ArticleFrontiers in psychology2021
Syllable Complexity and Morphological Synthesis: A Well-Motivated Positive Complexity Correlation Across Subdomains.
Article in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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1 citing paper in PubMed.
- Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks.Frontiers in psychology · 2021Article
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- Erratum issued
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4 authors.
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Abstract
Relationships between phonological and morphological complexity have long been proposed in the linguistic literature, with empirical investigations often seeking complexity trade-offs. Positive complexity correlations tend not to be viewed in terms of motivations. We argue that positive complexity correlations can be diachronically well-motivated, emerging from crosslinguistically prevalent processes of language change. We examine the correlation between syllable complexity and morphological synthesis, hypothesizing that the process of grammaticalization motivates a positive relationship between the two features. To test this, we conduct a typological survey of 95 diverse languages and a corpus study of 21 languages with substantive (predominantly >10,000 words) corpora from the DoReCo project. The first study establishes a significant positive correlation between syllable complexity, measured in terms of maximal syllable patterns, and the index of synthesis (morpheme/word ratio). The second study tests the hypothesis that the relationship between syllable complexity and synthesis holds at local (word-initial and word-final) levels and within noun and verb types, as predicted by a grammaticalization account. While the findings of the corpus study are limited in their statistical power, the observed tendencies are consistent with our predictions. This study contributes important findings to the complexity literature, as well as a novel method which incorporates broad typological sampling and deep corpus analysis.
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