The Evolution of Colour TermsExplaining Typology

Mike Dowman

Language, Evolution and Computation Research Unit, University of Edinburgh

3 September, 2005

Colour Term Typology

There are clear typological patterns in how languages name colour.

neurophysiology of vision system or cultural explanation?

• 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

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.

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

Neurophysiology and Unique Hues

Red and green, yellow and blue are opposite colours

De Valois and Jacobs (1968): There are cells in the retina that respond

maximally to either one of the unique hues, black or white

Heider (1971): The unique hues are especially salient

psychologically

Tony Belpaeme (2002)

• Ten artificial people

• Colour categories represented with adaptive networks

• CIE-LAB colour space used (red-green, yellow-blue, light-dark)

• Agents try to distinguish target from context colours (the guessing game).

• Correction given in case of failure

Emergent Languages

• Coherent colour categories emerged that were shared by all the artificial people

• Colour space divided into a number of regions – each named by a different colour word

• But some variation between speakers

No explanation of Typology

Belpaeme and Bleys (2005)

Colour terms represented using points in the colour space

Colours chosen from natural scenes, or at random

Few highly saturated colours

Emergent colour categories tend to be clustered at certain points in the colour space

Similarity with WCS was greatest when both natural colours were used and communication was simulated

Colour Space in Bayesian Acquisitional Model

red - 7

orange

purple

blue - 30green - 26

yellow - 19

Possible Hypotheses

high probabilityhypothesis

medium probability hypothesis

low probabilityhypothesis

Equations

)(

)()|()|(

dP

hPhdPdhP Bayes’ RuleBayes’ Rule

h

c

R

RProbability of an accurate example at colour c within h if hypothesis h is correct

t

c

R

RProbability of an erroneous example at colour c

RRcc is probability of remember an example at colour is probability of remember an example at colour cc

RRhh is sum of is sum of RRcc for all for all cc in hypothesis in hypothesis hh

RRtt is sum of is sum of RRcc for whole of the colour spacefor whole of the colour space

Probability of the data

Problem – we don’t know which examples are accurateProblem – we don’t know which examples are accurate

pp is the probability for each example that it is accurate is the probability for each example that it is accuratee e is an example is an example E E is the set of all examplesis the set of all examples

t

c

R

RpheP

)1()|(

Probability for examples outside of Probability for examples outside of

hypothesis (must be inaccurate)hypothesis (must be inaccurate)

Probability for examples inside of Probability for examples inside of hypothesis (may be accurate or hypothesis (may be accurate or inaccurate)inaccurate) t

c

h

c

R

Rp

R

pRheP

)1()|(

Ee

hePhdP )|()|(

Hh

hdPhPdP )|()()(

Hypothesis Averaging

We really want to know the probability that each colour can be denoted by the colour term

So, sum probabilities for all hypotheses that include the colour in their denotation

Doing this for all colours produces fuzzy sets

Hhi

Hhii

ii

hdP

hdP

hdPhP

hdPhPdhP

)|(

)|(

)|()(

)|()()|(

Substituting into Bayes’ rule:

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

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 people 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 person, 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 people

chosen

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

Adding Random Noise

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

-RType of colour term

WCS

No noise

50% noise

The model is very robust to noise

Derived terms with noise

• 60.6% purple

• 26.8% orange

• 0.3% turquoise

• 9.9% lime

Number of Colour Terms Emerging

2

2.5

3

3.5

4

4.5

5

5.5

0 20 40 60 80 100 120

Number of colour acurate examples remembered during an average lifetime

Me

an

nu

mb

er

of

ba

sic

co

lou

r te

rms

in

em

erg

en

t la

ng

ua

ge

s

No noise

50% noise

Implications of number of words Emerging

Languages are complex because we talk a lot

Not because complex languages help us to communicate

• No communication ever takes place

• So no truly functional pressures

Conclusions

(1) Colour term typology a product of the uneven spacing of unique hues in the conceptual colour space.

Problem: we might be able to obtain similar results with a significantly different model.

(2) Colour term typology can be explained as a product of learning biases and cultural evolution.