Tony Belpaeme

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Are colour categories innate or learned? Insights
from computational modelling

• Tony Belpaeme
• Artificial Intelligence Lab
• Vrije Universiteit Brussel

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Situating the research

• Artificial Life modelling
• Uses computer simulation
• Investigates particular natural phenomena
• Provides theories which are to be referred back
  to other disciplines
• Allows investigation of phenomena where
  observational disciplines fall short.

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Perceptual categories

• The origins of perceptual categories
• Facial expressions
• Odour
• Colour
• Debate on the origins of perceptual categories

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Three positions

• Genetic determinism (or nativism)
• Perceptual categories, among others, are innate.
• Either directly, or indirectly through other
  innate mechanisms.
• Chomsky, Jackendoff, Fodor, Pinker.
• Empiricism
• Perceptual categories are learned.
• Through interaction between the individual and
  its environment.
• Elman, Piaget.
• Culturalism
• Perceptual categories are learned.
• Through social (linguistic) interaction with
  other individuals and a shared environment.
• Whorf, Tomasello, Davidoff.

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Colour categories

• Case study for this work
• the origins of colour categories
• Why colour categories?
• Well-documented field Anthropology, psychology,
  cognitive science, neurophysiology, physics,
  philosophy,
• Well-known field
• Tightly defined domain
• Controversial
• Easy to relate to

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Consensus

• Colour categories have a focal point and an
  extent with fuzzy boundaries.
• Colour categories can be named.
• Different languages use different colour words.
• Colour categorisation aids our visual perception.
• Mechanism of human colour perception

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Human colour perception

• Human retina contains three types of chromatic
  photoreceptors
• Combining the reaction of these three types
  provides chromatic discrimination.
• From trichromacy to opponent channel processing
• Psychologically humans react in an opponent
  fashion to colours.

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Controversies

• Are colour categories innate or learned?
• Shared within a language community?
• Shared between different cultures?
• If learned,
• What constraints are there on learning?
• Can learning explain sharedness?
• If culturally learned, does language have an
  influence on colour categorisation?

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Support for universalism

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

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Berlin Kay (1969)

• Experiment to identify colour categories in
  different cultures through their linguistic
  coding.
• Identified basic colour terms (BCT) of language.
• Asked subjects to point out the focus and extent
  of each BCT.

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Berlin and Kay, results
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Rosch (1971, 1972)

• Experiments with Dugum Dani tribe
• To demonstrate that colour categories are not
  under the influence of language.
• All confirmed that categories were shared (and
  thus innate) and not influenced by language.

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Support for relativism

• Brown and Lenneberg (1954)
• Positive correlation between codability of
  colour terms and memorising colours.
• Davidoff et al. (1999)
• Reimplemented Roschs experiments.
• Unable to confirm Rosch, but instead support for
  relativism.
• From 1990s
• Critical evaluation of 20 years universalism
  (Lucy, Saunders van Brakel).
• Evidence from subjects with anomalous colour
  vision (Webster et al., 2000).

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Summary
 
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Four experiments

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

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Experimental setup

• An individual is modelled by an agent
• Perception
• Categorisation
• Lexicalisation
• Communication
• Agents are placed in a population

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Overview of an agent
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Perception

• Stimuli are presented as spectral power
  distributions
• Modelling chromatic perception
• A model is needed
• Suitable for modelling categories on

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Perception

• CIE Lab space
• Perceptually equidistant space.
• Similarity function exists.
• Straightforward computation.
• Suitable for defining colour categories on
  (Lammens, 1994).

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Categorisation

• Define categories on an internal colour
  representation.
• Requirements
• Delimiting regions in representation space
• Measure of membership
• Fuzzy extent
• Learnable
• Adaptable
• Mutable
• Several possible representations, but the choice
  fell on adaptive networks

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Adaptive network

• An adaptive network is radial basis function
  network which is adapted instead of trained.
• One adaptive network represents one category
• Properties
• Fulfils all requirements.
• Based on exemplars.
• Can represent non-convex and asymmetrical
  category shapes.
• Can be used as an instantiation of prototype
  theory (Rosch).
• Easy to analyse
• Speedy

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Adaptive network
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Lexicalisation

• A category can be associated with no, one or more
  word forms
• The strength of the association between a word
  form and category is represented by a score.

