Talk at Institut Jean Nicod on 6 October 2010
- 1. Phylogenetic methods of language diversification Robin J. Ryder CEREMADE – Paris Dauphine and CREST – ENSAE Work done at the Department of Statistics, University of Oxford, under the supervision of Geoff K. Nicholls www.slideshare.net/robinryder
- 3. What to expect Past attempts: Swadesh and glottochronology
- 4. Background from Evolutionary Biology
- 5. Modern methods (a sample) + criticisms
- 6. Application to dating of Proto-Indo-European
- 7. Before we start... Statistics: additional insight alongside the comparative method
- 8. None of these models represent the truth. Nonetheless, they can provide us with information.
- 10. What Statistics add Quantitative estimates
- 12. Model testing
- 13. Automatization
- 14. Swadesh and glottochronology 200/100 word list
- 15. Compares 2 languages (c=fraction of shared cognates)
- 16. Assumes r=fraction of shared cognates after 1000 years constant for all languages (86%)
- 17. Infers age t of Most Recent Common Ancestor
- 18. I you (singular) he we you (plural) they this that here there who what where when how not all many some few other one two three four five big long wide thick heavy small short narrow thin woman man (adult male) man (human being) child wife husband mother father animal fish bird dog louse snake worm tree forest stick fruit seed leaf root bark flower grass rope skin meat blood bone fat (n.) egg horn tail feather hair head ear eye nose mouth tooth tongue fingernail foot leg knee hand wing belly guts neck back breast heart liver drink eat bite suck spit vomit blow breathe laugh see hear know think smell fear sleep live die kill fight hunt hit cut split stab scratch dig swim fly (v.) walk come lie sit stand turn fall give hold squeeze rub wash wipe pull push throw tie sew count say sing play float flow freeze swell sun moon star water rain river lake sea salt stone sand dust earth cloud fog sky wind snow ice smoke fire ashes burn road mountain red green yellow white black night day year warm cold full new old good bad rotten dirty straight round sharp dull smooth wet dry correct near far right left at in with and if because name
- 19. Bergsland & Vogt (1962) Found different rates for different pairs of languages: Old Norse and Icelandic, Georgian and Mingrelian, Armenian and Old Armenian
- 21. Sankoff (1973): sample selection bias, no estimation of uncertainty
- 22. Fair criticism
- 23. Bad observation protocol from Swadesh
- 24. Does not apply so much to modern methods
- 25. Genetics 101 Genetic information is stored in DNA
- 26. DNA uses 4 letters: A, C, T and G
- 27. DNA transmission
- 28. DNA transmission
- 29. DNA transmission
- 30. DNA transmission
- 31. Phylogenetics A: TTGCAATCCG B: TAGCAATCCG C: CTGCAATACG D: CTGCAATAGA
- 32. Compare different possible trees
- 34. Charles Darwin «The formation of different languages and of distinct species, and the proofs that both have developed through a gradual process, are curiously parallel... We find in distinct languages striking homologies due to community of descent, and analogies due to a similar process of formation.»
- 35. Similarities between genes and languages As in genetics, a tree model is relevant for certain types of linguistic data. Characteristic Genetics Linguistics Discrete units Genes, nucleotides Lexical, morpholosyntactic and/or phonological traits Transmission Transcription Learning, imitation Horizontal Viruses, hybridization... Borrowing, creoles... transmission Change Point mutation, indels... Vowel shift, innovations, word loss
- 38. Internal ages
- 39. Age of the root: 6000-6500 BP or 8000-9500 BP?
- 41. Core vocabulary 100 or 200 meanings, present in almost all languages : bird, hand, to eat, red...
