modelling the evolution of language for modellers and non-modellers ijcai 2005 1 hands on...

Modelling the evolution of language for modellers and non-modellersIJCAI 2005

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Hands on demonstrationHands on demonstration

Nature of colour categories


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What do we know?What do we know?

• There is evidence that colour categories are universal.– All cultures have colour categories that are

similar to RED, GREEN, BLUE, YELLOW, and so on.

• Three possible explanations– Genetically determined.– Culturally (and linguistically) determined.– Ecologically determined.

• In this demonstration we will take a closer look at the last explanation.


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Why do we want to know?Why do we want to know?

• Language is about communicating concepts, we want to now how concepts are acquired.

• As a case study we take colour categories.

• “…this may at first appear to be a comparatively trivial example of some minor aspect of language, but the implications for other aspects of language evolution are truly staggering.” (Deacon, 1997)


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Hypothesis and assumptionsHypothesis and assumptions• Research question

– Does our ecology contain enough structure to specify colour categories the way they are?

• Hypothesis– Human ecologies contain enough structure to specify

human colour categories.

• Assumptions– No semantics, culture or language is involved.– Colour categories have a prototypical nature.– Colour categories are extracted from chromatic stimuli

in an unsupervised manner.– We choose a representation for our colours that is

psychophysically plausible.


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What do we expect?What do we expect?

• If the claim is true:

– Categories extracted from the real-world data should resemble human colour categories.

– Categories extracted from the random data should not resemble human colour categories.

– Categories extracted from real-world data should not resemble the ones from random data.


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ToolsTools

• A digital camera.• Matlab (a mathematical package).• SPSS (a statistics package).


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MethodologyMethodology• Gather image collection from natural and urban

environments.

• Draw 25,000 random pixels from each collection. Construct random set as control.


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MethodologyMethodology

• Extract categories from the data– This we do by unsupervised clustering (k-means

clustering) as this does not violate our assumptions.

• Compare the categories to human colour categories– Sturges & Whitfield (1995) have recorded the 11

basic colour categories of American English-speaking informants.

– Quantitative and objective comparing happens through matching couples and calculating the correlation between clusters and human colour categories. We use Kendall’s Tau correlation for ranked and matched observations.


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The colour stimuliThe colour stimuli

natural urban

random


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Extracted categories versus human categoriesExtracted categories versus human categories

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l for Sturges and Whitfield

l for cluste

rs

NATURE vs Sturges and Whitfield (1995) in CIElab


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DemonstrationDemonstration

• A quick demonstration of a “light” version of an experiment.


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ResultsResults

0 10 20 30 40 50 60 70 80 90 1000102030405060708090100

l for Sturges and Whitfieldl for c

lusters


-60 -40 -20 0 20 40 60 80-60-40-20020406080

a for Sturges and Whitfield

a for cluste

rs


-60 -40 -20 0 20 40 60 80 100-60-40-20020406080100

b for Sturges and Whitfield

b for cluste

rs


0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100


l for

clu

sters

URBAN vs Sturges and Whitfield (1995) in CIElab

-60 -40 -20 0 20 40 60 80-60-40-20020406080

a for Sturges and Whitfielda for

clusters


-60 -40 -20 0 20 40 60 80 100-60-40-20020406080100

b for Sturges and Whitfield

b for cluste

rs


0 10 20 30 40 50 60 70 80 90 1000102030405060708090100


l for cluste

rs

RANDOM vs Sturges and Whitfield (1995) in CIElab

-60 -40 -20 0 20 40 60 80-80-60-40-20020406080

a for Sturges and Whitfield

a for cluste

rs


-60 -40 -20 0 20 40 60 80 100-80-60-40-20020406080100

b for Sturges and Whitfieldb for

clusters


l a b

nature

urban

random


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CorrelationCorrelationCorrelations between lightness, colour axes, chroma and hue.

Correlation between random

distribution and human

categories is not lower than for a real-world

distribution

For two different colour appearance models (CIE L*a*b* and CIE L*u*v*).


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ConclusionConclusion

• We could not refute the null hypothesis.– Clustering random colours produces categories

that correlate equally well.

• Human ecologies have only a marginal influence on colour categories.

• What then does have an influence?– Psychophysical properties of colour perception.– The nature of categories (maximally distinct).– And possible culture and language (but no proof

in this experiment).


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More on thisMore on this• Yendrikhovskij, S.N. (2001) Computing Color Categories

from Statistics of Natural Images. Journal of Imaging Science and Technology, 45(5):409-417.

• Belpaeme, T. & Bleys, J. (2004) Does structure in the environment influence our conceptualization? Proceedings of the Conference on the Evolution of Language 2004, Leipzig, Germany.

• Steels, L. & Belpaeme, T. (2005) Coordinating perceptually grounded categories through language: A case study for colour. Behavioral and Brain Sciences, 28(4). In press.

modelling the evolution of language for modellers and non-modellers ijcai 2005 1 hands on...

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