The present study was designed to determine whether a biocybernetic, adaptive system could enhance vigilance performance. Participants were asked to monitor the repetitive presentation of white bars on a computer screen for occasional increases in length. An index of task engagement was derived from participants' electroencephalographic (EEG) activity and was used to change the presentation rate of events among 3 values (6, 20, and 60 events/min). Under a negative feedback contingency, event rates increased if the engagement index decreased and, conversely, decreased if the index increased. Under positive feedback, the opposite contingency existed. Each experimental participant had a yoked control partner who received the same pattern of changes in event rates irrespective of his or her EEG activity. The results showed that better vigilance performance was obtained under negative feedback and that the performance of the yoked participants was similar to that of their experimental partners. These findings suggest that it may be possible to improve monitoring performance on critical activities such as air traffic control and radar and sonar operation through a pattern of event rate changes that do not rely on an operator's overt behavior.
Get full access to this article
View all access options for this article.
References
1.
Byrne, E. A., & Parasuraman, R. (1996). Psychophysiology and adaptive automation. Biological Psychology, 42, 249–268.
2.
Davies, D. R., & Parasuraman, R. (1982). The psychology of vigilance.London: Academic.
3.
Dember, W. N., & Warm, J. S. (1979). Psychology of perception (2nd ed.). New York: Holt, Rinehart, & Winston.
4.
Donaldson, W. (1992). Measuring recognition memory. Journal of Experimental Psychology: General, 121, 275–277.
5.
Freeman, F.Mikulka, P., Prinzel, L., & Scerbo, M. (1999). Evaluation of an adaptive automation system using three indices with a visual tracking task. Biological Psychology, 50, 61–76.
6.
Freeman, F.Mikulka, P., Scerbo, M., Prinzel, L., & Clouatre, K. (2000). Evaluation of a psychophysiologically controlled adaptive automation system, using performance on a tracking task. Applied Psychophysiology and Biofeedback, 25, 103–115.
7.
Galinsky, T. L., Dember, W. N., & Warm, J. S. (1989, April). Effects of event rate on subjective workload in vigilance performance. Paper presented at the 81st Meeting of the Southern Society for Philosophy and Psychology, New Orleans, LA.
8.
Gomer, F. E. (1981). Physiological monitoring and the concept of adaptive systems. In J. Moraal & K. F. Kraiss (Eds.), Manned systems design: Methods, equipment, and applications (pp. 271–287). New York: Plenum.
9.
Green, D. M., & Swets, J. A. (1974). Signal detection theory and psychophysics.Huntington, NY: Krieger.
10.
Grier, J. B. (1971). Non-parametric indices for sensitivity and bias: Computing formulas. Psychological Bulletin, 75, 424–429.
11.
Grubb, P. L., Miller, L. C., Nelson, W. T., Warm, J. S., Dember, W. N., & Davies, D. R. (1994). Cognitive failure and perceived workload In vigilance performance. In M. Mouloua & R. Parasuraman (Eds.), Human performance in automated systems: Current research and trends (pp. 115–121). Hillsdale, NJ: Erlbaum.
12.
Hadley, G.Mikulka, P., Freeman, F., Scerbo, M., & Prinzel, L. (1997). An examination of system sensitivity in a biocybernetic adaptive system. In Proceedings of the 9th Symposium on Aviation Psychology (pp. 305–309). Columbus: Ohio State University.
13.
Hancock, P. A., Chignell, M. H., & Lowenthal, A. (1985). An adaptive human-machine system. In Proceedings of the Institute of Electrical and Electronics Engineers Conference on Systems, Man, and Cybernetics (pp. 627–629). Washington, DC: IEEE.
14.
Inagaki, T.Takae, Y., & Moray, N. (1999). Automation and human-interface for takeoff safety. In Proceedings of the 10th International Symposium on Aviation Psychology (pp. 402–407). Columbus: Ohio State University.
15.
Jasper, H. H. (1958). Report of the Committee on methods of critical examination in electroencephalography. Electroencephalography and Clinical Neurophysiology, 10, 370–375.
16.
Jerison, H. J., & Pickett, R. M. (1964). Vigilance: The importance of the elicited observing rate. Science, 143, 970–971.
17.
Kramer, A. F., Trejo, L. J., & Humphrey, D. G. (1996). Psycho-physiological measures of workload: Potential applications to adaptively automated systems. In R. Parasuraman & M. Mouloua (Eds.), Automation and human performance: Theory and applications (pp. 137–162). Mahwah, NJ: Erlbaum.
18.
Krulewitz, J. E., Warm, J. S., & Wohl, T. H. (1975). Effects of shifts in the rate of repetitive stimulation on sustained attention. Perception and Pyschophysics, 18, 245–249.
19.
Lubar, J. F. (1991). Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders. Biofeedback and Self-Regulation, 16, 201–225.
20.
