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The World Color Survey: Data Analysis and Simulations

  • Peter Lewinski
  • Michal Lukasik
  • Konrad Kurdej
  • Filip Leonarski
  • Natalia Bielczyk
  • Franciszek Rakowski
  • Dariusz PlewczynskiEmail author
Chapter

Abstract

The distribution of colors in the environment shapes local peoples’ perceptions of those colors, a phenomenon observable across all types of environments. We analyzed color categorization data from each of the 107 languages in the World Color Survey (WCS) database. Next, we grouped the WCS languages according to their geographic location, with reference to the seven terrestrial habitats (biomes) classified by the World Wildlife Fund (WWF). We developed a computer algorithm to establish the most frequently occurring colors in each environment based on the color distribution extracted from National Geographic natural images of the respective biomes. We then compared the average standardized value of the mode (i.e., most frequently occurring answers) for each group of WCS languages; we followed the same procedure for the most frequently occurring colors as well as the remaining colors. Results indicated statistically significant lower values of the average mode answers for the most frequently occurring colors. These results support our hypothesis that the environment type shapes color category boundaries. Further, we follow Steels and Belpaeme’s (Behav Brain Sci 28:469–489, 2005) model, which allows for computer simulations of the cultural emergence of color categories. An agent-based model of the cultural emergence of color categories shows that boundaries might be seen as a product of agent’s communication in a given environment. We propose the extension of this generic agent-based modeling framework to include a culturally driven emergence of color categories. We therefore underscore external constraints on cognition: the structure of the environment in which a system evolves and learns, and the learning capacities of individual agents. Finally, we discuss the methodological issues related to real data characterization (World Color Survey), as well as to the process of modeling the emergence of perceptual categories in human subjects.

Notes

Acknowledgements

This work was supported by grants from the Polish National Science Centre (Grants number 2014/15/B/ST6/05082 and UMO-2013/09/B/NZ2/00121), and the European Cooperation in Science and Technology (COST BM1405 and BM1408). Michal Lukasik was supported by research fellowships within “Information technologies: research and their interdisciplinary applications” agreement POKL.04.01.01-00-051/10-00.

