Are colour categories innate or learned? Insights from computational modelling

1. Are colour categories innate or learned? Insights from computational modelling Tony Belpaeme Artificial Intelligence Lab Vrije Universiteit Brussel Tony Belpaeme VUB AI-lab

2. 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. Tony Belpaeme VUB AI-lab

3. Perceptual categories • The origins of perceptual categories • Facial expressions • Odour • Colour • Debate on the origins of perceptual categories Tony Belpaeme VUB AI-lab

4. 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. Tony Belpaeme VUB AI-lab

5. 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 Tony Belpaeme VUB AI-lab

6. 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… Tony Belpaeme VUB AI-lab

7. 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. Tony Belpaeme VUB AI-lab

8. 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? Tony Belpaeme VUB AI-lab

9. Support for universalism • For example • Berlin and Kay (1969). • Rosch (1971, 1972). Tony Belpaeme VUB AI-lab

10. 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. Tony Belpaeme VUB AI-lab

11. Berlin and Kay, results Tony Belpaeme VUB AI-lab

12. 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. Tony Belpaeme VUB AI-lab

13. Support for relativism • Brown and Lenneberg (1954) • Positive correlation between ‘codability’ of colour terms and memorising colours. • Davidoff et al. (1999) • Reimplemented Rosch’s 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). Tony Belpaeme VUB AI-lab

14. 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 Summary Tony Belpaeme VUB AI-lab

15. Colour categories Evolved Learned Without Individual learning Genetic evolution Language Genetic evolution under linguistic pressure Cultural learning With Four experiments • Goal • Study positions through computer simulations. • Advance claims based on these simulations. Tony Belpaeme VUB AI-lab

16. Experimental setup • An individual is modelled by an agent • Perception • Categorisation • Lexicalisation • Communication • Agents are placed in a population Tony Belpaeme VUB AI-lab

17. Overview of an agent Tony Belpaeme VUB AI-lab

18. Perception • Stimuli are presented as spectral power distributions • Modelling chromatic perception • A model is needed • Suitable for modelling categories on Tony Belpaeme VUB AI-lab

19. Perception • CIE L*a*b* space • Perceptually equidistant space. • Similarity function exists. • Straightforward computation. • Suitable for defining colour categories on (Lammens, 1994). Tony Belpaeme VUB AI-lab

20. 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’ Tony Belpaeme VUB AI-lab

21. 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 Tony Belpaeme VUB AI-lab

22. Adaptive network Tony Belpaeme VUB AI-lab

23. f1 s1 s2 f2 c sn fn 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. Tony Belpaeme VUB AI-lab

24. 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 Tony Belpaeme VUB AI-lab

25. 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 Tony Belpaeme VUB AI-lab

26. 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? Tony Belpaeme VUB AI-lab

27. DG dynamics • If the discrimination game fails, this provides opportunity to create new or adapt old categories. Tony Belpaeme VUB AI-lab

28. Guessing game • Two agents are selected for playing a GG. • Serves as task to generate a categorical repertoire and associated lexicalisations. Tony Belpaeme VUB AI-lab

29. 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. Tony Belpaeme VUB AI-lab

30. 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. Tony Belpaeme VUB AI-lab

31. 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 Tony Belpaeme VUB AI-lab

32. Genetic operator • Agents are endowed with the ability to have a categorical repertoire (!). • Categories are genetically evolved, instead of a ‘genetic code’. • Asexual reproduction. Tony Belpaeme VUB AI-lab

33. Genetic operator • Mutation • Adding a category • Removing a category • Extending a category • Restricting a category • Fitness measure • Discriminative success Tony Belpaeme VUB AI-lab

34. 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 Tony Belpaeme VUB AI-lab

35. Individual learning • Discriminative successN=10, lOl=3, D=50 Tony Belpaeme VUB AI-lab

36. Individual learning • Category variance Tony Belpaeme VUB AI-lab

37. Individual learning • Categories of two agents on Munsell chart • There is no sharing across populations Tony Belpaeme VUB AI-lab

38. Genetic evolution • Discriminative successN=10, IOI=3, D=50 Tony Belpaeme VUB AI-lab

39. Genetic evolution • Category variance Tony Belpaeme VUB AI-lab

40. Genetic evolution • Categories of two agents on Munsell chart. • There is no sharing across populations. Tony Belpaeme VUB AI-lab

41. 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. Tony Belpaeme VUB AI-lab

42. Results with communication • Cultural learning. Colour categories Learned Evolved Without Individual learning Genetic evolution Language Genetic evolution under linguistic pressure With Cultural learning Tony Belpaeme VUB AI-lab

43. Cultural learning • Discriminative successN=10, IOI=3,D=50 Tony Belpaeme VUB AI-lab

44. Cultural learning • Communicative success Tony Belpaeme VUB AI-lab

45. Cultural learning • Category variance Tony Belpaeme VUB AI-lab

46. Cultural learning • Categories of two agents on Munsell chart. • There is no sharing across populations. Tony Belpaeme VUB AI-lab

47. Influence of communication on coherence ratio Without language With language Tony Belpaeme VUB AI-lab

48. Influence of communication on coherence Individual learning Cultural learning Tony Belpaeme VUB AI-lab

49. 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. Tony Belpaeme VUB AI-lab

50. 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. Tony Belpaeme VUB AI-lab