- 42. Borrowing is possible (non-tree-like change), but:
- 43. “ Easy” to detect
- 44. Uncommon
- 45. Does not introduce systematic bias
- 46. Data coding Old English: stierfþ Old High German: stirbit , touwit Avestan: miriiete Old Church Slavonic: umĭretŭ Latin: moritur Oscan: ? Cognacy classes: 1. {stierfþ, stirbit} 2. {touwit} 3. {miriiete, umĭretŭ, moritur}
- 47. Data coding (2) Specialist linguists make cognacy judgments
- 49. Only do this for languages which are known to be related
- 51. Core vocabulary (Swadesh 100 or 200)
- 52. Two data sets
- 53. Dyen et al. (1997): 87 languages, mostly modern
- 54. Ringe et al. (2002): 24 languages, mostly ancient
- 55. Constraints Constraints on parts of the topology
- 56. Constraints on some internal ages
- 57. We use these constraints to infer rates and other ages
- 59. Using models from biology First attempts: Jordan & Gray (2000), Gray & Atkinson (2003)
- 60. Biological models make assumptions which do not apply to languages
- 62. Gray and Atkinson (2003); tree of 87 Indo-European languages obtained using lexical data and the mrbayes package (Huelsenbeck & Ronquist).
- 63. Selection of criticisms Multiple births
- 64. Missing data
- 66. Description of the model (1) Traits are born at rate λ
- 67. Trait instances die at rate μ
- 68. λ and μ are constants
- 69. Description of the model (2) Catastrophes occur at rate ρ
- 70. At a catastrophe, each trait dies with probability κ and Poiss(ν) traits are born.
- 71. λ/μ=ν/κ: the number of traits is constant on average.
- 72. Description of the model (3) Observation model: each data point (0s and 1s) is missing with probability ξ
- 73. Some traits are not observed and are therefore deleted from the data
- 80. Statistical inference (MCMC) Fit the model to the data
- 81. Trees that make the data likely
- 82. Move across the tree space; try thousands of possible trees
- 83. Obtain a sample of plausible trees and dates
- 84. Samples weighted by quality of fit to data
- 85. Synthetic data True tree, ~40 words/language Consensus tree
- 86. Synthetic data (2) Death rate (μ)
- 87. Influence of borrowing True tree, ~40 words/language Borrowing: 10% Consensus tree
- 88. Influence of borrowing (2) Consensus tree True tree, ~40 words/language Borrowing: 50%
- 89. Influence of borrowing (3) Topology is reconstructed correctly
- 90. Dates are underestimated for high levels of borrowing Root age Death rate ( μ) Borrowing: 50%
- 91. Detecting borrowing Confirmed: hardly any borrowing!
- 104. Cross-validation
- 105. Root age
- 106. Conclusions Strong support for the Anatolian hypothesis: root age around 8000BP. No support for the Kurgan hypothesis.
- 107. Applicable to a variety of linguistic and cultural data sets
- 108. TraitLab: it's free!
- 109. Questions otázky spørgsmåler vragen questions Fragen domande pytania questões întrebări вопросы vprašanja preguntes preguntas frågor vrae spurningar quaestiones ερωτήσεις въпроси kesses spørsmåler kláusimai запитанні سوال प्रश्न cwestiwnau
- 110. References Ryder & Nicholls (2011), Missing data in a stochastic Dollo model for cognate data, and its application to the dating of Proto-Indo-European, JRSS C
- 111. Nicholls (2008), Horses or farmers? The tower of Babel and confidence in trees, Significance
- 112. Nicholls & Gray (2008), Dated ancestral trees from binary trait data and their application to the diversification of languages, JRSS B
- 113. Gray & Atkinson (2003), Language-tree divergence times support the Anatolian theory of Indo-European origin, Nature
- 114. Gray & Jordan (2000), Language trees support the express-train sequence of Austronesian expansion
- 115. Bergsland & Vogt (1962), On the validity of glottochronology, Current Anthropology
- 116. Sankoff (1973), Mathematical developments in lexicostatistic theory, Current Trends in Linguistics
- 117. Ryder (2010), Phylogenetic models of language diversification, DPhil thesis, University of Oxford