Are colour categories innate or learned? Insights from computational modelling

Tony BelpaemeVUB AI-lab

Are colour categories innate or learned? Are colour categories innate or learned? Insights from computational modelling Insights from computational modelling

Tony BelpaemeTony Belpaeme

Artificial Intelligence LabArtificial Intelligence Lab

Vrije Universiteit BrusselVrije Universiteit Brussel


Situating the researchSituating the research

• Artificial Life modellingArtificial Life modelling

– Uses computer simulationUses computer simulation– Investigates particular natural phenomenaInvestigates particular natural phenomena– Provides theories which are to be referred back to Provides theories which are to be referred back to

other disciplinesother disciplines– Allows investigation of phenomena where Allows investigation of phenomena where

observational disciplines fall short.observational disciplines fall short.


Perceptual categoriesPerceptual categories

• The origins of The origins of perceptual categoriesperceptual categories

– Facial expressionsFacial expressions– OdourOdour– ColourColour

• Debate on the origins of perceptual categoriesDebate on the origins of perceptual categories


Three positionsThree positions

1.1. Genetic determinismGenetic determinism (or nativism) (or nativism)– Perceptual categories, among others, are innate.Perceptual categories, among others, are innate.– Either directly, or indirectly through other innate Either directly, or indirectly through other innate

mechanisms.mechanisms.– Chomsky, Jackendoff, Fodor, Pinker.Chomsky, Jackendoff, Fodor, Pinker.

2.2. EmpiricismEmpiricism– Perceptual categories are learned.Perceptual categories are learned.– Through interaction between the individual and its Through interaction between the individual and its

environment.environment.– Elman, Piaget.Elman, Piaget.

3.3. CulturalismCulturalism– Perceptual categories are learned.Perceptual categories are learned.– Through social (linguistic) interaction with other Through social (linguistic) interaction with other

individuals and a shared environment.individuals and a shared environment.– Whorf, Tomasello, Davidoff.Whorf, Tomasello, Davidoff.


Colour categoriesColour categories

• Case study for this work: Case study for this work:

the origins of colour categoriesthe origins of colour categories

• Why colour categories?Why colour categories?– Well-documented field Well-documented field

Anthropology, psychology, cognitive science, neurophysiology, Anthropology, psychology, cognitive science, neurophysiology, physics, philosophy, …physics, philosophy, …

– Well-known fieldWell-known field– Tightly defined domainTightly defined domain– ControversialControversial– Easy to relate toEasy to relate to


ConsensusConsensus

• Colour categories have a focal point and an Colour categories have a focal point and an extent with fuzzy boundaries.extent with fuzzy boundaries.

• Colour categories can be named.Colour categories can be named.

• Different languages use different colour words.Different languages use different colour words.

• Colour categorisation aids our visual Colour categorisation aids our visual perception.perception.

• Mechanism of human colour perception…Mechanism of human colour perception…


Human colour perceptionHuman colour perception

• Human retina contains three types of chromatic Human retina contains three types of chromatic photoreceptorsphotoreceptors

• Combining the reaction of these three types provides Combining the reaction of these three types provides chromatic discrimination.chromatic discrimination.

• From trichromacy to opponent channel processingFrom trichromacy to opponent channel processing– Psychologically humans react in an opponent fashion to Psychologically humans react in an opponent fashion to

colours.colours.


ControversiesControversies

• Are colour categories innate or learned?Are colour categories innate or learned?

• Shared within a language community?Shared within a language community?

• Shared between different cultures?Shared between different cultures?

• If learned,If learned,– What constraints are there on learning?What constraints are there on learning?– Can learning explain sharedness?Can learning explain sharedness?

• If culturally learned, does language have an If culturally learned, does language have an influence on colour categorisation?influence on colour categorisation?


Support for universalismSupport for universalism

• For exampleFor example– Berlin and Kay (1969).Berlin and Kay (1969).– Rosch (1971, 1972).Rosch (1971, 1972).


Berlin & Kay (1969)Berlin & Kay (1969)

• Experiment to identify colour categories in Experiment to identify colour categories in different cultures through their linguistic different cultures through their linguistic coding.coding.

