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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 m
embership
in colour category
hue 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-green Intersection 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 fitto 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).
LF
E
Occurring languages
Expression-Induction ModelsModels 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
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 - 30green - 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 probabilityhypothesis
medium probability hypothesis
low probabilityhypothesis
Urdu
0
0.2
0.4
0.6
0.8
1
Hue (red at left to purple at right)
Nila
Hara
Banafshai
Lal
Pila
Unique Hues
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 120
• 25 simulation runs in each conditionLanguages 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 agentEach column is a hueBoxes mark unique hues
Typological Results
0
5
10
15
20
25
30P
erc
en
t o
f te
rms
of
this
ty
pe
Re
d
Ye
llow
Gre
en
Blu
e
R-Y
Y-G
G-B
B-R
R-Y
-G
Y-G
-B
G-B
-R
Type of colour term
WCS
Simulations
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?
0
5
10
15
20
25
30
Pe
rce
nt
of
term
s o
f th
is t
yp
e
Red
Yel
low
Gre
en
Blu
e
R-Y
Y-G
G-B
B-R
R-Y
-G
Y-G
-B
G-B
-R
R-Y
-G-B
Type of colour term
WCS
No UniqueHues
Unique Hues
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.
ReferencesBelpaeme, 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): 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).
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?