Visual complexity is conventionally defined as the level of detail or intricacy contained within an image. This paper evaluates different measures of complexity and the extent to which they may be compromised by a familiarity bias. It considers the implications with reference to measures of visual complexity based on users’ subjective judgments and explores other metrics which may provide a better basis for evaluating visual complexity in icons and displays. The interaction between shading and complexity is considered as a future direction for the empirical study of visual complexity.


Icons Visual complexity Familiarity Metrics 


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  1. 1.
    Snodgrass, J.G., Vanderwart, M.: A standardized set of 260 pictures. Norms for name agreement, image agreement, familiarity and visual complexity. Journal of Experimental psychology. Human Learning & Memory 6, 174–215 (1980)CrossRefGoogle Scholar
  2. 2.
    McDougall, S.J.P., Bruijn, D.O., Curry, M.B.: Measuring symbol and icon characteristics: Norms for concreteness, complexity, meaningfulness, familiarity and semantic distance for 239 symbols. Behavior Research Methods 31(3), 487–519 (1999)CrossRefGoogle Scholar
  3. 3.
    Forsythe, A., Sheehy, N., Sawey, M.: Measuring icon complexity: an automated analysis. Behavior Research Methods, Instruments, and Computers 35, 334–342 (2003a)CrossRefGoogle Scholar
  4. 4.
    McDougall, S.J.P., Bruijn de, O., Curry, M.B.: Exploring the affects of picture characteristics on user performance: The role of picture concreteness, complexity and distinctiveness. Journal of Experimental Psychology: Applied 6, 291–306 (2000)Google Scholar
  5. 5.
    Arend, U., Muthig, K.P., Wandmacher, J.: Evidence for global feature superiority in menu selection by pictures. Behavior and Information Technology 6, 411–426 (1987)CrossRefGoogle Scholar
  6. 6.
    Bryne, M.D.: Using pictures to find documents: Simplicity is critical. In: Proceedings of the conference on Human Factors in Computing systems, INTERCHI 1993. Addison-Wesley, Reading (1993)Google Scholar
  7. 7.
    Brunel, N., Ninio, J.: Time to detect the difference between two images presented side by side. Cognitive Brain Research 5, 273–282 (1997)CrossRefGoogle Scholar
  8. 8.
    Rossion, B., Pourtois, G.: Revisiting Snodgrass and Vanderwart’s object set: The role of surface detail in basic-level object recognition. Perception 33, 217–236 (2004)CrossRefGoogle Scholar
  9. 9.
    Feldman, J.: How surprising is a simple pattern? Quantifying “Eureka!”. Cognition 93, 199–224 (2004)CrossRefGoogle Scholar
  10. 10.
    Garcia, M., Badre, A.N., Stasko, J.T.: Development and validation of icons varying in their abstractness. Interacting with Computers 6(2), 191–211 (1994)CrossRefGoogle Scholar
  11. 11.
    Bruner, J.S.: Beyond the Information Given: Studies in the Psychology of Knowing. Norton, London (1973)Google Scholar
  12. 12.
    Hochberg, J.E.: Perception, 2nd edn. Prentice-Hall, Englewood Cliffs (1986)Google Scholar
  13. 13.
    Koffka, K.: Principles of Gestalt Psychology. Lund Humphries, London (1935)Google Scholar
  14. 14.
    Attneave, F.: Some informational aspects of visual perception. Psychological Review 61, 183–193 (1954)CrossRefGoogle Scholar
  15. 15.
    Attneave, F., Arnoult, M.D.: The quantitative study of shape and pattern perception. Psychological Bulletin 53, 452–471 (1956)CrossRefGoogle Scholar
  16. 16.
    Hochberg, J.E., Brooks, V.: The psychophysics of form: Reversible perspective drawings of spatial objects. American Journal of Psychology 73, 337–354 (1960)CrossRefGoogle Scholar
  17. 17.
    Johnson, C.J., Paivio, A., Clark, J.A.: Cognitive components of picture naming. Psychological Bulletin 120(1), 113–139 (1996)CrossRefGoogle Scholar
  18. 18.
    Geiselman, R.E., Landee, B.M., Christen, F.G.: Perceptual discriminability as a basis for selecting graphic symbols. Human Factors 24, 329–337 (1982)Google Scholar
