Recently, a growing number of studies have considered the role of language in the social transmission of tool-making skill during human evolution. In this article, I address this question in light of a new theory of language and its evolution, and review evidence from anthropology and experimental archaeology related to it. I argue that the specific function of language—the instruction of imagination—is not necessary for the social transmission of tool-making skill. Evidence from hunter-gatherer ethnographies suggests that social learning relies mainly on observation, participation, play, and experimentation. Ethnographies of traditional stone cultures likewise describe group activities with simple, context-bound interactions embedded in the here and now. Experiments comparing gestural and verbal teaching of tool-making skills also demonstrate that language is not necessary for that process. I conclude that there is no convincing evidence that language played an important role in the social transmission of lithic technology, although the possibility that linguistic instruction was involved as part of the social interactions accompanying tool-making cannot be excluded.
{"title":"Is Language Necessary for the Social Transmission of Lithic Technology?","authors":"Dorothy O. Shilton","doi":"10.1093/JOLE/LZZ004","DOIUrl":"https://doi.org/10.1093/JOLE/LZZ004","url":null,"abstract":"Recently, a growing number of studies have considered the role of language in the social transmission of tool-making skill during human evolution. In this article, I address this question in light of a new theory of language and its evolution, and review evidence from anthropology and experimental archaeology related to it. I argue that the specific function of language—the instruction of imagination—is not necessary for the social transmission of tool-making skill. Evidence from hunter-gatherer ethnographies suggests that social learning relies mainly on observation, participation, play, and experimentation. Ethnographies of traditional stone cultures likewise describe group activities with simple, context-bound interactions embedded in the here and now. Experiments comparing gestural and verbal teaching of tool-making skills also demonstrate that language is not necessary for that process. I conclude that there is no convincing evidence that language played an important role in the social transmission of lithic technology, although the possibility that linguistic instruction was involved as part of the social interactions accompanying tool-making cannot be excluded.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JOLE/LZZ004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47620917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Every language produces some type of verse in the form of songs, poems or nursery rhymes, which can be analysed as a layer of words set to a template (e.g. a tune, a poetic metre). Verse templates typically consist of hierarchically organised sections: songs are made up of stanzas, divided into lines, containing bars, etc. We hypothesise that this kind of patterns may emerge in the process of cultural transmission; unstructured sound sequences impose a challenge to short-termmemory, but chunking the input makes it easier to parse and reproduce the sequences accurately. In order to test this hypothesis, we have run an iterated learning experiment where random sequences of syllables are evolved across four transmission chains with ten generations of subjects each (all native Dutch speakers). The initial random sequences are generated by concatenating twelve tokens of the set {ban, bi, ta, tin}, as a way to materialise the abstract verse templates without using content-words. More precisely, the experiment aims to model the sequences of nonsense syllables used in many traditions to communicate the rhythmic patterns underlying songs (e.g. bols in Hindustani music, lalay patterns in Berber verse). Participants listened to the sequences of syllables, and tried to reproduce them using four computer keys, eachmapped to one of the four syllables used in the input sequences. The relative timing of the participants’ responses were normalised so that the input always consisted of completely isochronous sequences. Overall, the results show that sequences become shorter, easier to recall and more structured in the transmission process. Some regularities can be related to a global tendency to chunk the input and increase the popularity of a handful of ngrams. Besides, sequences increasingly tend to be opened by a heavy syllable (e.g. ban) and closed by a light syllable (e.g. ta), which can derive from a Dutch-specific bias.
