The Role of Social Network Structure in the Emergence of Linguistic Structure

Abstract

Social network structure has been argued to shape the structure of languages, as well as affect the spread of innovations and the formation of conventions in the community. Specifically, theoretical and computational models of language change predict that sparsely connected communities develop more systematic languages, while tightly knit communities can maintain high levels of linguistic complexity and variability. However, the role of social network structure in the cultural evolution of languages has never been tested experimentally. Here, we present results from a behavioral group communication study, in which we examined the formation of new languages created in the lab by micro‐societies that varied in their network structure. We contrasted three types of social networks: fully connected, small‐world, and scale‐free. We examined the artificial languages created by these different networks with respect to their linguistic structure, communicative success, stability, and convergence. Results did not reveal any effect of network structure for any measure, with all languages becoming similarly more systematic, more accurate, more stable, and more shared over time. At the same time, small‐world networks showed the greatest variation in their convergence, stabilization, and emerging structure patterns, indicating that network structure can influence the community's susceptibility to random linguistic changes (i.e., drift).

Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks

“…Interestingly, while the network structure does not affect the final language at which the population stabilizes, it does affect the speed with which it stabilizes: this is faster in larger random networks, and generally slower in scale-free networks. This is consistent with Raviv et al ( 2020 )'s experimental findings, where it is suggested that stability is faster in denser networks, while sparser networks would be slower to stabilize. Differences in convergence times between different network structures was also found in the statistical physics literature that studies cultural dynamics (Baxter et al, 2008 ; Castellano et al, 2009 ; Blythe, 2015 ).…”

“…Language is not an exception, with studies ranging from “classic” sociolinguistics (Milroy and Gordon, 2008 ) to more recent network-centric (Ke et al, 2008 ; Fagyal et al, 2010 ; Abitbol et al, 2018 ). Language change has also been studied using real-world examples, such as the vowel chain shift in Ximu or the consonant convergence in Duoxu (Chirkova and Gong, 2014 , 2019 ), and using experimental approaches (Raviv, 2020 ; Raviv et al, 2020 ) showing that we must consider the structure of the connectivity in linguistic communities. Social structure, and more specifically the average degree, the presence of shortcuts and the level of centrality can have an effect on linguistic categorization (Gong et al, 2012a ) or the degree of diffusion of a variant in a population (Gong et al, 2012b ).…”

Experimental studies of cultural evolution

“…This is because partially-connected groups can better explore the full set of cultural trait lineages than fully-connected groups. Other studies have explored the effect of different social network structures on language evolution, suggesting that small world networks generate more variation in linguistic structure than other networks (Raviv, Meyer, & Lev-Ari, 2020). These results suggest that population structure is just as important as population size in facilitating CCE.…”

Foreigner-directed speech is simpler than native-directed: Evidence from social media

“…The openly-available WordReference corpus can be used for further studies of foreigner-directed speech (and, of course, other phenomena related to non-native acquisition and usage). The understanding of the properties of L2 production can, for instance, be nuanced by taking into account other relevant factors, such as speakers' mother tongues (Schepens et al, 2020) or social-network structure (Raviv et al, 2020).…”