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Adaptive models

• Learning without language
• Implemented as discrimination games.
• Learning with language
• Implemented as guessing games.
• Steels et al

Colour categories
Learned
Evolved
Without
Individual learning
Genetic evolution
Language
Genetic evolution under linguistic pressure
With
Cultural learning
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Discrimination game

• Discrimination serves as a task to force the
  acquisition of categories.
• Serves as pressure to create new categories and
  adapt existing categories.
• Also used to evaluate the categorical repertoire

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DG scenario

• Create context and chose topic.
• Agent perceives context.
• Agent finds closest matching category for each
  percept.
• Is topic matched by a unique category?

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DG dynamics

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

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Guessing game

• Two agents are selected for playing a GG.
• Serves as task to generate a categorical
  repertoire and associated lexicalisations.

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Guessing game scenario

• Two agents are selected one speaker, one hearer.
• A context is presented to both agents, the
  speaker knows the topic.
• The speaker finds a discriminating category c for
  the topic.
• It conveys the associated word form f to the
  hearer.
• The hearer interprets the word form, finds the
  associated category c and points out the topic.

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GG dynamics

• Game can fail at many points
• Speaker
• No discriminating category.
• No associated word form.
• Hearer
• Does not know the word form.
• Fails to point out the topic.
• Opportunity to extend and adapt categories and
  lexicon.

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Evolutionary models

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

Colour categories
Learned
Evolved
Without
Individual learning
Genetic evolution
Language
Genetic evolution under linguistic pressure
With
Cultural learning
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Genetic operator

• Agents are endowed with the ability to have a
  categorical repertoire (!).
• Categories are genetically evolved, instead of a
  genetic code.
• Asexual reproduction.

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Genetic operator

• Mutation
• Adding a category
• Removing a category
• Extending a category
• Restricting a category
• Fitness measure
• Discriminative success

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Results without communication

• Learning categories
• Genetic evolution of categories

Colour categories
Learned
Evolved
Without
Individual learning
Genetic evolution
Language
Genetic evolution under linguistic pressure
With
Cultural learning
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Individual learning

• Discriminative successN10, lOl3, D50

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Individual learning

• Category variance

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Individual learning

• Categories of two agents on Munsell chart
• There is no sharing across populations

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Genetic evolution

• Discriminative successN10, IOI3, D50

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Genetic evolution

• Category variance

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Genetic evolution

• Categories of two agents on Munsell chart.
• There is no sharing across populations.

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Summary

• Without communication
• Both approaches attain a categorical repertoire
  functional for discrimination.
• Individual learning leads to a certain amount of
  sharing, but no 100 coherence.
• Genetic evolution leads to complete sharing.
• Both approaches do not arrive at sharing across
  populations.
• Timescale different.

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Results with communication

• Cultural learning.

Colour categories
Learned
Evolved
Without
Individual learning
Genetic evolution
Language
Genetic evolution under linguistic pressure
With
Cultural learning
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Cultural learning

• Discriminative successN10, IOI3,D50

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Cultural learning

• Communicative success

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Cultural learning

• Category variance

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Cultural learning

• Categories of two agents on Munsell chart.
• There is no sharing across populations.

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Influence of communication on coherence
ratio
Without language
With language
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Influence of communication on coherence

• Individual learning Cultural learning

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Discussion on cultural learning

• Communication forces sharing in a cultural
  learning through positive feedback between
  category formation and communication.
• Communication has a causal influence on category
  formation.
• First learning categories, and then lexicalising
  does allow communication.
• Communicative success never 100. In accordance
  with anthropological experiments (Stefflre et al,
  1966).
• Nature of categories is stochastic. Not in accord
  with Berlin and Kay (1969).
• Model possibly does not contain enough ecological
  and biological constraints.

