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Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Editor: Stephen A. Burns
  • Vol. 26, Iss. 6 — Jun. 1, 2009
  • pp: 1414–1423

Evolutionary models of color categorization. I. Population categorization systems based on normal and dichromat observers

Kimberly A. Jameson and Natalia L. Komarova  »View Author Affiliations

JOSA A, Vol. 26, Issue 6, pp. 1414-1423 (2009)

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The evolution of color categorization is investigated using artificial agent population categorization games, by modeling observer types using Farnsworth–Munsell 100 Hue Test performance to capture human processing constraints on color categorization. Homogeneous populations of both normal and dichromat agents are separately examined. Both types of populations produce near-optimal categorization solutions. While normal observers produce categorization solutions that show rotational invariance, dichromats’ solutions show symmetry-breaking features. In particular, it is found that dichromats’ local confusion regions tend to repel color category boundaries and that global confusion pairs attract category boundaries. The trade-off between these two mechanisms gives rise to population categorization solutions where color boundaries are anchored to a subset of locations in the stimulus space. A companion paper extends these studies to more realistic, heterogeneous agent populations [ J. Opt. Soc. Am. A 26, 1424–1436 (2009) ].

© 2009 Optical Society of America

OCIS Codes
(330.1690) Vision, color, and visual optics : Color
(330.4060) Vision, color, and visual optics : Vision modeling
(330.5020) Vision, color, and visual optics : Perception psychology

ToC Category:
Vision, Color, and Visual Optics

Original Manuscript: August 29, 2008
Revised Manuscript: March 5, 2009
Manuscript Accepted: March 6, 2009
Published: May 22, 2009

Virtual Issues
Vol. 4, Iss. 8 Virtual Journal for Biomedical Optics

Kimberly A. Jameson and Natalia L. Komarova, "Evolutionary models of color categorization. I. Population categorization systems based on normal and dichromat observers," J. Opt. Soc. Am. A 26, 1414-1423 (2009)