– Identified basic colour terms (BCT) of language.Identified basic colour terms (BCT) of language.– Asked subjects to point out the focus and extent of each Asked subjects to point out the focus and extent of each

BCT.BCT.


Berlin and Kay, resultsBerlin and Kay, results


Rosch (1971, 1972)Rosch (1971, 1972)

• Experiments with Dugum Experiments with Dugum Dani tribeDani tribe

– To demonstrate that colour To demonstrate that colour categories are not under categories are not under the influence of language.the influence of language.

– All confirmed that All confirmed that categories were shared categories were shared (and thus innate) and not (and thus innate) and not influenced by language.influenced by language.


Support for relativismSupport for relativism

• Brown and Lenneberg (1954)Brown and Lenneberg (1954)– Positive correlation between ‘codability’ of colour Positive correlation between ‘codability’ of colour

terms and memorising colours.terms and memorising colours.

• Davidoff et al. (1999)Davidoff et al. (1999)– Reimplemented Rosch’s experiments.Reimplemented Rosch’s experiments.– Unable to confirm Rosch, but instead support for Unable to confirm Rosch, but instead support for

relativism.relativism.

• From 1990sFrom 1990s– Critical evaluation of 20 years universalism (Lucy, Critical evaluation of 20 years universalism (Lucy,

Saunders & van Brakel).Saunders & van Brakel).– Evidence from subjects with anomalous colour vision Evidence from subjects with anomalous colour vision

(Webster et al., 2000).(Webster et al., 2000).


SummarySummary

Position Acquisition Sharing

Universalism/ nativism

Genetic expression during development

Gene propagation

Empiricism Individual learning Similar environment, ecology and physiology

Culturalism Social and cultural learning

Similar environment, ecology and physiology with cultural learning


Four experimentsFour experiments

• GoalGoal– Study positions through computer simulations.Study positions through computer simulations.– Advance claims based on these simulations.Advance claims based on these simulations.

Colour categoriesLearned Evolved

Lan

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Individual learning

Geneticevolution

Cultural learning

Genetic evolution under linguistic pressure


Experimental setupExperimental setup

• An individual is modelled by an An individual is modelled by an agentagent

– PerceptionPerception– CategorisationCategorisation– LexicalisationLexicalisation– CommunicationCommunication

• Agents are placed in a Agents are placed in a populationpopulation


Overview of an agentOverview of an agent

Perception Categorisation Lexicalisation

Internal representation Categories

Agent

W ord form s


PerceptionPerception

• Stimuli are presented as spectral power Stimuli are presented as spectral power distributionsdistributions

• Modelling chromatic perceptionModelling chromatic perception– A model is neededA model is needed– Suitable for modelling categories onSuitable for modelling categories on


PerceptionPerception

• CIE CIE L*a*b*L*a*b* space space

– Perceptually equidistant space.Perceptually equidistant space.– Similarity function exists.Similarity function exists.– Straightforward computation.Straightforward computation.– Suitable for defining colour categories on (Lammens, Suitable for defining colour categories on (Lammens,

1994).1994).


CategorisationCategorisation

• Define categories on an internal colour Define categories on an internal colour representation.representation.

• RequirementsRequirements– Delimiting regions in representation spaceDelimiting regions in representation space– Measure of membershipMeasure of membership– Fuzzy extentFuzzy extent– LearnableLearnable– AdaptableAdaptable– MutableMutable

• Several possible representations, but the Several possible representations, but the choice fell on ‘adaptive networks’choice fell on ‘adaptive networks’


Adaptive networkAdaptive network

• An adaptive network is An adaptive network is radial basis function radial basis function network network which is adapted instead of trained.which is adapted instead of trained.

• One adaptive network represents one categoryOne adaptive network represents one category

• PropertiesProperties– Fulfils all requirements.Fulfils all requirements.– Based on exemplars.Based on exemplars.– Can represent non-convex and asymmetrical Can represent non-convex and asymmetrical

category shapes.category shapes.– Can be used as an instantiation of prototype theory Can be used as an instantiation of prototype theory

(Rosch).(Rosch).– Easy to analyseEasy to analyse– SpeedySpeedy


Adaptive networkAdaptive network

…

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LexicalisationLexicalisation

• A category can be associated with no, one or A category can be associated with no, one or more word formsmore word forms

• The strength of the association between a The strength of the association between a word form and category is represented by a word form and category is represented by a score.score.

