When performing tasks in complex, dynamic domains individuals must consider information regarding both high-level constraints (relationships among several variables, performance goals) and low-level data (the values of individual variables). Previous research has revealed mixed results concerning the effectiveness of configural displays in achieving these dual design goals. Two empirical studies were conducted to investigate these issues using a laboratory analogue of a complex, dynamic task modeled on a real-world domain. Performance with a configural display, which highlighted the low-level data, was compared with performance with a bar graph display. For the extraction of information about high-level constraints in a memory probe task, the configural display significantly increased accuracy with no cost in latency. For low-level data there were no differences in accuracy across the two display conditions, but there was a significant cost in latency with the configural display. However, this cost was dependent on both experience and system state. These results suggest that configural displays can be designed to support the extraction of both high-level constraints and low-level data in complex, dynamic domains. To support the extraction of information for high-level constraints, the emergent features produced by a configural display must reflect the critical data relationships that are present in the domain. To support the extraction of low-level data, the graphical elements of the display must be made more salient perceptually through a variety of techniques, including emphasis of scale, spatial separation, and color-coding.
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References
1.
Barnett, B. J., and Wickens, C. D. (1988). Display proximity in multicue information integration: The benefits of boxes. Human Factors, 30, 15–24.
2.
Bennett, K. B., and Flach, J. M. (1992). Graphical displays: Implications for divided attention, focused attention, and problem solving. Human Factors, 34, 513–533.
3.
Bennett, K. B., Woods, D. D., Roth, E. M., and Haley, P. H. (1986). Predictor displays for complex, dynamic tasks: A preliminary investigation. In Proceedings of the Human Factors Society 30th Annual Meeting (pp. 684–688). Santa Monica, CA: Human Factors Society.
4.
Brown, R. L. (1985). Methods for graphic representation of systems of simulated data. Ergonomics, 28, 1439–1454.
5.
Buttigieg, M. A. (1989). Emergent features in visual display design for two types of failure detection tasks (Tech. Report EPRL-89-04). Urbana-Champaign: University of Illinois, Engineering Psychology Research Laboratory and Aviation Research Laboratory.
6.
Carswell, C. M. (1988). Integral, configural, and unitary graphs. In Proceedings of the Human Factors Society 32nd Annual Meeting (pp. 1345–1349). Santa Monica, CA: Human Factors Society.
7.
Carswell, C. M., and Wickens, C. D. (1987). Information integration and the object display. Ergonomics, 30, 511–527.
8.
Casey, E. J., and Wickens, C. D. (1986). Visual display representation of multidimensional systems: The effect of information correlation and display integrality (Tech. Report CPL-86-2). Urbana-Champaign: University of Illinois, Cognitive Psychophysiology Laboratory.
9.
Chernoff, H. (1973). The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association, 68, 361–368.
10.
Cleveland, W. S. (1985). The elements of graphing data.Belmont, CA: Wadsworth.
11.
Coury, B. G., Boulette, M. D., and Smith, R. A. (1989). Effect of uncertainty and diagnosticity on classification of multidimensional data with integral and separable displays of system status. Human Factors, 31(5), 551–570.
12.
Crossman, E. R. F. W., and Cooke, J. E. (1974). Manual control of slow-response systems. In E. Edwards and F. P. Lees (Eds.), The human operator in process control (pp. 51–66). London: Taylor & Francis.
13.
Dolan, N. J., Elvers, G. C., and Schmidt, J. (1991). Measurement scale and zero point indicators affect object display separate and integral task performance. In Proceedings of the Human Factors Society 35th Annual Meeting (pp. 1589–1592). Santa Monica, CA: Human Factors Society.
14.
Flach, J. M., and Vicente, K. J. (1989). Complexity, difficulty, direct manipulation and direct perception (Tech. Report EPRL-89-03). Urbana-Champaign: University of Illinois, Engineering Psychology Research Laboratory.
15.
Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton-Mifflin.
16.
Goldsmith, T. E., and Schvaneveldt, R. W. (1984). Facilitating multiple-cue judgments with integral information displays. In J. C. Thomas and M. L. Schneider (Eds.), Human factors in computer systems (pp. 243–270). Norwood, NJ: Erlbaum.
17.
Haley, P. H., and Woods, D. D. (1988). Methods and apparatus for dynamic systems control. Patent Number 4,770,841, Patent Date September 13, 1988.
18.
Hansen, J. P. (in press). Representation of system invariants by optical invariants in configural displays in process control. In J. M. Flach, P. A. Hancock, J. K. Caird, and K. J. Vicente (Eds.). An ecological approach to human-machine systems: II. Local applications.Hillsdale, NJ: Erlbaum.
19.
