The Evolution of C o l o u r Terms Explaining Typology

1. The Evolution of Colour TermsExplaining Typology Mike Dowman Language, Evolution and Computation Research Unit, University of Edinburgh 3 September, 2005

2. Colour Term Typology There are clear typological patterns in how languages name colour. • neurophysiology of vision system • or cultural explanation? • Constraints on learnable languages • or an evolutionary process?

3. Basic Colour Terms Most studies look at a subset of all colour terms: • Terms must be psychologically salient • Known by all speakers • Meanings are not predictable from the meanings of their parts • Don’t name a subset of colours named by another term

4. Number of Basic Terms English has red, orange, yellow, green, blue, purple, pink, brown, grey, black and white. crimson, blonde, taupe are not basic. All languages have 2 to 11 basic terms • Except Russian and Hungarian

5. Prototypes Colour terms have good and marginal examples  prototype categories • People disagree about the boundaries of colour word denotations • But agree on the best examples – the prototypes Berlin and Kay (1969) found that this was true both within and across languages.

6. World Colour Survey 110 minor languages (Kay, Berlin, Merrifield, 1991; Kay et al 1997; Kay and Maffi, 1999) • All surveyed using Munsell arrays Black, white, red, yellow, green and blue seem to be fundamental colours • They are more predictable than derived terms (orange, purple, pink, brown and grey)

7. white-red-yellow + black-green-blue white + red-yellow + black-green-blue white + red-yellow + black + green-blue white + red + yellow + black + green-blue white + red + yellow + black + green + blue white + red + yellow + black-green-blue white + red + yellow + green + black-blue white + red + yellow-green-blue + black white + red + yellow-green + blue + black Evolutionary Trajectories

8. Derived Terms • Brown and purple terms often occur together with green-blue composites • Orange and pink terms don’t usually occur unless green and blue are separate • But sometimes orange occurs without purple • Grey is unpredictable • No attested turquoise or lime basic terms

9. Exceptions and Problems • 83% of languages on main line of trajectory • 25 languages were in transition between stages • 6 languages didn’t fit trajectories at all  Kuku-Yalanji (Australia) has no consistent term for green • Waorani (Ecuador) has a yellow-white term that does not include red • Gunu (Cameroon) contains a black-green-blue composite and a separate blue term

10. Neurophysiology and Unique Hues Red and green, yellow and blue are opposite colours De Valois and Jacobs (1968): • There are cells in the retina that respond maximally to either one of the unique hues, black or white Heider (1971):  The unique hues are especially salient psychologically

11. Tony Belpaeme (2002) • Ten artificial people • Colour categories represented with adaptive networks • CIE-LAB colour space used (red-green, yellow-blue, light-dark) • Agents try to distinguish target from context colours (the guessing game). • Correction given in case of failure

12. Emergent Languages • Coherent colour categories emerged that were shared by all the artificial people • Colour space divided into a number of regions – each named by a different colour word • But some variation between speakers  No explanation of Typology

13. Belpaeme and Bleys (2005) Colour terms represented using points in the colour space Colours chosen from natural scenes, or at random  Few highly saturated colours Emergent colour categories tend to be clustered at certain points in the colour space Similarity with WCS was greatest when both natural colours were used and communication was simulated

14. Colour Space in Bayesian Acquisitional Model red - 7 orange purple yellow - 19 blue - 30 green - 26

15. Possible Hypotheses low probability hypothesis high probability hypothesis medium probability hypothesis

16. Equations Bayes’ Rule Probability of an accurate example at colour c within h if hypothesis h is correct Probability of an erroneous example at colour c Rc is probability of remember an example at colour c Rh is sum of Rc for all c in hypothesis h Rt is sum of Rcfor whole of the colour space

17. Probability of the data Problem – we don’t know which examples are accurate p is the probability for each example that it is accurate e is an example E is the set of all examples Probability for examples outside of hypothesis (must be inaccurate) Probability for examples inside of hypothesis (may be accurate or inaccurate)

18. Hypothesis Averaging Substituting into Bayes’ rule: We really want to know the probability that each colour can be denoted by the colour term • So, sum probabilities for all hypotheses that include the colour in their denotation • Doing this for all colours produces fuzzy sets

19. Urdu

20. Start A speaker is chosen. Evolutionary Model A hearer is chosen. A colour is chosen. Yes (P=0.001) The Speaker makes up a new word to label the colour. Decide whether speaker will be creative. No (P=0.999) The speaker says the word which they think is most likely to be a correct label for the colour based on all the examples that they have observed so far. The hearer hears the word, and remembers the corresponding colour. This example will be used to determine the word to choose, when it is the hearer’s turn to be the speaker.

21. Evolutionary Simulations • Average lifespan (number of colour examples remembered) set at: 18, 20, 22, 24, 25, 27, 30, 35, 40, 50, 60, 70, 80, 90, 100, 110 or 120 • 25 simulation runs in each condition Languages spoken at end analysed • Only people over half average lifespan included • Only terms for which at least 4 examples had been remembered were considered

22. Analyzing the Results Speakers didn’t have identical languages • Criteria needed to classify language spoken in each simulation • For each person, terms classified as red, yellow, green, blue, purple, orange, lime, turquoise or a composite (e.g. blue-green) • Terms must be known by most adults • Classification favoured by the most people chosen

23. Typological Results Percentage of Color Terms of each type in the Simulations and the World Color Survey

24. Derived Terms • 80 purple terms • 20 orange terms • 0 turquoise terms • 4 lime terms

25. Divergence from Trajectories • 1 Blue-Red term • 1 Red-Yellow-Green term • 3 Green-Blue-Red terms Most emergent systems fitted trajectories: • 340 languages fitted trajectories • 9 contained unattested color terms • 35 had no consistent name for a unique hue • 37 had an extra term

26. Adding Random Noise

27. The model is very robust to noise • 60.6% purple • 26.8% orange • 0.3% turquoise • 9.9% lime Derived terms with noise

28. Number of Colour Terms Emerging

29. Implications of number of words Emerging  Languages are complex because we talk a lot • Not because complex languages help us to communicate • No communication ever takes place • So no truly functional pressures

30. Conclusions • Colour term typology a product of the uneven spacing of unique hues in the conceptual colour space. Problem: we might be able to obtain similar results with a significantly different model. (2) Colour term typology can be explained as a product of learning biases and cultural evolution.