modelling the evolution of language for modellers and non-modellers ijcai 2005 1 hands on...
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![Page 1: Modelling the evolution of language for modellers and non-modellers IJCAI 2005 1 Hands on demonstration Nature of colour categories](https://reader036.vdocuments.mx/reader036/viewer/2022062423/56649f575503460f94c7c71e/html5/thumbnails/1.jpg)
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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
0 10 20 30 40 50 60 70 80 90 1000102030405060708090100
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
NATURE vs Sturges and Whitfield (1995) in CIElab
-60 -40 -20 0 20 40 60 80-60-40-20020406080
a for Sturges and Whitfield
a for cluste
rs
NATURE vs Sturges and Whitfield (1995) in CIElab
-60 -40 -20 0 20 40 60 80 100-60-40-20020406080100
b for Sturges and Whitfield
b for cluste
rs
NATURE vs Sturges and Whitfield (1995) in CIElab
0 10 20 30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
l for Sturges and Whitfield
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
URBAN vs Sturges and Whitfield (1995) in CIElab
-60 -40 -20 0 20 40 60 80 100-60-40-20020406080100
b for Sturges and Whitfield
b for cluste
rs
URBAN vs Sturges and Whitfield (1995) in CIElab
0 10 20 30 40 50 60 70 80 90 1000102030405060708090100
l for Sturges and Whitfield
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
RANDOM vs Sturges and Whitfield (1995) in CIElab
-60 -40 -20 0 20 40 60 80 100-80-60-40-20020406080100
b for Sturges and Whitfieldb for
clusters
RANDOM vs Sturges and Whitfield (1995) in CIElab
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.