Colour Language 2: Explaining Typology Mike Dowman Language and Cognition 5 October, 2005

Today’s Lecture Kay and McDaniel: Direct neurophysiological explanation Terry Regier et al: Predicting denotations from foci Yendrikhovskij: Colours in the environment Evolutionary and Acquisitional Explanations Me: An evolutionary model

Kay and McDaniel (1978) Red, yellow, green and blue colour categories could be derived directly from the outputs of opponent process cells Degree of membershipin colour category hue

Opponent Processes Composite categories can be derived using fuzzy unions Purple, pink, brown and grey can be derived as fuzzy using fuzzy intersections Union of blue and green = blue-greenIntersection of red and yellow = orange

Problems Colour term denotations vary across languages. Denotations and foci aren’t in the same places as opponent process cells predict. Doesn’t explain why some types of colour term are unattested (e.g. blue-red composites, yellow-green derived terms (lime)).

Regier et al (2005) Is knowing the location of the prototypes in the colour space enough to predict the full denotations of colour words? Investigated using a computer model. Used CIEL*a*b colour space which attempts to accurately capture conceptual distances between colours

Details of Computer Model Colour categories are represented as points in the colour space – each at a unique hue Plus a parameter that controls for category size Size parameter was fit to naming data to get best result Each colour is classified based on the distance to each focus, and the size of the categories based on each focus

Results: Berinmo Berinmo naming data: Model predictions fit to data: Categories centred at red, yellow, green, black and white universal foci Explains naming in terms of foci But doesn’t explain which foci each language uses Doesn’t show that non-attested colour term systems can’t be represented

Yendrikhovskij (2001) Can the colours in the environment explain typological patterns in colour naming? N.B. Photo from Tony Belpaeme, not Yendrikhovskij

Distribution of Colours Full range of colours:Those in natural images: Colours in natural images mapped to CIE colour space Then clustered (those closest to each other were grouped together) Number of clusters was varied

Yendrikhovskij’s Results 11 Clusters  10 are close to centres’ of English colour terms  A yellow-green cluster replaces purple 7 Clusters  black, white, red, green, yellow, blue, brown 3 Clusters  black, white, red Distribution of colours in the environment together with the properties of the ‘sensorial system’ predict attested colour term systems quite well

Acquisitional and Evolutionary Explanations Language Acquisition Device Individual's Knowledge of Language Primary Linguistic Data Chomsky’s Conceptualization of Language Acquisition. Language Acquisition Device Arena of Language Use Primary Linguistic Data Individual's Knowledge of Language Hurford’s Diachronic Spiral

Learnable and Evolvable Languages All of the languages which actually exist in the world will fall within the intersection of the learnable languages, (L), and those languages which are preferred as a result of evolutionary pressures, (F) (Kirby, 1999). L F E Occurring languages

Expression-Induction 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 L0L0 L1L1 L2L2

Evolving Colour Categories: Dowman (2003, 2004) Can we explain colour term typology in terms of cultural evolution?  This was the original thesis of Berlin & Kay (1969). Small biases in the way we learn or perceive colour categories could create evolutionary pressures that, over several generations, result in only a limited range of languages emerging. Tony Belpaeme (2002) and Me both have expression-induction models of colour term evolution

Hypothesis Typological patterns observed in colour term naming are due to irregularities in the conceptual colour space.  In particular the irregular spacing of the unique hues  and their added salience

Agents’ Conceptual Colour Space red - 7 orange purple blue - 30 green - 26 yellow - 19 The whole colour space is 40 units in size

Learning by Bayesian Inference Statistical inference allows the most likely denotation for colour terms to be estimated based on some example colours Has no predisposition to believe any type of colour term is more likely than any other Can cope with errors in the data Each colour word is learned individually

Learning Colour Word Denotations from Examples 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. Evolutionary 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

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 unique hues

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 unique hue 37 had an extra term

Does Increased Salience of Unique Hues Matter?

Unique Hues 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 tend to suggest that colours are much more messy than Kay et al suggest WCS is only a sample – not a gold standard Is data massaged to fit theories?

Summary Typological patterns in colour term systems cross-linguistically can be explained in terms of uneven conceptual spacing of the unique hues. The typological patterns are emergent properties of the cultural evolution of colour term systems over time. The evolutionary approach readily accommodates exceptional languages. Environmental and/or cultural pressures probably also influence emergent colour term systems.

References Belpaeme, Tony (2002). Factors influencing the origins of color categories. PhD Thesis, Artificial Intelligence Lab, Vrije Universiteit Brussel. Berlin, B. & Kay, P. (1969). Basic Color Terms. Berkeley: University of California Press. Dowman, M. (2003). Explaining Color Term Typology as the Product of Cultural Evolution using a Bayesian Multi-agent Model. In R. Alterman and D. Kirsh (Eds.) Proceedings of the 25th Annual Meeting of the Cognitive Science Society. Mahwah, N.J.: Lawrence Erlbaum Associates. Dowman, M. (2004). Colour Terms, Syntax and Bayes: Modelling Acquisition and Evolution. Ph.D. Thesis, University of Sydney. Hurford, J. R. (1987). Language and Number The Emergence of a Cognitive System. New York, NY: Basil Blackwell. Kirby, S. (1999). Function Selection and Innateness: The Emergence of Language Universals. Oxford: Oxford University Press.

Kay, P. & McDaniel, K. (1978). The Linguistic Significance of the Meanings of Basic Color Terms. Language, 54 (3): Regier, T. Kay, P. and Cook, R. S. (2005). Universal Foci and Varying Boundaries in Linguistic Color Categories. In B. G. Bara, L. Barsalou and M. Bucciarelli (Eds.), Proceedings of the XXVII Annual Conference of the Cognitive Science Society. Mahwah, New Jersey: Lawrence Erlbaum Associates. Yendrikhovskij, S. N. (2001). Computing Color Categories from Statistics of Natural Images, Journal of Imaging Science and Technology, 45(5).

Discussion Questions for Tomorrow Is colour term typology best explained in terms of neurophysiology, the environment, cultural practices, or some other factor? What evidence is there for innate biases concerning colour terms? Is colour term evolution really as predictable as Berlin and Kay’s implicational hierarchy suggests? Is it really possible to separate basic from non-basic colour terms objectively? (Think about English and any other languages you know.) Is colour term typology best explained ontogenetically or diachronically?