How social structure affects the persistence and features of sign languages

Mudd, Katie

doi:10.1075/sll.00072.mud

Cited by 1 publication

(2 citation statements)

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“…They propose that social connectedness might exert a stronger influence on the level of culture compared to population size. While most of these models do not specifically explore language variation, Mudd (2022) and Meir, Israel, Sandler, Padden, and Aronoff (2012) also emphasize the role of social structure on lexical variation in emergent sign languages. Notably, Mudd (2022) developed computational models grounded in observations from the Kata Kolok community, focusing on various aspects of their social structure, such as connectivity and size.…”

Section: Introductionmentioning

confidence: 99%

“…While most of these models do not specifically explore language variation, Mudd (2022) and Meir, Israel, Sandler, Padden, and Aronoff (2012) also emphasize the role of social structure on lexical variation in emergent sign languages. Notably, Mudd (2022) developed computational models grounded in observations from the Kata Kolok community, focusing on various aspects of their social structure, such as connectivity and size. To explain why some sign languages witness more lexical variation than others, she proposed that population size is an important factor influencing lexical variability in these languages.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

How Network Structure Shapes Languages: Disentangling the Factors Driving Variation in Communicative Agents

Josserand,

Allassonnière‐Tang,

Pellegrino

et al. 2024

Cognitive Science

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Languages show substantial variability between their speakers, but it is currently unclear how the structure of the communicative network contributes to the patterning of this variability. While previous studies have highlighted the role of network structure in language change, the specific aspects of network structure that shape language variability remain largely unknown. To address this gap, we developed a Bayesian agent‐based model of language evolution, contrasting between two distinct scenarios: language change and language emergence. By isolating the relative effects of specific global network metrics across thousands of simulations, we show that global characteristics of network structure play a critical role in shaping interindividual variation in language, while intraindividual variation is relatively unaffected. We effectively challenge the long‐held belief that size and density are the main network structural factors influencing language variation, and show that path length and clustering coefficient are the main factors driving interindividual variation. In particular, we show that variation is more likely to occur in populations where individuals are not well‐connected to each other. Additionally, variation is more likely to emerge in populations that are structured in small communities. Our study provides potentially important insights into the theoretical mechanisms underlying language variation.

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Section: Introductionmentioning

confidence: 99%