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10 - Genetic Biases in Language: Computer Models and Experimental Approaches

from Part IV - Social and Language Evolution

Published online by Cambridge University Press:  30 November 2017

Rick Janssen
Affiliation:
Language and Genetics Department, Max Planck Institute for Psycholinguistics, The Netherlands
Dan Dediu
Affiliation:
Language and Genetics Department,Max Planck Institute for Psycholinguistics, The Netherlands
Thierry Poibeau
Affiliation:
Centre National de la Recherche Scientifique (CNRS), Paris
Aline Villavicencio
Affiliation:
Universidade Federal do Rio Grande do Sul, Brazil
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Summary

Abstract

Computer models of cultural evolution have shown language properties emerging on interacting agents with a brain that lacks dedicated, nativist language modules. Notably, models using Bayesian agents provide a precise specification of (extra-)liguististic factors (e.g., genetic) that shape language through iterated learning (biases on language), and demonstrate that weak biases get expressed more strongly over time (bias amplification). Other models attempt to lessen assumption on agents’ innate predispositions even more, and emphasize self-organization within agents, highlighting glossogenesis (the development of language from a nonlinguistic state). Ultimately however, one also has to recognize that biology and culture are strongly interacting, forming a coevolving system. As such, computer models show that agents might (biologically) evolve to a state predisposed to language adaptability, where (culturally) stable language features might get assimilated into the genome via Baldwinian niche construction. In summary, while many questions about language evolution remain unanswered, it is clear that it is not to be completely understood from a purely biological, cognitivist perspective. Language should be regarded as (partially) emerging on the social interactions between large populations of speakers. In this context, agent models provide a sound approach to investigate the complex dynamics of genetic biasing on language and speech.

Introduction

Biasing Language

In this chapter, we argue not only that the best approach to understanding the origins and present-day diversity of language is rooted in evolutionary theory, but also that extra-linguistic factors, more specifically biological ones in our genes, may play an important role in shaping language. Likewise, these factors do not act in a void, but interact with multiple constraints and affordances on different scales in parallel. So-called cultural evolution of language (Section 10.1.2) must thus be seen in a rich context (partially) molded by the biological and cognitive entities that ultimately acquire, use, and transmit language – us. Important factors in this context are therefore represented not only by the brain – it has been recognized for a while now that the brain indeed shapes language (Christiansen & Chater 2008) – but also by the anatomy and physiology of the vocal tract and hearing organs.

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Publisher: Cambridge University Press
Print publication year: 2018

