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The Evolution of Colour Terms Explaining Typology Mike Dowman Language, Evolution and Computation Research Unit, University of Edinburgh 3 September, 2005 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? 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 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 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. 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) Evolutionary Trajectories 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 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 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 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 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 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 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 Colour Space in Bayesian Acquisitional Model red - 7 orange purple yellow - 19 blue - 30 green - 26 Possible Hypotheses low probability hypothesis high probability hypothesis medium probability hypothesis Equations Bayes’ Rule P ( d | h) P ( h) P( h | d ) P( d ) Probability of an accurate example at colour c within h if hypothesis h is correct Rc Rh Probability of an erroneous example at colour c Rc Rt 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 Rc for whole of the colour space 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) P(d | h) P(e | h) eE (1 p) Rc P(e | h) Rt pRc (1 p) Rc P ( e | h) Rh Rt P(d ) P(h) P(d | h) hH Hypothesis Averaging Substituting into Bayes’ rule: P(h) P(d | h) P( h | d ) P(hi ) P(d | hi ) hi H P ( d | h) P(d | hi ) hi H 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 Urdu 1 0.8 Nila Hara 0.6 Banafshai 0.4 Lal Pila 0.2 0 Hue (red at left to purple at right) Unique Hues Start A speaker is chosen. Evolutionary Model A hearer is chosen. A colour is chosen. Yes (P=0.001) Decide whether speaker will be creative. The Speaker makes up a new word to label the colour. 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. 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 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 Typological Results 25 20 WCS Simulations 15 10 G-B-R Y-G-B R-Y-G B-R G-B Y-G R-Y Blue Green 0 Yellow 5 Red Percent of terms of this type 30 Type of colour term Percentage of Color Terms of each type in the Simulations and the World Color Survey Derived Terms • • • • 80 purple terms 20 orange terms 0 turquoise terms 4 lime terms 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 0 Type of colour term G-B-R Y-G-B R-Y-G B-R G-B Y-G R-Y Blue Green Yellow Red Percent of terms of this type Adding Random Noise 30 25 20 15 10 5 WCS No noise 50% noise Derived terms with noise • • • • 60.6% purple 26.8% orange 0.3% turquoise 9.9% lime The model is very robust to noise Mean number of basic colour terms in emergent languages Number of Colour Terms Emerging 5.5 5 4.5 4 No noise 3.5 50% noise 3 2.5 2 0 20 40 60 80 100 120 Number of colour acurate examples remembered during an average lifetime 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 Conclusions (1) 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.