  19. 19.
    Beck, H., Graham, N., Sutter, A.: Lightness differences and the perceived segregation of regions and population. Perception and Psychophysics. 49(3), 257–269 (1991)CrossRefGoogle Scholar
  20. 20.
    Harwerth, R.S., Levi, D.M.: Reaction time as a measure of suprathreshold grating detection. Vision Research 18, 1579–1586 (1978)CrossRefGoogle Scholar
  21. 21.
    Sutter, A., Beck, J., Graham, N.: Contrast and spatial variables in texture segregation: Testing a simple spatial-frequency channels model. Perception and Psychophysics 46(4), 312–332 (1989)CrossRefGoogle Scholar
  22. 22.
    Vassilev, A., Mitov, D.: Perceptual time and spatial frequency. Vision Research 16, 89–92 (1976)CrossRefGoogle Scholar
  23. 23.
    Hoeger, R.: Speed of processing and stimulus complexity in low-frequency and high-frequency channels. Perception 26, 1039–1045 (1997)CrossRefGoogle Scholar
  24. 24.
    Parker, D.M., Lishman, J.R., Hughes, J.: Integration of spatial information in human vision is temporally anisotropic: evidence from a spatiotemporal discrimination task. Perception 26, 1169–1180 (1997)CrossRefGoogle Scholar
  25. 25.
    Forsythe, A., Sheehy, N., Sawey, M.: The automated measurement of pictorial image complexity: a feasibility study. In: Harris, D., Duffy, V., Smith, M., Shephanisdis, C. (eds.) Human-Centred Computing: Cognitive, Social and Ergonomic Aspects, vol. 3, pp. 205–209. Lawrence Erlbaum, Hillsdale (2003b)Google Scholar
  26. 26.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)CrossRefGoogle Scholar
  27. 27.
    Vitevitch, M.S., Armbrüster, J., Chu, S.: Sublexical and Lexical Representations in Speech Production: Effects of Phonotactic Probability and Onset Density. Journal of Experimental Psychology: Learning, Memory, and Cognition 30(2), 514–529 (2004)Google Scholar
  28. 28.
    Donderi, D.: Visual Complexity: A review. Psychological Bulletin 132, 73–97 (2006)CrossRefGoogle Scholar
  29. 29.
    Shannon, C.E., Weaver, W.: The mathematical theory of communication. University of Illinois Press, Urbana (1949)MATHGoogle Scholar
  30. 30.
    Forsythe, A., Mulhern, G., Sawey, M.: Confounds in pictorial sets: the role of complexity and familiarity in basic-level picture processing. Behavior Research Methods 40(1), 116–129 (2008)CrossRefGoogle Scholar
  31. 31.
    Rump, E.E.: Is there a general factor of preference for complexity? Perception& Psychophysics 3, 346–348 (1968)CrossRefGoogle Scholar
  32. 32.
    Berlyne, D.E.: Novelty, complexity, and interestingness. In: Berlyne, D.E. (ed.) Studies in the new experimental aesthetics: Steps toward an objective psychology of aesthetic appreciation, pp. 175–180. Hemisphere Publishing Corporation, Washington (1974)Google Scholar
  33. 33.
    Queen, M.: Icon Analysis; Evaluating Low Spatial Frequency Compositions, Boxes and Arrows (2006),
  34. 34.
    De Valios, R., De Valios, K.: Spatial Vision. Oxford Series, vol. 14. Oxford University Press, Oxford (1990)Google Scholar
  35. 35.
    Ginsburg, A.P., Evans, D.W.: Contrast sensitivity predicts pilots’ performance in aircraft simulators. American Journal of Optometry and Physiological Optics 59, 105–109 (1982)CrossRefGoogle Scholar
  36. 36.
    Harvey, L.O., Roberts Jr., J.O., Gervais, M.J.: The spatial frequency basis of internal representations. In: Geissler, H.G., Buffart, H.F.J.M., Leeuvenberg, E.L.J., Sarris, V. (eds.) Modern (1983)Google Scholar
  37. 37.
    Treisman, A.: Features and objects in visual processing. Scientific America, 106–115 (November 1986)Google Scholar
  38. 38.
    Treisman, A., Gelade, G.: A feature integration theory of attention. Cognitive Psychology 12, 97–136 (1980)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alexandra Forsythe
    • 1
  1. 1.Liverpool John Moores UniversityUK

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