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References

Baldwin, J. M. (1896), ‘A new factor in evolution’, American naturalist pp. 536–553.
Ball, P. (1999), The self-made tapestry: Pattern formation in nature, Oxford University Press.Google Scholar
Baronchelli, A., Chater, N., Christiansen, M. H. & Pastor-Satorras, R. (2013), ‘Evolution in a changing environment’, PloS one 8(1), e52742.CrossRefGoogle Scholar
Baronchelli, A., Chater, N., Pastor-Satorras, R. & Christiansen, M. H. (2012), ‘The biological origin of linguistic diversity’, PloS one 7(10), e48029.CrossRefGoogle Scholar
Bernardo, J. M. & Smith, A. F. (2009), Bayesian theory, Vol. 405, John Wiley & Sons.Google Scholar
Bouckaert, R., Lemey, P., Dunn, M., Greenhill, S. J., Alekseyenko, A. V., Drummond, A. J., Gray, R. D., Suchard, M. A. & Atkinson, Q. D. (2012), ‘Mapping the origins and expansion of the Indo-European language family’, Science 337(6097), 957– 960.CrossRefGoogle Scholar
Burkett, D. and Griffiths, T. L. (2010), ‘Iterated learning of multiple languages from multiple teachers’, The evolution of language: Proceedings of Evolang pp. 58–65.
Butcher, A. (2006), Australian Aboriginal languages: Consonant-salient phonologies and the ‘place-of-articulation imperative’, New York and Hove: Psychology Press, pp. 187–210.Google Scholar
Calvin, W. H. (2002), A brain for all seasons: Human evolution and abrupt climate change, University of Chicago Press.Google Scholar
Campbell, L. & Poser, W. J. (2008), Language classification: History and method, Cambridge University Press.CrossRefGoogle Scholar
Carroll, S. B. (2005), Endless forms most beautiful: The new science of evo devo and the making of the animal kingdom, number 54, W.W. Norton & Company.Google Scholar
Chater, N., Reali, F. & Christiansen, M. H. (2009), ‘Restrictions on biological adaptation in language evolution’, Proceedings of the National Academy of Sciences 106(4), 1015–1020.CrossRefGoogle Scholar
Chomsky, N. (1965), Aspects of the theory of syntax, number 11, MIT press.Google Scholar
Chomsky, N. (1986), Knowledge of language: Its nature, origin, and use, Greenwood Publishing Group.Google Scholar
Christiansen, M. H. & Chater, N. (2008), ‘Language as shaped by the brain’, Behavioral and Brain Sciences 31(05), 489–509.CrossRefGoogle Scholar
Croft, W. (2000), Explaining language change: An evolutionary approach, Pearson Education.Google Scholar
Dávid-Barrett, T. & Dunbar, R. (2013), ‘Processing power limits social group size: Computational evidence for the cognitive costs of sociality’, Proceedings of the Royal Society B: Biological Sciences 280(1765).CrossRefGoogle Scholar
Dawkins, R. (1976), The selfish gene, Oxford University Press.Google Scholar
de Boer, B. (2000a), ‘Emergence of vowel systems through self-organisation’, AI Communications 13(1), 27–39.Google Scholar
de Boer, B. (2000b), ‘Self-organization in vowel systems’, Journal of Phonetics 28(4), 441–465.CrossRefGoogle Scholar
de Boer, B. & Fitch, W. T. (2010), ‘Computer models of vocal tract evolution: An overview and critique’, Adaptive Behavior 18(1), 36–47.CrossRefGoogle Scholar
de Boer, B. & Zuidema, W. (2010), ‘Multi-agent simulations of the evolution of combinatorial phonology’, Adaptive Behavior 18(2), 141–154.CrossRefGoogle Scholar
Deacon, T. (1997), The symbolic species: The co–evolution of language and the brain, number 202, WW Norton & Company.Google Scholar
Dediu, D. (2008), ‘The role of genetic biases in shaping the correlations between languages and genes’, Journal of Theoretical Biology 254(2), 400–407.CrossRefGoogle Scholar
Dediu, D. (2009), ‘Genetic biasing through cultural transmission: Do simple Bayesian models of language evolution generalise?’, Journal of Theoretical Biology 259(3), 552–561.CrossRefGoogle Scholar
Dediu, D. (2011), ‘Are languages really independent from genes? If not, what would a genetic bias affecting language diversity look like?’, Human Biology 83(2), 279– 296.CrossRefGoogle ScholarPubMed
Dediu, D. & Levinson, S. C. (2013), ‘On the antiquity of language: The reinterpretation of Neandertal linguistic capacities and its consequences’, Frontiers in Psychology 4.CrossRefGoogle Scholar
Dunn, M., Greenhill, S. J., Levinson, S. C. & Gray, R. D. (2011), ‘Evolved structure of language shows lineage-specific trends in word-order universals’, Nature 473(7345), 79–82.CrossRefGoogle Scholar
Everett, C., Blasi, D. E. & Roberts, S. G. (2015), ‘Climate, vocal folds, and tonal languages: Connecting the physiological and geographic dots’, Proceedings of the National Academy of Sciences 112(5), 1322–1327.CrossRefGoogle Scholar
Farrell, S., Wagenmakers, E.-J. & Ratcliff, R. (2006), ‘1/f noise in human cognition: Is it ubiquitous, and what does it mean?’, Psychonomic Bulletin & Review 13(4), 737– 741.CrossRefGoogle Scholar
Fehér, O., Wang, H., Saar, S., Mitra, P. P. and Tchernichovski, O. (2009), ‘De novo establishment of wild-type song culture in the zebra finch’, Nature 459(7246), 564– 568.CrossRefGoogle Scholar