Hollnagel, E., and Woods, D. D. (1983). Cognitive systems engineering: New wine in new bottles. International Journal of Man-Machine Studies, 18, 583–600.
20.
Kahneman, D., and Treisman, A. (1984). Changing views of attention and automaticity. In R. Parasuraman and D. R. Davies (Eds.), Varieties of attention (pp. 29–61). New York: Academic.
21.
Kleiner, B., and Hartigan, J. A. (1981). Representing points in many dimensions by trees and castles. Journal of the American Statistical Association, 76, 260–269.
22.
MacGregor, D., and Slovic, P. (1986). Graphic representation of judgmental information. Human-Computer Interaction, 2, 179–200.
23.
Moray, N. (1987). Intelligent aids, mental models, and the theory of machines. International Journal of Man-Machines Studies, 27, 619–629.
24.
Moray, N., Lootsteen, P., and Pajak, J. (1986). Acquisition of process control skills. IEEE Transactions on Systems, Man, and Cybernetics, SMC-16, 497–504.
25.
Naveh-Benjamin, M., and Pachella, R. G. (1982). The effect of complexity on interpreting “Chernoff” faces. Human Factors, 24, 11–18.
26.
Norman, D. A. (1986). Cognitive engineering. In D. A. Norman and S. W. Draper (Eds.), User-centered system design (pp. 31–61). Hillsdale, NJ: Erlbaum.
27.
Pomerantz, J. R. (1986). Visual form perception: An over-view. In H. C. Nusbaum and E. C. Schwab (Eds.), Pattern recognition by humans and machines: Vol. 2. Visual perception (pp. 1–30). Orlando, FL: Academic.
28.
Rasmussen, J. (1986). Information processing and human-machine interaction: An approach to cognitive engineering.New York: North-Holland.
29.
Roth, E. M., and Woods, D. D. (1988). Aiding human performance: I. Cognitive analysis. Le Travail Humain, 51(1), 39–64.
30.
Sanderson, P. M., Flach, J. M., Buttigieg, M. A., and Casey, E. J. (1989). Object displays do not always support better integrated task performance. Human Factors, 31, 183–198.
31.
Treisman, A. M., and Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12, 97–136.
32.
Tufte, E. R. (1983). The visual display of quantitative information.Cheshire, CT: Graphics.
33.
Vicente, K. J., and Rasmussen, J. (1990). The ecology of human-machine systems: II. Mediating “direct perception” in complex work domains. Ecological Psychology, 2(3), 207–249.
34.
Wickens, C. D., and Andre, A. D. (1988). Proximity compatibility and the object display. In Proceedings of the Human Factors Society 32nd Annual Meeting (pp. 1335–1339). Santa Monica, CA: Human Factors Society.
35.
Wickens, C. D., and Andre, A. D. (1990). Proximity compatibility and information display: Effects of color, space, and objectness on information integration. Human Factors, 32, 61–78.
36.
Wickens, C. D., Kramer, A., Barnett, B., Carswell, M., Fracker, L., Goettl, B., and Harwood, K. (1985). Display-cognitive interface: The effect of information integration requirements on display formatting for C3 displays (Tech. Report EPL-85-3). Urbana-Champaign: University of Illinois, Engineering Psychology Research Laboratory and Aviation Research Laboratory.
37.
Woods, D. D. (1991a). The cognitive engineering of problem representations. In G. R. S. Weir and J. L. Alty (Eds.), Human-computer interaction and complex systems (pp. 169–188). London: Academic.
38.
Woods, D. D. (1991b). Representation aiding: A ten-year retrospective. In Proceedings of the 1991 IEEE International Conference on Systems, Man, and Cybernetics (pp. 1173–1176). Charlottesville, VA: IEEE.
39.
Woods, D. D., and Hollnagel, E. (1987). Mapping cognitive demands in complex problem-solving worlds. International Journal of Man-Machine Studies, 26, 257–275.
40.
Woods, D. D., and Roth, E. M. (1988a). Aiding human performance: II. From cognitive analysis to support systems. Le Travail Humain, 51(2), 139–171.
41.
Woods, D. D., and Roth, E. M. (1988b). Cognitive systems engineering. In M. Helander (Ed.), Handbook of human-computer interaction (pp. 1–41). Amsterdam: Elsevier.
42.
Woods, D. D., Wise, J. A., and Hanes, L. F. (1981). An evaluation of nuclear power plant safety parameter display systems. In Proceedings of the Human Factors Society 25th Annual Meeting (pp. 110–114). Santa Monica, CA: Human Factors Society.
43.
Zachary, W. (1986). A cognitively based functional taxonomy of decision support techniques. Human-Computer Interaction, 2, 25–63.