{"title":"The emergence of verse templates through iterated learning","authors":"V. deCastro-Arrazola, S. Kirby","doi":"10.1093/JOLE/LZY013","DOIUrl":"https://doi.org/10.1093/JOLE/LZY013","url":null,"abstract":"Every language produces some type of verse in the form of songs, poems or nursery rhymes, which can be analysed as a layer of words set to a template (e.g. a tune, a poetic metre). Verse templates typically consist of hierarchically organised sections: songs are made up of stanzas, divided into lines, containing bars, etc. We hypothesise that this kind of patterns may emerge in the process of cultural transmission; unstructured sound sequences impose a challenge to short-termmemory, but chunking the input makes it easier to parse and reproduce the sequences accurately. In order to test this hypothesis, we have run an iterated learning experiment where random sequences of syllables are evolved across four transmission chains with ten generations of subjects each (all native Dutch speakers). The initial random sequences are generated by concatenating twelve tokens of the set {ban, bi, ta, tin}, as a way to materialise the abstract verse templates without using content-words. More precisely, the experiment aims to model the sequences of nonsense syllables used in many traditions to communicate the rhythmic patterns underlying songs (e.g. bols in Hindustani music, lalay patterns in Berber verse). Participants listened to the sequences of syllables, and tried to reproduce them using four computer keys, eachmapped to one of the four syllables used in the input sequences. The relative timing of the participants’ responses were normalised so that the input always consisted of completely isochronous sequences. Overall, the results show that sequences become shorter, easier to recall and more structured in the transmission process. Some regularities can be related to a global tendency to chunk the input and increase the popularity of a handful of ngrams. Besides, sequences increasingly tend to be opened by a heavy syllable (e.g. ban) and closed by a light syllable (e.g. ta), which can derive from a Dutch-specific bias.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":"1 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JOLE/LZY013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41987681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"When the Bough Breaks: A Contribution to Falk’s Hypothesis","authors":"D. Lindsay","doi":"10.1093/JOLE/LZY011","DOIUrl":"https://doi.org/10.1093/JOLE/LZY011","url":null,"abstract":"","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2018-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JOLE/LZY011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43740514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Iterated language learning experiments have shown that meaningful and structured signalling systems emerge when there is pressure for signals to be both learnable and expressive. Yet, such experiments have mainly been conducted with adults using language-like signals. Here we explore whether structured signalling systems can also emerge when signalling domains are unfamiliar and when the learners are children with their well-attested cognitive and pragmatic limitations. In Experiment 1, we compared iterated learning of binary auditory sequences denoting small sets of meanings in chains of adults and 5- to 7-year-old children. Signalling systems became more learnable even though iconicity and structure did not emerge despite applying a homonymy filter designed to keep the systems expressive. When the same types of signals were used in referential communication by adult and child dyads in Experiment 2, only the adults, but not the children, were able to negotiate shared iconic and structured signals. Referential communication using their native language by 4- to 5-year-old children in Experiment 3 showed that only interaction with adults, but not with peers resulted in informative expressions. These findings suggest that emergence and transmission of communication systems are unlikely to be driven by children, and point to the importance of cognitive maturity and pragmatic expertise of learners as well as feedback-based scaffolding of communicative effectiveness by experts during language evolution.
{"title":"Adults are more efficient in creating and transmitting novel signalling systems than children","authors":"V. Kempe, N. Gauvrit, Alison Gibson, M. Jamieson","doi":"10.1093/JOLE/LZY012","DOIUrl":"https://doi.org/10.1093/JOLE/LZY012","url":null,"abstract":"\u0000 Iterated language learning experiments have shown that meaningful and structured signalling systems emerge when there is pressure for signals to be both learnable and expressive. Yet, such experiments have mainly been conducted with adults using language-like signals. Here we explore whether structured signalling systems can also emerge when signalling domains are unfamiliar and when the learners are children with their well-attested cognitive and pragmatic limitations. In Experiment 1, we compared iterated learning of binary auditory sequences denoting small sets of meanings in chains of adults and 5- to 7-year-old children. Signalling systems became more learnable even though iconicity and structure did not emerge despite applying a homonymy filter designed to keep the systems expressive. When the same types of signals were used in referential communication by adult and child dyads in Experiment 2, only the adults, but not the children, were able to negotiate shared iconic and structured signals. Referential communication using their native language by 4- to 5-year-old children in Experiment 3 showed that only interaction with adults, but not with peers resulted in informative expressions. These findings suggest that emergence and transmission of communication systems are unlikely to be driven by children, and point to the importance of cognitive maturity and pragmatic expertise of learners as well as feedback-based scaffolding of communicative effectiveness by experts during language evolution.