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f2

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sn


Adaptive modelsAdaptive models

• Learning without languageLearning without language– Implemented as discrimination games.Implemented as discrimination games.

• Learning with languageLearning with language– Implemented as guessing games.Implemented as guessing games.

• Steels et alSteels et al Colour categoriesLearned Evolved

Languag

eW

ith

Wit

ho

ut

Individual learning

Geneticevolution

Cultural learning



Discrimination gameDiscrimination game

• Discrimination serves as a task to force the Discrimination serves as a task to force the acquisition of categories.acquisition of categories.

– Serves as pressure to create new categories and Serves as pressure to create new categories and adapt existing categories.adapt existing categories.

– Also used to evaluate the categorical repertoire Also used to evaluate the categorical repertoire


DG scenarioDG scenario

• Create context and chose topic.Create context and chose topic.

• Agent perceives context.Agent perceives context.

• Agent finds closest matching category for each Agent finds closest matching category for each percept.percept.

• Is topic matched by a unique category?Is topic matched by a unique category?

1, , NO o o= K

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DG dynamicsDG dynamics

• If the discrimination game fails, this provides If the discrimination game fails, this provides opportunity to create new or adapt old opportunity to create new or adapt old categories.categories.


Guessing gameGuessing game

• Two agents are selected for playing a GG.Two agents are selected for playing a GG.

• Serves as task to generate a categorical Serves as task to generate a categorical repertoire and associated lexicalisations.repertoire and associated lexicalisations.


Guessing game scenarioGuessing game scenario

• Two agents are selected; one Two agents are selected; one speakerspeaker, one , one hearer.hearer.

• A context is presented to both agents, the A context is presented to both agents, the speaker knows the topic.speaker knows the topic.

• The speaker finds a discriminating category The speaker finds a discriminating category cc for the topic.for the topic.

• It conveys the associated word form It conveys the associated word form ff to the to the hearer.hearer.

• The hearer interprets the word form, finds the The hearer interprets the word form, finds the associated category associated category c’c’ and points out the and points out the topic.topic.

( )( )point argmax c io y o=


GG dynamicsGG dynamics

Game can fail at many pointsGame can fail at many points

• SpeakerSpeaker– No discriminating category.No discriminating category.– No associated word form.No associated word form.

• HearerHearer– Does not know the word form.Does not know the word form.– Fails to point out the topic.Fails to point out the topic.

• Opportunity to extend and adapt categories and lexicon.Opportunity to extend and adapt categories and lexicon.


Evolutionary modelsEvolutionary models

• Genetic evolution without languageGenetic evolution without language– Fitness evaluated by playing discrimination games.Fitness evaluated by playing discrimination games.


Languag

eW

ith

Wit

ho

ut

Individual learning

Geneticevolution

Cultural learning



Genetic operatorGenetic operator

• Agents are endowed with the ability to have a Agents are endowed with the ability to have a categorical repertoire (!).categorical repertoire (!).

• Categories are genetically evolved, instead of Categories are genetically evolved, instead of a ‘genetic code’.a ‘genetic code’.

• Asexual reproduction.Asexual reproduction.


Genetic operatorGenetic operator

• MutationMutation

– Adding a categoryAdding a category– Removing a categoryRemoving a category– Extending a categoryExtending a category– Restricting a categoryRestricting a category

• Fitness measureFitness measure

– Discriminative successDiscriminative success


Results Results withoutwithout communication communication

• Learning categoriesLearning categories• Genetic evolution of categoriesGenetic evolution of categories


Languag

eW

ith

Wit

ho

ut

Individual learning

Geneticevolution

Cultural learning



Individual learningIndividual learning

• Discriminative successDiscriminative success

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• Category varianceCategory variance

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• Categories of two agents on Munsell chartCategories of two agents on Munsell chart

• There is There is nono sharing sharing acrossacross populations populations


Genetic evolutionGenetic evolution


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• Categories of two agents on Munsell chart.Categories of two agents on Munsell chart.

