{"title":"Beyond cognacy: historical relations between words and their implication for phylogenetic reconstruction","authors":"Johann-Mattis List","doi":"10.1093/JOLE/LZW006","DOIUrl":null,"url":null,"abstract":"This article investigates the terminology and the processes underlying the fundamental historical relations between words in linguistics ( cognacy ) and genes in biology ( homology ). The comparison between linguistics and biology shows that there are major inconsistencies in the analogies drawn between the two research fields and the models applied in phylogenetic reconstruction in linguistics. Cognacy between words is treated as a binary relation which is either present or not. Words, however, can exhibit different degrees of cognacy which go beyond the distinction between orthologous and paralogous genes in biology. The complex nature of cognacy has strong implications for the models used for phylogenetic reconstruction. Instead of modeling lexical evolution as a process of cognate gain and cognate loss, we need to go beyond the cognate relation and develop models which take the degrees of cognacy into account. This opts for the use of evolutionary models which handle multistate characters and allow to define potentially asymmetrical transition tendencies among the character states instead of time-reversible binary state models in phylogenetic approaches. The benefit of multistate models with asymmetric transition tendencies is demonstrated by testing how well different models of lexical change perform in semantic reconstruction on a lexicostatistical dataset of 23 Chinese dialects in a parsimony framework. The results show that the improved models largely outperform the popular gain–loss models. This suggests that improved models of lexical change may have strong consequences for phylogenetic approaches in linguistics.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/JOLE/LZW006","citationCount":"35","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Language Evolution","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/JOLE/LZW006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"LANGUAGE & LINGUISTICS","Score":null,"Total":0}
引用次数: 35
Abstract
This article investigates the terminology and the processes underlying the fundamental historical relations between words in linguistics ( cognacy ) and genes in biology ( homology ). The comparison between linguistics and biology shows that there are major inconsistencies in the analogies drawn between the two research fields and the models applied in phylogenetic reconstruction in linguistics. Cognacy between words is treated as a binary relation which is either present or not. Words, however, can exhibit different degrees of cognacy which go beyond the distinction between orthologous and paralogous genes in biology. The complex nature of cognacy has strong implications for the models used for phylogenetic reconstruction. Instead of modeling lexical evolution as a process of cognate gain and cognate loss, we need to go beyond the cognate relation and develop models which take the degrees of cognacy into account. This opts for the use of evolutionary models which handle multistate characters and allow to define potentially asymmetrical transition tendencies among the character states instead of time-reversible binary state models in phylogenetic approaches. The benefit of multistate models with asymmetric transition tendencies is demonstrated by testing how well different models of lexical change perform in semantic reconstruction on a lexicostatistical dataset of 23 Chinese dialects in a parsimony framework. The results show that the improved models largely outperform the popular gain–loss models. This suggests that improved models of lexical change may have strong consequences for phylogenetic approaches in linguistics.