition, but a complex pattern of results arose in the dual-task condition: it is suggested that NASA TLX would be a subjective addition of the rating of each task; pupil dilation would measure the average arousal underlying the cognitive tasks; and the blink rate would produce opposite effects: whereas mental workload of cognitive tasks would increase blink rate, visual demand would inhibit it. All three measures were good predictors of visual impairment. The soundness of these measures is discussed with regard to the applied field of driving and other activities.

Este experimento tiene dos objetivos: 1) Estudiar la validez concurrente de tres medidas de carga mental, la escala de juicios NASA TLX, la dilatación de la pupila y la tasa de parpadeo, poniendo a prueba la hipótesis de que, en situaciones de tarea única, arrojan resultados convergentes, pero, en doble tarea, arrojan resultados disociativos. 2) Analizar su capacidad para predecir el deterioro en la búsqueda visual. Las tres medidas fueron analizadas con las mismas tareas cognitivas realizadas en condiciones de tarea simple y de doble tarea (tarea cognitiva y búsqueda visual) en un experimento intrasujetos con veintinueve participantes. Las medidas de carga mental mostraron validez concurrente en las condiciones de tarea única, pero en las condiciones de doble tarea apareció un patrón de resultados complejo que sugiere que NASA TLX consistiría en la adición subjetiva de los juicios de cada tarea; la dilatación de la pupila mediría la activación promedio que subyace a las tareas cognitivas; y la tasa de parpadeo produciría efectos contrapuestos: mientras que la carga mental de las tareas cognitivas incrementa la tasa de parpadeo, las demandas visuales la inhiben. Las tres medidas fueros buenos predictores del deterioro visual. Se discute la justificación del uso de estas medidas en el campo aplicado de la conducción y otras actividades.