Lubar, J. F., Swartwood, M. O., Swartwood, J. N., & O'Donnell, P. N. (1995). Evaluation of the effectiveness of EEG neurofeedback training for ADHD in a clinical setting as measured by changes in T.O.V.A. scores, behavioral ratings, and WISC-R performance. Biofeedback and Self-Regulation, 20, 83–99.
21.
Mackworth, N. (1948). The breakdown of vigilance in prolonged search. Quarterly Journal of Experimental Psychology, 1, 6–21.
22.
Matheson, D. W., Bruce, R. L., & Beauchamp, K. L. (1974). Introduction to experimental psychology.New York: Holt, Rinehart & Winston.
23.
Metzger, K. R., Warm, J. S., & Senter, R. J. (1974). Effects of background event rate and critical signal amplitude on vigilance performance. Perceptual and Motor Skills, 38, 1175–1181.
24.
Moray, N.Inagaki, I., & Itoh, M. (2000). Adaptive automation, trust, and self-confidence in fault management of time-critical tasks. Journal of Experimental Psychology: Applied, 6, 44–58.
25.
O'Hanlon, J. F., & Beatty, J. (1977). Concurrence of electroen-cephalographic and performance changes during a simulated radar watch and some implications for the arousal theory of vigilance. In R. R. Mackie (Eds.), Vigilance: Theory, operational performance and physiological correlates (pp. 189–201). New York: Plenum.
26.
Parasuraman, R. (2000). Designing automation for human use: Empirical studies and quantitative methods. Ergonomics, 43, 931–951.
27.
Parasuraman, R.Bahri, T., Deaton, J. E., Morrison, J. G., & Barnes, M. (1992). Theory and design of adaptive automation in aviation systems (Progress Report No. NAWCADWAR-92033-60). Warminster, PA: Naval Air Warfare Center, Aircraft Division.
28.
Parasuraman, R. & Davies, D. R. (1976). Decision theory analysis of response latencies in vigilance. Journal of Experimental Psychology: Human Perception and Performance, 2, 578–590.
29.
Parasuraman, R.Mouloua, M., Molloy, R., & Hilburn, B. (1996). Monitoring of automated systems. In R. Parasuraman & M. Mouloua (Eds.), Automation and human performance: Theory and applications (pp. 91–115). Mahwah, NJ: Erlbaum.
30.
Parasuraman, R.Warm, J. S., & Dember, W. N. (1987). Vigilance: Taxonomy and utility. In L. S. Mark, J. S. Warm, & R. L. Huston (Eds.), Ergonomics and human factors: Recent research (pp. 11–32). New York: Springer-Verlag.
31.
Pope, A.Bogart, E., & Bartolome, D. (1995). Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology, 40, 187–195.
32.
Prinzel, L. J. (1998). A psychophysiological assessment of the efficacy of event-related potentials and electroencephalogram for adaptive task allocation. Unpublished doctoral dissertation, Old Dominion University, Norfolk, VA.
33.
Prinzel, L. J., Freeman, F. G., Scerbo, M. W., Mikulka, P. J., & Pope, A. T. (2000). A closed-loop system for examining psy-chophysiological measures for adaptive automation. International Journal of Aviation Psychology, 10, 393–410.
34.
Scallen, S. F., Hancock, P. A., & Duley, J. A. (1995). Pilot performance and preference for short cycles of automation in adaptive function allocation. Applied Ergonomics, 26, 397–403.
35.
Scerbo, M. W. (1996). Theoretical perspectives on adaptive automation. In R. Parasuraman & M. Mouloua (Eds.), Automation and human performance: Theory and applications (pp. 37–63). Mahwah, NJ: Erlbaum.
36.
Scerbo, M. W. (1998). Fifty years of vigilance research: Where are we now? In Proceedings of the Human Factors and Ergonomics Society 42nd Annual Meeting (p. 1575). Santa Monica, CA: Human Factors and Ergonomics Society.
37.
See, J. E., Howe, S. R., Warm, J. S., & Dember, W. N. (1995). Meta-analysis of the sensitivity decrement in vigilance. Psychological Bulletin, 117, 230–249.
38.
Streitberg, B.Röhmel, J., Herrmann, W. M., & Kubicki, S. (1987). COMSTAT rule for vigilance classification based on spontaneous EEG activity. Neuropsychobiology, 17, 105–117.
39.
Wa rm, J. S., & Jerison, H. J. (1984). The psychophysics of vigilance. In J. S. Warm (Eds.), Sustained attention in human performance (pp. 15–19). Chichester, England: Wiley.
40.
Wiener, E. L. (1973). Adaptive measurement of vigilance decrement. Ergonomics, 16, 353–363.
41.
Woods, D. D. (1996). Decomposing automation: Apparent simplicity, real complexity. In R. Parasuraman & M. Mouloua (Eds.), Automation and human performance: Theory and applications (pp. 3–17). Mahwah, NJ: Erlbaum.