• There is There is nono sharing sharing acrossacross populations. populations.


SummarySummary

• Without communicationWithout communication

– Both approaches attain a categorical repertoire Both approaches attain a categorical repertoire functional for discrimination.functional for discrimination.

– Individual learning leads to a certain amount of Individual learning leads to a certain amount of sharing, but no 100% coherence.sharing, but no 100% coherence.

– Genetic evolution leads to complete sharing.Genetic evolution leads to complete sharing.

– Both approaches do not arrive at sharing across Both approaches do not arrive at sharing across populations.populations.

– Timescale different.Timescale different.


Results Results withwith communication communication

• Cultural learning.Cultural learning.


Languag

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Individual learning

Geneticevolution

Cultural learning



Cultural learningCultural learning


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• Communicative successCommunicative success

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Influence of communication on coherenceInfluence of communication on coherence

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With language


Influence of communication on coherenceInfluence of communication on coherence

Individual learningIndividual learning Cultural learning Cultural learning

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Discussion on cultural learningDiscussion on cultural learning

• Communication forces sharing in a cultural learning Communication forces sharing in a cultural learning through positive feedback between category formation through positive feedback between category formation and communication.and communication.

• Communication has a causal influence on category Communication has a causal influence on category formation.formation.

• First learning categories, and then lexicalising does First learning categories, and then lexicalising does allow communication.allow communication.

• Communicative success never 100%. In accordance Communicative success never 100%. In accordance with anthropological experiments (Stefflre et al, 1966).with anthropological experiments (Stefflre et al, 1966).

• Nature of categories is stochastic. Not in accord with Nature of categories is stochastic. Not in accord with Berlin and Kay (1969).Berlin and Kay (1969).

• Model possibly does not contain enough ecological and Model possibly does not contain enough ecological and biological constraints.biological constraints.


SummarySummary

• Computer simulations on the acquisition of Computer simulations on the acquisition of colour categories.colour categories.

• Extreme positions to allow a clear discussion.Extreme positions to allow a clear discussion.

• Both cultural learning and genetic evolution Both cultural learning and genetic evolution seem to be good candidates for explaining seem to be good candidates for explaining sharedness.sharedness.

• Results and recent literature lend support for Results and recent literature lend support for culturalism.culturalism.


http://arti.vub.ac.be/~tonyhttp://arti.vub.ac.be/~tony


Critical notesCritical notes

• A computer simulation requires assumptions A computer simulation requires assumptions and models.and models.

Though results confirm the choices made, the assumptions Though results confirm the choices made, the assumptions might be wrong.might be wrong.

• Weak ecological and biological constraints. Weak ecological and biological constraints. Stronger constraints might explain phenomena Stronger constraints might explain phenomena now unaccounted for.now unaccounted for.

• Colour has been taken in isolation.Colour has been taken in isolation.


ContributionsContributions

• Provide food for thought for disciplines other than AI.Provide food for thought for disciplines other than AI.

• Formalisation of an interdisciplinary and often rhetoric Formalisation of an interdisciplinary and often rhetoric debate.debate.

• Computer simulations of real world phenomena.Computer simulations of real world phenomena.

• Simulations with Simulations with continuouscontinuous meaning representation. meaning representation.

• A computational representation of natural categories.A computational representation of natural categories.


Artificial intelligenceArtificial intelligence

Two kinds of AITwo kinds of AI

Constructing intelligence

Building artefacts which display adaptive or even

intelligent behaviour.

Understanding intelligence

Studying complex behaviour through

constructing artificial systems.

http://aggie-horticulture.tamu.edu/cucurbit/insect/insect4/insect4.5a.html


Situating the researchSituating the research

• The origins and evolution of languageThe origins and evolution of language– Humans are the only species mastering complex language.Humans are the only species mastering complex language.

– Humans possess complex cognitive abilities.– Language might be the key to intelligence.