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Summary

• Computer simulations on the acquisition of colour
  categories.
• Extreme positions to allow a clear discussion.
• Both cultural learning and genetic evolution seem
  to be good candidates for explaining sharedness.
• Results and recent literature lend support for
  culturalism.

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• http//arti.vub.ac.be/tony

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Critical notes

• A computer simulation requires assumptions and
  models.Though results confirm the choices made,
  the assumptions might be wrong.
• Weak ecological and biological constraints.
  Stronger constraints might explain phenomena now
  unaccounted for.
• Colour has been taken in isolation.

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Contributions

• Provide food for thought for disciplines other
  than AI.
• Formalisation of an interdisciplinary and often
  rhetoric debate.
• Computer simulations of real world phenomena.
• Simulations with continuous meaning
  representation.
• A computational representation of natural
  categories.

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Artificial intelligence

• Two kinds of AI

Constructing intelligenceBuilding artefacts
which display adaptive or even intelligent
behaviour.
Understanding intelligenceStudying complex
behaviour through constructing artificial systems.
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Situating the research

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

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

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The origins and evolution of language

• Different lines of attack
• Linguistics
• Ethology
• Anthropology
• Artificial intelligence.

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The origins and evolution of language

• Computers as a tool for investigating linguistic
  phenomena
• Uses models and simulations.
• Allows investigation of mechanisms difficult or
  impossible to study by other disciplines.
• Allows investigation of large parameter spaces.
• Provides no definite answers, but theories which
  are referred back to observational disciplines.

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Various evidence for universalism

• Opponent neural response to chromatic stimuli
• Explains basic colour categories (Kay McDaniel,
  1978).
• Research on infants
• Infants possess colour categories for fundamental
  colours (Bornstein et al., 1976).

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GG scenario

• Two agents are selected one speaker, one hearer.
• A context is presented to both agents, the
  speaker knows the topic.
• The speaker finds a discriminating category c for
  the topic.
• It conveys the associated word form f to the
  hearer.
• The hearer interprets the word form, finds the
  associated category c and points out the topic.

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Guessing game

• Initialise the game

speaker
hearer
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Guessing game

• Speaker discriminates topic

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Guessing game
Speaker finds word form associated with category
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Guessing game

• Speaker conveys word form

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Guessing game

• Hearer interprets word form

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Guessing game

• Hearer non-verbally points at topic

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Chromatic input

• Spectral power distributions of actual chips
• Presented in aperture mode.
• Constant adaptation state.
• No commitment to any specific device.

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Individual learning

• Changing environment

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Genetic evolution

• Changing environment

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Berlin and Kay, results

• Evolutionary order of basic colour terms.
• A language has at most 11 BCTs.
• Basic colour categories are genetically
  determined.

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Cultural learning

• Number of categories

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Individual learning

• Number of categories

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Genetic evolution

• Number of categories

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Genetic evolution with communication

• Number of categories

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Genetic evolution with communication

• Discriminative successN20, IOI3,
  D50

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Genetic evolution with communication

• Communicative success

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Genetic evolution with communication

• Category variance

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Genetic evolution with communication

• Categories of two agents on Munsell chart.
• There is no sharing across populations.

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Discussion on genetic evolution with communication

• Categories still evolve under communicative
  pressure.
• Sharedness within population arises through
  propagation of genetic material.
• Not shared cross-culturally.
• Time-scale is radically different from cultural
  learning.
• Again, model possibly does not contain enough
  ecological and biological constraints.

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Summary

• Learning with communication
• Both approaches attain a categorical repertoire
  and lexicon.
• Both arrive at shared categories in the
  population.
• Both do not arrive at shared categories across
  populations.
• No human-like categories.