Ferdinand, V. and Zuidema,W. (2009), Thomas' theoremmeets Bayes' rule: A model of the iterated learning of language, in ‘Proceedings of the 31st Annual Conference of the Cognitive Science Society’, Cognitive Science Society Austin, TX, pp. 1786–1791.
Fisher, S. E. (2006), ‘Tangled webs: Tracing the connections between genes and cognition’, Cognition 101(2), 270–297.CrossRefGoogle Scholar
Fodor, J. A. (1983), The modularity of mind: An essay on faculty psychology, MIT Press.Google Scholar
Gould, S. J. & Vrba, E. S. (1982), ‘Exaptation – a missing term in the science of form’, Paleobiology, pp. 4–15.CrossRef
Gray, R. D. & Atkinson, Q. D. (2003), ‘Language-tree divergence times support the Anatolian theory of Indo-European origin’, Nature 426(6965), 435–439.CrossRefGoogle Scholar
Griffiths, T. L. and Kalish, M. L. (2007), ‘Language evolution by iterated learning with bayesian agents’, Cognitive Science 31(3), 441–480.CrossRefGoogle Scholar
Hamilton, W. D. (1963), ‘The evolution of altruistic behavior’, American Naturalist pp. 354–356.Google Scholar
Hanke, D. (2004), ‘Teleology: The explanation that bedevils biology’, Explanations: Styles of Explanation in Science, pp. 143–155.
Hebb, D. O. (1949), The organization of behavior: A neuropsychological approach, John Wiley & Sons.Google Scholar
Henrich, J. & McElreath, R. (2003), ‘The evolution of cultural evolution’, Evolutionary Anthropology: Issues, News, and Reviews 12(3), 123–135.CrossRefGoogle Scholar
Hinton, G. and Nowlan, S. (1987), ‘How learning can guide evolution’, Complex Systems 1(1), 495–502.Google Scholar
Hockett, C. (1960), ‘The origin of speech’, Scientific American 203, 88–96.CrossRefGoogle Scholar
Johnson, K. (2005), Speaker normalization in speech perception, in The handbook of speech perception, John Wiley & Sons, pp. 363–389.Google Scholar
Kirby, S., Cornish, H. & Smith, K. (2008), ‘Cumulative cultural evolution in the laboratory: An experimental approach to the origins of structure in human language’, Proceedings of the National Academy of Sciences 105(31), 10681–10686.CrossRefGoogle Scholar
Kirby, S., Dowman, M. and Griffiths, T. L. (2007), ‘Innateness and culture in the evolution of language’, Proceedings of the National Academy of Sciences 104(12), 5241–5245.CrossRefGoogle Scholar
Kirby, S. & Hurford, J. R. (2002), The emergence of linguistic structure: An overview of the iterated learning model, in Simulating the evolution of language, Springer, pp. 121–147.Google Scholar
Kohonen, T. (1982), ‘Self-organized formation of topologically correct feature maps’, Biological Cybernetics 43(1), 59–69.CrossRefGoogle Scholar
Kohonen, T. (2001), Self-organizing maps, Vol. 30, Springer.CrossRefGoogle Scholar
Kröger, R. H. & Biehlmaier, O. (2009), ‘Space-saving advantage of an inverted retina’, Vision Research 49(18), 2318–2321.CrossRefGoogle Scholar
Kruschke, J. K. (1992), ‘Alcove: An exemplar-based connectionist model of category learning.’, Psychological Review 99(1), 22.CrossRefGoogle Scholar
Ladd, D. R., Dediu, D. & Kinsella, A. R. (2008), ‘Languages and genes: Reflections on biolinguistics and the nature-nurture question’, Biolinguistics 2(1), 114–126.Google Scholar
Ladefoged, P. (1984), Out of chaos comes order? Physical, biological, and structural patterns in phonetics in A., Cohen & M., van den Broecke, eds, Proceedings of the Tenth International Congress of Phonetic Sciences, Foris Publications: Dordrecht, Holland, pp. 83–95.Google Scholar
Ladefoged, P. & Maddieson, I. (1998), ‘The sounds of the world's languages’, Language 74(2), 374–376.Google Scholar
Laland, K. N., Odling-Smee, J. & Myles, S. (2010), ‘How culture shaped the human genome: Bringing genetics and the human sciences together’, Nature Reviews Genetics 11(2), 137–148.CrossRefGoogle Scholar
Levinson, S. C. & Gray, R. D. (2012), ‘Tools from evolutionary biology shed new light on the diversification of languages’, Trends in Cognitive Sciences 16(3), 167– 173.CrossRefGoogle Scholar
Lin, C. & Shu, F. H. (1964), ‘On the spiral structure of disk galaxies.’, The Astrophysical Journal 140, 646.CrossRefGoogle Scholar
Maddieson, I. (1984), Patterns of sounds, Cambridge University Press.CrossRefGoogle Scholar
Mameli, M. & Bateson, P. (2006), ‘Innateness and the sciences’, Biology and Philosophy 21(2), 155–188.CrossRefGoogle Scholar
Mayley, G. (1996), The evolutionary cost of learning, in Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pp. 458–467.
Mesoudi, A. & Whiten, A. (2008), ‘The multiple roles of cultural transmission experiments in understanding human cultural evolution’, Philosophical Transactions of the Royal Society B: Biological Sciences 363(1509), 3489–3501.CrossRefGoogle Scholar
Miller, G. (2001), ‘The mating mind: How sexual choice shaped the evolution of human nature’, Psycoloquy 12(8), 1–15.Google Scholar
