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Explaining C o l o u r 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?.

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colour and colour words
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
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
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
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
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.

berlin and kay 1969

purple

pink

orange

grey

green

yellow

white

black

red

blue

brown

Berlin and Kay’s Implicational Hierarchy.

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

world colour survey
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

Evolutionary Trajectories
derived terms
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
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
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
Universal Foci

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

why are there universal foci
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
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
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
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
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
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
Colour Space in the Model

red - 7

yellow - 19

blue - 30

green - 26

Colour space is 40 units in size

iterated learning models

red

red

red

yellow

yellow

yellow

blue

blue

blue

green

green

green

U1

U2

Bayesian inference of colour term system

naming of colours

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
tony belpaeme 2002
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
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
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
Belpaeme and Bleys (in press)

Colour terms represented using points in the colour space

Colours chosen from natural scenes, or at random

results
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
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
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
Bayesian Learning

low probability

hypothesis

high probability

hypothesis

medium probability hypothesis

agent communication

Nol: 15, 18, 23

Wor: 38, 5, 11

Mehi: 25, 28, 30, 35

Agent Communication

Agent 3

Agent 8

Agent 3 thinks Mehi is the best label for colour 27

Mehi: 27 remembered by agent 8

Says: Mehi

Nol: 11, 14

Wor: 3, 12

Mehi: 33

Both agents can see: colour 27

slide34

Start

A speaker is chosen.

My Model

A hearer is chosen.

A colour is chosen.

Yes (P=0.001)

The Speaker makes up a new word to label the colour.

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.

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
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
slide37

Degree of membership in colour category 

The colour term systems of four agents from a single simulation

 Hue 

analyzing the results
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
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
Typological Results

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

derived terms41
Derived Terms
  • 80 purple terms
  • 20 orange terms
  • 0 turquoise terms
  • 4 lime terms
divergence from trajectories
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
universal foci create more regular colour term systems
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
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
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
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
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
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
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.