Colour Language 2: Explaining Typology

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

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Kay and McDaniel (1978)

• Red, yellow, green and blue colour categories
  could be derived directly from the outputs of
  opponent process cells

hue
hue
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Opponent Processes
Union of blue and green blue-green
Intersection of red and yellow orange

• Composite categories can be derived using fuzzy
  unions
• Purple, pink, brown and grey can be derived as
  fuzzy using fuzzy intersections

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Problems

• Colour term denotations vary across languages.
• Denotations and foci arent in the same places as
  opponent process cells predict.
• Doesnt explain why some types of colour term are
  unattested (e.g. blue-red composites,
  yellow-green derived terms (lime)).

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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 CIELab colour space which attempts to
  accurately capture conceptual distances between
  colours

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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

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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 doesnt explain which foci each language uses
• Doesnt show that non-attested colour term
  systems cant be represented

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Yendrikhovskij (2001)

• Can the colours in the environment explain
  typological patterns in colour naming?

N.B. Photo from Tony Belpaeme, not Yendrikhovskij
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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

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Yendrikhovskijs 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

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Acquisitional and Evolutionary Explanations
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Learnable and Evolvable Languages
E
L
F
Occurring 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).
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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

L0
L1
L2
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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

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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

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Agents Conceptual Colour Space
The whole colour space is 40 units in size
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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

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Learning Colour Word Denotations from Examples
low probability hypothesis
high probability hypothesis
medium probability hypothesis
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Urdu
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Agent Communication
Agent 3
Agent 8
Agent 3 thinks Mehi is the best label for colour
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Mehi 27 remembered by agent 8
Says Mehi
Nol 11, 14 Wor 3, 12 Mehi 33
Both agents can see colour 27
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A speaker is chosen.
Evolutionary Model
A hearer is chosen.
A colour is chosen.
Yes (P0.001)
The Speaker makes up a new word to label the
colour.
Decide whether speaker will be creative.
No (P0.999)
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
hearers turn to be the speaker.
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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 agents over half average lifespan included
• Only terms for which at least 4 examples had been
  remembered were considered

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Analyzing the Results

• Speakers didnt 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

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

• 80 purple terms
• 20 orange terms
• 0 turquoise terms
• 4 lime terms

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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

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Does Increased Salience of Unique Hues Matter?
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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

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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?

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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.

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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.

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• Kay, P. McDaniel, K. (1978). The Linguistic
  Significance of the Meanings of Basic Color
  Terms. Language, 54 (3) 610-646.
• 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).

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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 Kays 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?