Odling-Smee, F. J., Laland, K. N. & Feldman, M. W. (2003), Niche construction: The neglected process in evolution, number 37, Princeton University Press.Google Scholar
Okasha, S. (2006), Evolution and the Levels of Selection, Vol. 16, Clarendon Press Oxford.CrossRefGoogle Scholar
Ostrom, J. H. (1976), ‘Archaeopteryx and the origin of birds’, Biological Journal of the Linnean Society 8(2), 91–182.CrossRefGoogle Scholar
Oudeyer, P.-Y. (2005a), ‘The self-organization of combinatoriality and phonotactics in vocalization systems’, Connection Science 17(3-4), 325–341.CrossRefGoogle Scholar
Oudeyer, P.-Y. (2005b), ‘The self-organization of speech sounds’, Journal of Theoretical Biology 233(3), 435–449.CrossRefGoogle Scholar
Pagel, M., Atkinson, Q. D. & Meade, A. (2007), ‘Frequency of word-use predicts rates of lexical evolution throughout Indo-European history’, Nature 449(7163), 717–720.CrossRefGoogle Scholar
Perfors, A. (2012), ‘Bayesian models of cognition: What's built in after all?’, Philosophy Compass 7(2), 127–138.CrossRefGoogle Scholar
Perfors, A. & Navarro, D. J. (2011), Language evolution is shaped by the structure of the world, in Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Cognitive Science Society.
Perfors, A. and Navarro, D. J. (2014), ‘Language evolution can be shaped by the structure of the world’, Cognitive Science 38(4), 775–793.CrossRefGoogle Scholar
Pigliucci, M. (2007), ‘Do we need an extended evolutionary synthesis?’, Evolution 61(12), 2743–2749.CrossRefGoogle Scholar
Pinker, S. & Bloom, P. (1990), ‘Natural language and natural selection’, Behavioral and Brain Sciences 13(4), 707–727.CrossRefGoogle Scholar
Richerson, P. J. & Boyd, R. (2008), Not by genes alone: How culture transformed human evolution, University of Chicago Press.Google Scholar
Richerson, P. J., Boyd, R. & Henrich, J. (2010), ‘Gene-culture coevolution in the age of genomics’, Proceedings of the National Academy of Sciences 107(Supplement 2), 8985–8992.CrossRefGoogle Scholar
Richerson, P. J. & Christiansen, M. H. (2013), Cultural evolution: Society, technology, language, and religion, MIT Press.CrossRefGoogle Scholar
Schwartz, J.-L., Boë, L.-J., Vallée, N. & Abry, C. (1997), ‘Major trends in vowel system inventories’, Journal of Phonetics 25(3), 233–253.CrossRefGoogle Scholar
Shannon, C. E. (1948), The mathematical theory of communication, University of Illinois Press.Google Scholar
Smith, K. (2001), The evolution of learning mechanisms supporting symbolic communication, in CogSci2001, the 23rd Annual Conference of the Cognitive Science Society, Citeseer.
Smith, K. (2009), Iterated learning in populations of bayesian agents, in Proceedings of the 31st annual conference of the cognitive science society, Austin, TX: Cognitive Science Society, pp. 697–702.
Smith, K. and Kirby, S. (2008), ‘Cultural evolution: Implications for understanding the human language faculty and its evolution’, Philosophical Transactions of the Royal Society B: Biological Sciences 363(1509), 3591–3603.CrossRefGoogle Scholar
Smith, K., Tamariz, M. & Kirby, S. (2013), Linguistic structure is an evolutionary tradeoff between simplicity and expressivity, in Proceedings of Cogsci 2013, pp. 1348– 1353.
Turney, P. (1996),Myths and legends of the Baldwin Effect, in Proceedings of the Workshop on Evolutionary Computing and Machine Learning at the 13th International Conference on Machine Learning, pp. 135–142.
Verhoef, T. & de Boer, B. (2011), Cultural emergence of feature economy in an artificial whistled language, in Proceedings of the 17th international congress of phonetic sciences. Hong Kong: City University of Hong Kong, pp. 2066–2069.
Verhoef, T., de Boer, B. & Kirby, S. (2012), Holistic or synthetic protolanguage: Evidence from iterated learning of whistled signals, in The evolution of language: Proceedings of the 9th international conference (EVOLANG9), World Scientific, pp. 368–375.
Waddington, C. H. (1942), ‘Canalization of development and the inheritance of acquired characters’, Nature 150(3811), 563–565.CrossRefGoogle Scholar
Williams, G. C. (1966), Adaptation and natural selection: A critique of some current evolutionary thought, Princeton University Press.Google Scholar
Wilson, D. S. & Wilson, E. O. (2008), ‘Evolution “for the good of the group”’, American Scientist 96(5), 380–389.Google Scholar
Wynne-Edwards, V. C. (1962), Animal dispersion in relation to social behaviour, Hafner Pub. Co. Google Scholar
Wynne-Edwards, V. C. (1986), Evolution through group selection, Blackwell Scientific.Google Scholar
Zipf, G. K. (1949), Human behavior and the principle of least effort, Addison-Wesley.Google Scholar
Zuidema, W. & de Boer, B. (2009), ‘The evolution of combinatorial phonology’, Journal of Phonetics 37(2), 125–144.CrossRefGoogle Scholar
Zwicker, E. (1961), ‘Subdivision of the audible frequency range into critical bands (Frequenzgruppen)’, The Journal of the Acoustical Society of America 33(2), 248–248.CrossRefGoogle Scholar

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