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JOLE/LZY012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43100694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Languages differ in their complexity. One possible explanation for this observation is that differences in social factors influence linguistic complexity: languages that are used for communication in small-scale 'societies of intimates' exhibit greater complexity as a result of the communicative contexts in which they are typically employed. We used the techniques from referential communication studies across three experiments to assess the effects of two social group factors-group size and amount of communally shared knowledge-on the brevity and transparency of linguistic conventions. In Experiment 1, we explored the effects of a manipulation of group size, comparing the conventions which develop from the interaction of two speakers, with those which develop between three speakers. In Experiment 2, we manipulated the extent to which groups of three speakers share talk-relevant contextual information. While we found the conditions that involve larger groups and less shared background information initially resulted in longer labels and a greater reliance on more literal descriptive terms, there was no effect of either factor in the longer term. In Experiment 3, we investigated the transparency of the conventions of Experiments 1 and 2 by assessing how well they could be matched to their intended referents by naive individuals. We found no evidence to support the claims that communicative contexts involving communicating with more individuals, or individuals with whom less relevant information is shared, produce more transparent conventions. Our experiments ultimately provide no support for the idea that the structure of linguistic conventions is shaped by the groups in which they develop.
{"title":"Social Group Effects on the Emergence of Communicative Conventions and Language Complexity","authors":"M. Atkinson, Gregory J. Mills, Kenny Smith","doi":"10.1093/JOLE/LZY010","DOIUrl":"https://doi.org/10.1093/JOLE/LZY010","url":null,"abstract":"Languages differ in their complexity. One possible explanation for this observation is that differences in social factors influence linguistic complexity: languages that are used for communication in small-scale 'societies of intimates' exhibit greater complexity as a result of the communicative contexts in which they are typically employed. We used the techniques from referential communication studies across three experiments to assess the effects of two social group factors-group size and amount of communally shared knowledge-on the brevity and transparency of linguistic conventions. In Experiment 1, we explored the effects of a manipulation of group size, comparing the conventions which develop from the interaction of two speakers, with those which develop between three speakers. In Experiment 2, we manipulated the extent to which groups of three speakers share talk-relevant contextual information. While we found the conditions that involve larger groups and less shared background information initially resulted in longer labels and a greater reliance on more literal descriptive terms, there was no effect of either factor in the longer term. In Experiment 3, we investigated the transparency of the conventions of Experiments 1 and 2 by assessing how well they could be matched to their intended referents by naive individuals. We found no evidence to support the claims that communicative contexts involving communicating with more individuals, or individuals with whom less relevant information is shared, produce more transparent conventions. Our experiments ultimately provide no support for the idea that the structure of linguistic conventions is shaped by the groups in which they develop.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2018-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JOLE/LZY010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46212330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carmen Saldana, S. Kirby, R. Truswell, Kenneth Smith
Compositional hierarchical structure is a prerequisite for productive languages; it allows language learners to express and understand an infinity of meanings from finite sources (i.e., a lexicon and a grammar). Understanding how such structure evolved is central to evolutionary linguistics. Previous work combining artificial language learning and iterated learning techniques has shown how basic compositional structure can evolve from the trade-off between learnability and expressivity pressures at play in language transmission. In the present study we show, across two experiments, how the same mechanisms involved in the evolution of basic compositionality can also lead to the evolution of compositional hierarchical structure. We thus provide experimental evidence showing that cultural transmission allows advantages of compositional hierarchical structure in language learning and use to permeate language as a system of behaviour.