References

  1. Baronchelli, A., Gong, T., Puglisi, A., & Loreto, V. (2010). Modeling the emergence of universality in color naming patterns. Proceedings of the National Academy of Sciences, 107(6), 2403–2407.Google Scholar
  2. Berlin, B., & Kay, P. (1969). Basic color terms: their universality and evolution. Berkeley, CA: University of California Press.Google Scholar
  3. Blakemore, C., & Cooper, G. F. (1970). Development of the brain depends on the visual environment. Nature, 228(5270), 477–478.CrossRefGoogle Scholar
  4. Boroditsky, L. (2001). Does language shape thought?: Mandarin and English speakers’ conceptions of time. Cognitive Psychology, 43(1), 1–22.CrossRefGoogle Scholar
  5. Cook, R. S., Kay, P., & Regier, T. (2005). The world color survey Database: history and use. In H. Cohen & C. Lefebvre (Eds.), Handbook of categorisation in the cognitive sciences (pp. 223–242). Amsterdam and London: Elsevier.CrossRefGoogle Scholar
  6. Dryer, M. S., & Haspelmath, M. (2011). The world Atlas of language structures online. Munich: Max Planck Digital Library. Retrieved from http://wals.info/.
  7. Gärdenfors, P. (2000). Conceptual spaces: The geometry of thought. Cambridge: The MIT Press.CrossRefGoogle Scholar
  8. Hein, A., Held, R., & Gower, E. C. (1970). Development and segmentation of visually controlled movement by selective exposure during rearing. Journal of Comparative and Physiological Psychology, 73(2), 181–187.CrossRefGoogle Scholar
  9. Heider, E. A. (1972). Probabilities, sampling and ethnographic method: The case of Dani colour names. Man, 7, 448–466.CrossRefGoogle Scholar
  10. Kay, P., & Kempton, W. (1984). What is the sapir-whorf hypothesis? American Anthropologist, 86(1), 65–79.CrossRefGoogle Scholar
  11. Kay, P., & Regier, T. (2003). Resolving the question of color naming universals. Proceedings of the National Academy of Sciences, 100, 9085–9089.CrossRefGoogle Scholar
  12. Kay, P., & Regier, T. (2006). Color naming universals: The case of berinmo. Cognition, 102(2).  https://doi.org/10.1016/j.cognition.2005.12.008.CrossRefGoogle Scholar
  13. Laboratory for Anthropogenic Landscape Ecology. (2010). Terrestrial biomes [Data file]. Retrieved from http://ecotope.org/anthromes/maps.
  14. Leventhal, A. G., & Hirsch, H. V. (1975). Cortical effect of early selective exposure to diagonal lines. Science, 190(4217), 902–909.CrossRefGoogle Scholar
  15. Martin, L. (1986). “Eskimo words for snow”: A case study in the genesis and decay of an anthropological example. American Anthropologist, 88(2), 418–423.CrossRefGoogle Scholar
  16. National Geographic. (2001). [Natural image of the terrestrial ecoregions of the world for all 867 land-based ecoregions on the planet]. National Geographic Data Access from nationalgeographic.com Wild World in collaboration with World Wild Fund (WWF). Retrieved from http://www.nationalgeographic.com/wildworld/profiles/photos/.
  17. Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., et al. (2001). Terrestrial ecoregions of the world: A new map of life on earth. BioScience, 51(11), 933–938.Google Scholar
  18. Plewczynski, D., Łukasik, M., Kurdej, K., Zubek, J., Rakowski, F., & Rączaszek-Leonardi, J. (2014). Generic framework for simulation of cognitive systems: A case study of color category boundaries. Man-Machine Interactions special issue of Advances in Intelligent Systems and Computing, 242, 385–393.  https://doi.org/10.1007/978-3-319-02309-0_42.CrossRefGoogle Scholar
  19. Pullum, G. K. (1989). The great eskimo vocabulary hoax. Natural Language & Linguistic Theory, 7(2). Retrieved from http://www.jstor.org/pss/4047733.
  20. Roberson, D., & Davidoff, J. (2000). The categorical perception of colors and facial expressions: The effect of verbal interference. Memory and Cognition, 28, 977–986.CrossRefGoogle Scholar
  21. Roberson, D., Davidoff, J., Davies, I. R. L., & Shapiro, L. R. (2005). Color categories: Evidence for the cultural relativity hypothesis. Cognitive Psychology, 50, 378–411.CrossRefGoogle Scholar
  22. Roberson, D., Davies, I. R. L., & Davidoff, J. (2000). Color categories are not universal: Replications & new evidence from a Stone-age culture. Journal of Experimental Psychology: General, 129, 369–398.CrossRefGoogle Scholar
  23. Searle, L. (1994). Pierce, Charles Sander. In M. Groden & M. Kreiswirth (Eds.), The John Hopkins guide to literature theory and criticism (pp. 560–562). Baltimore and London: John Hopkins University Press.Google Scholar
  24. Steels, L. (1998). The origins of syntax in visually grounded robotic agents. Artificial Intelligence, 103(1–2), 133–156.CrossRefGoogle Scholar
  25. Steels, L. (2001). Language games for autonomous robots. IEEE Intelligent Systems, 16(5), 16–22.Google Scholar
  26. Steels, L. (2002). Language games for emergent semantics. IEEE Intelligent Systems, 17(1), 83–85.Google Scholar
  27. Steels, L. (2003). Evolving grounded communication for robots. Trends in Cognitive Sciences, 7(7), 308–312.CrossRefGoogle Scholar
  28. Steels, L. (2006). Semiotic dynamics for embodied agents. IEEE Intelligent Systems, 21(3), 32–38.CrossRefGoogle Scholar
  29. Steels, L., & Belpaeme, T. (2005). Coordinating perceptually grounded categories through language: a case study for colour. Behavioural and Brain Sciences, 28(4), 469–489; discussion 489–529.Google Scholar
  30. Steels, L., & Kaplan, F. (1999). Collective learning and semiotic dynamics. Advances in Artificial Life, Proceedings, 1674, 679–688.CrossRefGoogle Scholar
  31. Wellens, P., Loetzsch, M., & Steels, L. (2008). Flexible word meaning in embodied agents. Connection Science, 20(2–3), 173–191.  https://doi.org/10.1080/09540090802091966.CrossRefGoogle Scholar
  32. World Color Survey. (2003). WCS data archives [Data file]. Retrieved from http://www.icsi.berkeley.edu/wcs/data.html.
  33. World Wildlife Fund. (2011a). The terrestrial ecoregions database [Data file]. Retrieved from http://www.worldwildlife.org/science/ecoregions/item1267.html.
  34. World Wildlife Fund. (2011b). Terrestrial ecoregions base global dataset [Data file]. Retrieved from http://www.worldwildlife.org/science/data/item1874.html.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Peter Lewinski
    • 1
  • Michal Lukasik
    • 2
  • Konrad Kurdej
    • 3
  • Filip Leonarski
    • 4
  • Natalia Bielczyk
    • 5
  • Franciszek Rakowski
    • 6
  • Dariusz Plewczynski
    • 7
    Email author
  1. 1.Faculty of Law and Saïd Business SchoolUniversity of OxfordOxfordUK
  2. 2.GoogleZurichSwitzerland
  3. 3.Faculty of Mathematics, Informatics and MechanicsUniversity of WarsawWarsawPoland
  4. 4.Faculty of ChemistryInterdisciplinary Centre for Mathematical and Computational Modelling, University of WarsawWarsawPoland
  5. 5.Polish Academy of SciencesRadboud University Nijmegen Medical CentreNijmegenNetherlands
  6. 6.Interdisciplinary Centre for Mathematical and Computational ModellingUniversity of WarsawWarsawPoland
  7. 7.Centre of New Technologies, University of WarsawWarsawPoland

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