The origins and evolution of The origins and evolution of languagelanguage• Different lines of attackDifferent lines of attack

– LinguisticsLinguistics

– EthologyEthology

– AnthropologyAnthropology

– Artificial intelligence.Artificial intelligence.


The origins and evolution of The origins and evolution of languagelanguage• Computers as a tool for investigating linguistic Computers as a tool for investigating linguistic

phenomenaphenomena

– Uses models and simulations.Uses models and simulations.

– Allows investigation of mechanisms difficult or Allows investigation of mechanisms difficult or impossible to study by other disciplines.impossible to study by other disciplines.

– Allows investigation of large parameter spaces.Allows investigation of large parameter spaces.

– Provides no definite answers, but theories which are Provides no definite answers, but theories which are referred back to observational disciplines. referred back to observational disciplines.


Various evidence for universalismVarious evidence for universalism

• Opponent neural response to chromatic stimuliOpponent neural response to chromatic stimuli– Explains basic colour categories (Kay & McDaniel, Explains basic colour categories (Kay & McDaniel,

1978).1978).

• Research on infantsResearch on infants– Infants possess colour categories for fundamental Infants possess colour categories for fundamental

colours (Bornstein et al., 1976).colours (Bornstein et al., 1976).


GG scenarioGG scenario

• Two agents are selected; one Two agents are selected; one speakerspeaker, one , one hearer.hearer.

• A context is presented to both agents, the A context is presented to both agents, the speaker knows the topic.speaker knows the topic.

• The speaker finds a discriminating category The speaker finds a discriminating category cc for the topic.for the topic.

• It conveys the associated word form It conveys the associated word form ff to the to the hearer.hearer.

• The hearer interprets the word form, finds the The hearer interprets the word form, finds the associated category associated category c’c’ and points out the and points out the topic.topic.

( )( )point argmax c io y o=



Initialise the gameInitialise the game

speaker hearer



Speaker discriminates topicSpeaker discriminates topic

a

b

L



=red

Speaker finds word form associated with category



Speaker conveys word formSpeaker conveys word form

red



Hearer interprets word formHearer interprets word form

“Red”? Do I know this form? If so, is it

uniquely related to a stimulus?



Hearer non-verbally points at topicHearer non-verbally points at topic


Chromatic inputChromatic input

• Spectral Spectral power power distributions of distributions of actual chipsactual chips

– Presented in Presented in aperture aperture mode.mode.

– Constant Constant adaptation adaptation state.state.

– No No commitment commitment to any specific to any specific device.device.



• Changing environmentChanging environment

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• Changing environmentChanging environment

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Berlin and Kay, resultsBerlin and Kay, results

• Evolutionary order of basic colour terms.Evolutionary order of basic colour terms.

• A language has at most 11 BCTs.A language has at most 11 BCTs.

• Basic colour categories are genetically Basic colour categories are genetically determined.determined.

[ ] [ ] [ ]

purplegreenwhite pink

red blue brownyellowblack orange

grey

é ùê úê úé ù é ù ê úê ú ê ú< < < < < ê úê ú ê úê ú ê úë ûë û ê úê úë û



• Number of categoriesNumber of categories

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Genetic evolution with communicationGenetic evolution with communication


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• Communicative successCommunicative success

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Discussion on genetic evolution with Discussion on genetic evolution with communicationcommunication• Categories still evolve under communicative pressure.Categories still evolve under communicative pressure.

• Sharedness within population arises through Sharedness within population arises through propagation of genetic material.propagation of genetic material.

• Not shared cross-culturally.Not shared cross-culturally.

• Time-scale is radically different from cultural learning.Time-scale is radically different from cultural learning.

• Again, model possibly does not contain enough Again, model possibly does not contain enough ecological and biological constraints.ecological and biological constraints.


SummarySummary

• Learning with communicationLearning with communication

– Both approaches attain a categorical repertoire and Both approaches attain a categorical repertoire and lexicon.lexicon.

– Both arrive at Both arrive at shared categories in the populationshared categories in the population..

– Both do not arrive at shared categories across Both do not arrive at shared categories across populations.populations.

– No human-like categories.No human-like categories.

Are colour categories innate or learned? Insights from computational modelling

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