Explaining Colour Term Typology with an Evolutionary Model Mike Dowman 24 November, 2005

Colour and Colour Words Colour is a continuous three dimensional space Colour words denote regions of colour Is colour naming completely arbitrary?

Colour Term Typology There are clear typological patterns in how languages name colour.  neurophysiology of the vision system  colours in the environment  or cultural factors? 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 Pirahã arguably has none The concept of basic colour term is disputed

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.

The Munsell Array

English and Berinmo Colour Mappings

Berlin and Kay (1969) Small set of possible colour term systems 98 Languages in study Only Cantonese, Vietnamese, Western Apache, Hopi, Samal and Papago didn’t fit the hierachy Berlin and Kay’s Implicational Hierarchy. purple pink orange grey white black red green yellow blue brown

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 + green + black-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-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

Criticism of Kay Much more variability than Kay suggests – both within and across languages Criteria for distinguishing basic colour terms don’t work Colour is often conflated with other properties: texture, variegation, etc. Colour words can only be understood in relation to the rest of the language Colour words have religious and cultural significance  Saunders (1992), MacLaury (1997), Levinson (2001), MacKeigan (2005)

Universal Foci Frequency Distribution of 10,644 WCS Colour-term Foci (MacLaury, 1997)

Why are there Universal Foci? Unique hues red and green yellow and blue Opposite colours: Afterimages Mixing opposites colours produces grey

Rosch-Heider’s Studies Foci are more salient Children tend to pick focal colours when given a free choice Dugum Dani speakers found it easier to learn to associate words with foci Most other results of Rosch can be attributed to properties of the Munsell array used in her experiments

Neurophysiological Basis for Unique Hues Opponent cells  Each cell responds maximally to red, yellow, green or blue  And minimally to the opposite colour Kay and McDaniel (1978) proposed that the maximal firing rates of these cells explained the universal foci

Problems with Neurophysiological Explanation Null point of red-green opponents is at greenish-yellow Cells respond to achromatic light Cells responding to red don’t respond to low wavelength violet colours Inter-subject variation in sensitivity to opponent channels does not correlate with inter-subject variation in which hues are unique

Predicting Colour Terms from Foci Hypothesis: green and blue are close together  green-blue composites are common  turquoise never occurs red and blue are far apart  red-blue composites never occur  purple is common Are they the correct distances between foci?

Conceptual Colour Spaces Similarity of colours is not predictable from physical properties of light Several perceptually standard colour spaces (Munsell, CIE L*a*b*, Optical Society of America, etc.) In Munsell red and blue are the most dissimilar In CIE L*a*b* they are the most similar  The relative distances between universal foci vary greatly between colour spaces  Is there really a correct psychological colour solid?

Colour Space in the Model red - 7 blue - 30 green - 26 yellow - 19 Colour space is 40 units in size

Iterated Learning Models Models simulate the transmission of language between agents (artificial people) Each agent can learn a language based on utterances spoken by another agent In turn they can speak and so create data from which another agent can learn red blue green yellow red blue green yellow red blue green yellow naming of colours Bayesian inference of colour term system U1U1 U2U2

Tony Belpaeme (2002) Ten agents Colour categories represented with adaptive networks CIE-L*a*b* colour space used Multi or single-generational Communication or individual development of colour words

Guessing game Speaker tries to find a word that names a topic colour but not context colours If this fails it modifies its colour categories to increase their discriminative potential Otherwise word and topic + context shown to the hearer If hearer can distinguish topic from context, word-category association strengthened. Otherwise hearer is shown the correct topic, and adapts its colour category

Emergent Languages Coherent colour categories emerged that were shared by all the agents Colour space divided into a number of regions – each named by a different colour word But some variation between speakers And no explanation of Typology

Belpaeme and Bleys (in press) Colour terms represented using points in the colour space Colours chosen from natural scenes, or at random

Results Locations of centres of emergent colour categories correlated with those seen in the World Colour Survey  Most clustered in a few parts of the colour space  Similarity was greatest when communication was simulated  And when example colours selected randomly Shape of colour space but not colours in the environment helps to explain typology

My Colour Model Learns using Bayesian inference Agents simply name colours (no feedback given about success of learning) Simplified 1 dimensional colour space Ten agents Multiple generations

Learning Colour Terms Each colour term is learned independently to any others Data is a set of example colours Universal foci are especially salient  Agents are more likely to remember examples of universal foci than of other colours

Bayesian Learning high probability hypothesis medium probability hypothesis low probability hypothesis

Urdu

Agent Communication Nol: 15, 18, 23 Wor: 38, 5, 11 Mehi: 25, 28, 30, 35 Agent 3 Nol: 11, 14 Wor: 3, 12 Mehi: 33 Agent 8 Says: Mehi Both agents can see: colour 27 Mehi: 27 remembered by agent 8 Agent 3 thinks Mehi is the best label for colour 27

The Speaker makes up a new word to label the colour. Start 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. Yes (P=0.001) A speaker is chosen. A hearer is chosen. A colour is chosen. 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. My Model

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 simulation runs in each condition Languages spoken at end analysed Only agents over half average lifespan included Only terms for which at least 4 examples had been remembered were considered

Number of Colour Terms Emerging

The colour term systems of four agents from a single simulation Degree of membership in colour category   Hue 

Analyzing the Results Speakers didn’t have identical languages  Criteria needed to classify language spoken in each simulation For each agent, 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 agents chosen

Example: One Emergent Language Denotations of Basic Color Terms for all Adults in a Community Each row is one agent Each column is a hue Boxes mark universal foci

Typological Results 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 universal focus 37 had an extra term

Does Increased Salience of Universal Foci Matter?

Universal Foci Create More Regular Colour Term Systems 644 purple terms 374 orange terms 118 lime terms 16 turquoise terms Only 87 of 415 emergent systems fits trajectories

How Reliable is WCS Data? Would a model that more closely replicated the WCS data be a better model? Field linguists sometimes suggest that colours are much more messy than Kay et al suggest WCS is only a sample – not a gold standard New types of colour term systems will probably emerge if more languages are investigated

Why an evolutionary model? Couldn’t we just explain everything from distances between universal foci? How to explain which types of colour term occur together? (Purple doesn’t usually occur in a language with a green-blue composite.) Red-yellow is commoner than yellow- green But orange is also commoner than lime  Foci distances alone cannot explain this

Explaining Composite Frequencies Red-yellow distance is 12 Yellow-green distance is 7 So why more red-yellow than yellow-green? Green usually forms a composite with blue Red is too far from blue to form a composite  Only an evolutionary model could predict the effect of these interacting pressures

Why a Bayesian Model? Different assumptions in design of model would have altered learning biases Why a Bayesian model at all?  Forces assumptions and prior bias to be explicit  Bayesian models closely parallel human learning (controversial!) Model should work just as well with an alternative learning mechanism.

A Three Dimensional Model The main limitation of the model is that it neglects the dimensions of lightness and saturation.  More accurate  Can account for pink, grey, black, white, brown Needs a distribution over possible shapes of colour term denotations (not just size and location) Kay and McDaniel (1978) was also 1 dimensional

What does the model tell us? Correct ordering of distances between universal foci (green and blue closest, red and blue furthest apart)? Colour typology can be explained in terms of properties of the perceptual system and the process of linguistic transmission?  The model makes a more explicit connection between universal foci and typological data than ever before.