Keywords

mental workloadpupilblinkNASA TLX rating scaledistraction in drivingcarga mentalpupilaparpadeoescala NASA TLXdistracción al conducir
Type
Articles
Information
The Spanish Journal of Psychology , Volume 11 , Issue 2 , November 2008 , pp. 374 - 385
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Beatty, J. (1982). Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychological Bulletin, 91, 276–292.CrossRefGoogle ScholarPubMed
De Waard, D. (1996). The measurement of drivers' mental workload. Unpublished doctoral dissertation, University of Groningen, Traffic Research Centre.Haren, The NetherlandsGoogle Scholar
De Waard, D. (2002). Mental workload. In Fuller, R. & Santos, J. A. (Eds.), Human factors for highway engineers (pp. 161–176). Oxford, Pergamon.Google Scholar
De Waard, D., & Brookhuis, K. A. (1997). On the measurement of driver mental workload. In Rothengatter, J. A. & Carbonell, E. (Eds.), Traffic and transport psychology (pp. 161–173). Amsterdam: Elsevier.Google Scholar
Fogarty, Ch., & Stern, J. A. (1989). Eye movements and blinks: Their relationship to higher cognitive processes. International Journal of Psychophysiology, 8, 35–42.CrossRefGoogle ScholarPubMed
Granholm, E., & Steinhauer, S. R. (2004). Introduction: Pupillometric measures of cognitive and emotional processing. International Journal of Psychophysiology, 52, 1–6.CrossRefGoogle Scholar
Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In Hancock, P.A. & Meshkati, N. (Eds.), Human mental workload (pp. 139–183). Amsterdam: North-Holland.CrossRefGoogle Scholar
HASTE. (2002). Human Machine Interface and the Safety of Traffic in Europe. Project GRD1/2000/25361 S12.319626.Google Scholar
Hoecks, B., & Levelt, W. J. M. (1993). Pupillary dilatation as a measure of attention: A quantitative system analysis. Behavior Research Methods, Instruments, & Computers, 25, 16–26.CrossRefGoogle Scholar
Iqbal, S. T., Zheng, X. S., & Bailey, B. P. (2004). Task-evoked pupillary response to mental workload in human-computer interaction (pp. 1477–1480). In CHI'04. New York: AMC Press.CrossRefGoogle Scholar
Janisse, M. P. (1977). Pupillometry: The psychology of the pupillary response. New York: Wiley.Google Scholar
Just, M. A., Carpenter, P., Keller, T., Emery, L., Zajac, H., & Thulborn, K. (2001). Interdependence of nonoverlapping cortical systems in dual cognitive tasks. NeuroImage 14, 417–426.CrossRefGoogle ScholarPubMed
Kahneman, D. (1973). Attention and effort. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Kubose, T., Bock, K., Dell, G. S., Garnsey, S. M, Kramer, A. F., & Mayhugh, J. (2006). The effects of speech production and speech comprehension on simulated driving. Performance Applied Cognitive Psychology, 20(1), 43–63.CrossRefGoogle Scholar
Lavie, N. (2006). The role of perceptual load in visual awareness. Brain Research, 1080, 91–100.Google Scholar
Mack, A., & Rock, I. (1998). Inattentional blindness. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Most, S. B., Scholl, B. J., Clifford, E. R., & Simons, D. J. (2005). What you see is what you set: Sustained inattentional blindness and the capture of awareness. Psychological Review, 112(1) 217–242.CrossRefGoogle ScholarPubMed
Näätänen, R. (1992). Attention and brain function. Hillsdale, NJ: Erlbaum.Google Scholar
Nunes, L. M., & Recarte, M. A. (2004). Speed, traffic complexity, and visual performance: A study on open road. In Underwood, G. (Ed.), Traffic and transport psychology: Theory and application (pp. 339–354). Amsterdam: Elsevier.Google Scholar
O'Donnell, R. D., & Eggemeier, F. T. (1986). Workload assessment methodology. In Boff, K.R., Kaufman, L., & Thomas, J.P. (Eds.), Handbook of perception and human performance: Vol. 2. Cognitive processes and performance (pp. 42/1–42/49). New York: Wiley.Google Scholar
Recarte, M. A., & Nunes, L. M. (2000). Effects of verbal and spatial-imagery task on eye fixations while driving. Journal of Experimental Psychology: Applied, 6, 31–43.Google ScholarPubMed
Recarte, M. A., & Nunes, L. M. (2002a). Parpadeo durante la conducción: efectos de la carga mental y del tiempo conduciendo. Vigilia-Sueño, 14 ( Supl.), 161–167.Google Scholar
Recarte, M. A., & Nunes, L. M. (2002b). Mental load and loss of control over speed in real driving: Towards a theory of attentional speed control. Transportation Research, Part F 5, 111–122.Google Scholar
Recarte, M. A., & Nunes, L. M. (2003). Mental workload while driving: Effects on visual search, discrimination, and decision making. Journal of Experimental Psychology: Applied, 9, 119–137.Google ScholarPubMed
Rubio, S., Díaz, E., Martín, J., & Puente, J. M. (2004). Evaluation of subjective mental workload: A comparison of SWAT, NASA-TLX and workload profile methods. International Review of Applied Psychology, 53, 61–86.CrossRefGoogle Scholar
Ryu, K., & Myung, R. (2005) Evaluation of mental workload with a combined measure based on physiological indices during a dual task of tracking and mental arithmetic. International Journal of Industrial Ergonomics, 35(11), 991–1009.CrossRefGoogle Scholar
Steinhauer, S. R., Siegle, G. J., Condray, R., & Pless, M. (2004). Sympathetic and parasympathetic innervation of pupillary dilation during sustained processing. International Journal of Psychophysiology, 52, 77–86.CrossRefGoogle ScholarPubMed
Stern, J. A., Boyer, D., & Schroeder, D. (1994). Blink rate: A possible measure of fatigue. Human Factors, 36, 285–297.CrossRefGoogle Scholar
Siveraag, E. J., & Stern, J. A. (2000). Ocular measures of fatigue and cognitive factors. In Backs, R. W. & Boucsein, W. (Eds.), Engineering psychophysiology: Issues and applications (pp. 269–287). Mahwah, NJ: Erlbaum.Google Scholar
Vicente, K. J., Thornton, D. C., & Moray, N. (1987). Spectral analysis of sinus arrhythmia: A measure of mental effort. Human Factors, 29, 171–182.CrossRefGoogle ScholarPubMed
Wickens, C. D. (1992). Engineering psychology and human performance. New York: HarperCollins.Google Scholar