{"title":"Compositional Hierarchical Structure Evolves through Cultural Transmission: An Experimental Study","authors":"Carmen Saldana, S. Kirby, R. Truswell, Kenneth Smith","doi":"10.1093/JOLE/LZZ002","DOIUrl":"https://doi.org/10.1093/JOLE/LZZ002","url":null,"abstract":"Compositional hierarchical structure is a prerequisite for productive languages; it allows language learners to express and understand an infinity of meanings from finite sources (i.e., a lexicon and a grammar). Understanding how such structure evolved is central to evolutionary linguistics. Previous work combining artificial language learning and iterated learning techniques has shown how basic compositional structure can evolve from the trade-off between learnability and expressivity pressures at play in language transmission. In the present study we show, across two experiments, how the same mechanisms involved in the evolution of basic compositionality can also lead to the evolution of compositional hierarchical structure. We thus provide experimental evidence showing that cultural transmission allows advantages of compositional hierarchical structure in language learning and use to permeate language as a system of behaviour.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2018-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JOLE/LZZ002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49306557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Language in Our Brain: The Origins of a Uniquely Human Capacity, by Angela D. Friederici","authors":"Elena Usai Morgan, Giosuè Baggio","doi":"10.1093/JOLE/LZY009","DOIUrl":"https://doi.org/10.1093/JOLE/LZY009","url":null,"abstract":"","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":"1 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2018-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JOLE/LZY009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41409206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Language is thought to be a crucial element behind Pleistocene expansions of Homo sapiens but our understanding of language change over the very long term is still poor. There have been two main approaches to language dynamics in this context. One assumes a continual ebb and flow of local human populations and languages, leading to high levels of ‘patchiness’ in both genes and languages. Another approach argues that long-term equilibrium leads not to patchiness but to areal diffusion and convergence. Both of these approaches assume equilibrium to be the norm. However, research in ecology since the 1970s has found that ecosystems have multiple potential states rather than a single equilibrium point. Under the name of resilience theory, such thinking is being increasingly applied to coupled socio-ecological systems using the concept of the adaptive cycle. This article proposes a model of long-term language change based on the adaptive cycle of resilience theory.
{"title":"Socio-ecological resilience and language dynamics: An adaptive cycle model of long-term language change","authors":"M. Hudson","doi":"10.1093/JOLE/LZY008","DOIUrl":"https://doi.org/10.1093/JOLE/LZY008","url":null,"abstract":"Language is thought to be a crucial element behind Pleistocene expansions of Homo sapiens but our understanding of language change over the very long term is still poor. There have been two main approaches to language dynamics in this context. One assumes a continual ebb and flow of local human populations and languages, leading to high levels of ‘patchiness’ in both genes and languages. Another approach argues that long-term equilibrium leads not to patchiness but to areal diffusion and convergence. Both of these approaches assume equilibrium to be the norm. However, research in ecology since the 1970s has found that ecosystems have multiple potential states rather than a single equilibrium point. Under the name of resilience theory, such thinking is being increasingly applied to coupled socio-ecological systems using the concept of the adaptive cycle. This article proposes a model of long-term language change based on the adaptive cycle of resilience theory.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2018-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JOLE/LZY008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44539776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Johann-Mattis List, M. Walworth, Simon J. Greenhill, Tiago Tresoldi, Robert Forkel
With increasing amounts of digitally available data from all over the world, manual annotation of cognates in multi-lingual word lists becomes more and more time-consuming in historical linguistics. Using available software packages to pre-process the data prior to manual analysis can drastically speed-up the process of cognate detection. Furthermore, it allows us to get a quick overview on data which have not yet been intensively studied by experts. LingPy is a Python library which provides a large arsenal of routines for sequence comparison in historical linguistics. With LingPy, linguists can not only automatically search for cognates in lexical data, but they can also align the automatically identified words, and output them in various forms, which aim at facilitating manual inspection. In this tutorial, we will briefly introduce the basic concepts behind the algorithms employed by LingPy and then illustrate in concrete workflows how automatic sequence comparison can be applied to multi-lingual word lists. The goal is to provide the readers with all information they need to (1) carry out cognate detection and alignment analyses in LingPy, (2) select the appropriate algorithms for the appropriate task, (3) evaluate how well automatic cognate detection algorithms perform compared to experts, and (4) export their data into various formats useful for additional analyses or data sharing. While basic knowledge of the Python language is useful for all analyses, our tutorial is structured in such a way that scholars with basic knowledge of computing can follow through all steps as well.