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  1. P. Kay and T. Regier, “Resolving the question of color naming universals,” Proc. Natl. Acad. Sci. U.S.A. 100, 9085-9089 (2003). [CrossRef] [PubMed]
  2. T. Belpaeme and J. Bleys, “Explaining universal color categories through a constrained acquisition process,” Adapt. Behav. 13, 293-310 (2005). [CrossRef]
  3. L. Steels and T. Belpaeme, “Coordinating perceptually grounded categories: a case study for colour,” Behav. Brain Sci. 28, 469-529 (2005). [CrossRef] [PubMed]
  4. L. D. Griffin, “The basic colour categories are optimal for classification,” J. R. Soc., Interface 3, 71-85 (2006). [CrossRef]
  5. M. Dowman, “Explaining color term typology with an evolutionary model,” Cogn. Sci. 31, 99-132 (2007). [CrossRef] [PubMed]
  6. N. L. Komarova, K. A. Jameson, and L. Narens, “Evolutionary models of color categorization based on discrimination,” J. Math. Psychol. 51, 359-382 (2007). [CrossRef]
  7. A. Puglisi, A. Baronchelli, and V. Loreto, “Cultural route to the emergence of linguistic categories,” Proc. Natl. Acad. Sci. U.S.A. 105, 7936-7940 (2008). [CrossRef] [PubMed]
  8. N. L. Komarova and K. A. Jameson, “Population heterogeneity and color stimulus heterogeneity in agent-based color categorization,” J. Theor. Biol. 253, 680-700 (2008). [CrossRef] [PubMed]
  9. T. Regier, P. Kay, and N. Khetarpal, “Color naming reflects optimal partitions of color space,” Proc. Natl. Acad. Sci. U.S.A. 104, 1436-1441 (2007). [CrossRef] [PubMed]
  10. M. A. Webster, S. M. Webster, S. Bharadwadj, R. Verma, J. Jaikumar, J. Madan, and E. Vaithilingam, “Variations in normal color vision. III. Unique hues in Indian and United States observers,” J. Opt. Soc. Am. A 19, 1951-1962 (2002). [CrossRef]
  11. M. A. Webster and P. Kay, “Individual and population differences in focal colors,” in Anthropology of Color: Interdisciplinary Multilevel Modeling, R.E.MacLaury, G.V.Paramei, and D.Dedrick eds. (Benjamins, 2007), pp. 29-53.
  12. Delwin T. Lindsey and Angela M. Brown, “Universality of color names,” Proc. Natl. Acad. Sci. U.S.A. 103, 16608-16613 (2006). [CrossRef] [PubMed]
  13. K. A. Jameson and N. L. Komarova, “Evolutionary models of categorization. II. Investigations based on realistic observer models and population heterogeneity,” J. Opt. Soc. Am. A 26, 1424-1436 (2009). [CrossRef]
  14. T. N. Cornsweet, Visual Perception (Academic, 1970).
  15. R. N. Shepard and L. A. Cooper, “Representation of colors in the blind, color blind, and normally sighted,” Psychol. Sci. 3, 97-104 (1992). [CrossRef]
  16. D. Farnsworth, The Farnsworth-Munsell 100 Hue Test for the Examination of Color Vision (Munsell Color Company, 1949/1957).
  17. K. Mantere, J. Parkkinen, M. Mäntyjärvi, and T. Jaaskelainen, “Eigenvector interpretation of the Farnsworth-Munsell 100-hue test,” J. Opt. Soc. Am. A 12, 2237-2243 (1995). [CrossRef]
  18. R. S. Cook, P. Kay, and T. Regier, “The World Color Survey database: history and use,” in Handbook of Categorisation in Cognitive Science, H.Cohen and C.Lefebvre, eds. (Elsevier, 2005), pp. 223-242. [CrossRef]
  19. T. Regier, P. Kay, and R. S. Cook, “Focal colors are universal after all,” Proc. Natl. Acad. Sci. U.S.A. 102, 8386-8391 (2005). [CrossRef] [PubMed]
  20. J. Birch, Diagnosis of Defective Colour Vision, 2nd ed. (Butterworth-Heinemann, 2001).
  21. J. H. Nelson, “Anomalous trichromatism and its relation to normal trichromatism,” Proc. Phys. Soc. 50, 661-702 (1938). [CrossRef]
  22. J. Pokorny, V. C. Smith, G. Verriest, and A. J. L. G. Pinckers, Congenital and Acquired Color Vision Defects (Grune & Stratton, 1979).
  23. G. Wyszecki and W. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd ed. (Wiley, 1982).
  24. L. T. Sharpe, A. Stockman, H. Jägle, and J. Nathans, “Opsin genes, cone photopigments, color vision, and color blindness,” in Color Vision: From Genes to Perception, K.R.Gegenfurtner and L. T. Sharpe, eds. (Cambridge U. Press, 1999), pp. 3-51.
  25. B. Sayim, K. A. Jameson, N. Alvarado, and M. K. Szeszel, “Semantic and perceptual representations of color: evidence of a shared color-naming function,” J. Cogn. Culture 5, 427-486 (2005). [CrossRef]
  26. Farnsworth-Munsell Scaling Software, Version 2.1 (MacBeth Division of Kolmorgen Corporation, 1997).
  27. D. Farnsworth, “The Farnsworth-Munsell 100-Hue and Dichotomous Tests for color vision,” J. Opt. Soc. Am. 33, 568-578 (1943). [CrossRef]
  28. S. J. Dain, “Clinical colour vision tests,” Clin. Exp. Optom. 87, 276-293 (2004). [CrossRef] [PubMed]
  29. The number of sequence inversions (of adjacent caps) needed to recreate a perfect order from the sorting data.
  30. Assuming that dichromats perform similarly to normals away from confusion axes, despite the suggestion of error-free performance outside dichromat local confusion regions in Fig. .
  31. These confusion regions closely resemble some based on the standard Farnsworth method of scoring .
  32. P=0 models an ideal normal-observer's sorting of the FM100 85 caps with zero error (see ), whereas a realistic normal-observer model sorts the 85 caps with probabilistic (p>0) error.
  33. W. R. Garner, The Processing of Information and Structure (Erlbaum, 1974).
  34. K. Jameson and R. G. D'Andrade, “It's not really red, green, yellow, blue: An inquiry into cognitive color space,” in Color Categories in Thought and Language, C.L.Hardin and L.Maffi, eds. (Cambridge U. Press, 1997), pp. 295-319. [CrossRef]
  35. K. A. Jameson, “Culture and cognition: What is universal about the representation of color experience?” J. Cogn. Culture 5, 293-347 (2005). [CrossRef]
  36. K. A. Jameson, “Sharing perceptually grounded categories in uniform and nonuniform populations,” Behav. Brain Sci. 28, 501-502 (2005). [CrossRef]

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