{"title":"Sequence comparison in computational historical linguistics","authors":"Johann-Mattis List, M. Walworth, Simon J. Greenhill, Tiago Tresoldi, Robert Forkel","doi":"10.1093/JOLE/LZY006","DOIUrl":"https://doi.org/10.1093/JOLE/LZY006","url":null,"abstract":"With increasing amounts of digitally available data from all over the world, manual annotation of cognates in multi-lingual word lists becomes more and more time-consuming in historical linguistics. Using available software packages to pre-process the data prior to manual analysis can drastically speed-up the process of cognate detection. Furthermore, it allows us to get a quick overview on data which have not yet been intensively studied by experts. LingPy is a Python library which provides a large arsenal of routines for sequence comparison in historical linguistics. With LingPy, linguists can not only automatically search for cognates in lexical data, but they can also align the automatically identified words, and output them in various forms, which aim at facilitating manual inspection. In this tutorial, we will briefly introduce the basic concepts behind the algorithms employed by LingPy and then illustrate in concrete workflows how automatic sequence comparison can be applied to multi-lingual word lists. The goal is to provide the readers with all information they need to (1) carry out cognate detection and alignment analyses in LingPy, (2) select the appropriate algorithms for the appropriate task, (3) evaluate how well automatic cognate detection algorithms perform compared to experts, and (4) export their data into various formats useful for additional analyses or data sharing. While basic knowledge of the Python language is useful for all analyses, our tutorial is structured in such a way that scholars with basic knowledge of computing can follow through all steps as well.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JOLE/LZY006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43239991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The historical connection between the Transeurasian languages, i.e. the Japonic, Koreanic, Tungusic, Mongolic, and Turkic languages, is among the most disputed issues of historical linguistics. Here, we will combine the power of classical historical-comparative linguistics and computational Bayesian phylogenetic methods to infer a phylogeny of the Transeurasian languages. To this end, we will use lexical etymologies supporting the reconstruction of proto-Transeurasian forms with meanings that belong to the Leipzig-Jakarta 200 basic vocabulary list. Our application of Bayesian phylogenetic inference to the classification of the Transeurasian languages is unprecedented. In addition to the methodological implications for Bayesian inference applied to proposed language phyla at relatively deep time depths and with relatively sparse sets of surviving daughter languages, our research has also factual implications for the existing theories of Transeurasian relationships. Our results move the field forward in that they provide a quantitative basis to test various competing hypotheses with regard to the internal structure of the Transeurasian family.
{"title":"Bayesian phylolinguistics reveals the internal structure of the Transeurasian family","authors":"Martine Robbeets, R. Bouckaert","doi":"10.1093/JOLE/LZY007","DOIUrl":"https://doi.org/10.1093/JOLE/LZY007","url":null,"abstract":"The historical connection between the Transeurasian languages, i.e. the Japonic, Koreanic, Tungusic, Mongolic, and Turkic languages, is among the most disputed issues of historical linguistics. Here, we will combine the power of classical historical-comparative linguistics and computational Bayesian phylogenetic methods to infer a phylogeny of the Transeurasian languages. To this end, we will use lexical etymologies supporting the reconstruction of proto-Transeurasian forms with meanings that belong to the Leipzig-Jakarta 200 basic vocabulary list. Our application of Bayesian phylogenetic inference to the classification of the Transeurasian languages is unprecedented. In addition to the methodological implications for Bayesian inference applied to proposed language phyla at relatively deep time depths and with relatively sparse sets of surviving daughter languages, our research has also factual implications for the existing theories of Transeurasian relationships. Our results move the field forward in that they provide a quantitative basis to test various competing hypotheses with regard to the internal structure of the Transeurasian family.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JOLE/LZY007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46719537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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