Social Network Limits Language Complexity

Lou-Magnuson, Matthew; Onnis, Luca

doi:10.1111/cogs.12683

Cited by 10 publications

(9 citation statements)

References 44 publications

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“…In particular, bigger communities tend to be more sparsely connected and more geographically spread out, have more contact with outsiders, and have a higher proportion of adult second language learners [14][15][16]. Each of these factors may contribute to the pattern of reduced complexity, and thus provide an alternative explanation for the correlation between community size and linguistic structure [5][6][7][8]20,21]. In fact, many researchers assume that this correlation is accounted for by the proportion of second language learners in the community [5][6][7]20] or by differences in network connectivity [15][16][17]21] (see discussion).…”

Section: Introductionmentioning

confidence: 99%

“…Each of these factors may contribute to the pattern of reduced complexity, and thus provide an alternative explanation for the correlation between community size and linguistic structure [5][6][7][8]20,21]. In fact, many researchers assume that this correlation is accounted for by the proportion of second language learners in the community [5][6][7]20] or by differences in network connectivity [15][16][17]21] (see discussion).…”

Section: Introductionmentioning

confidence: 99%

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Larger communities create more systematic languages

2019

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Understanding worldwide patterns of language diversity has long been a goal for evolutionary scientists, linguists and philosophers. Research over the past decade has suggested that linguistic diversity may result from differences in the social environments in which languages evolve. Specifically, recent work found that languages spoken in larger communities typically have more systematic grammatical structures. However, in the real world, community size is confounded with other social factors such as network structure and the number of second languages learners in the community, and it is often assumed that linguistic simplification is driven by these factors instead. Here, we show that in contrast to previous assumptions, community size has a unique and important influence on linguistic structure. We experimentally examine the live formation of new languages created in the laboratory by small and larger groups, and find that larger groups of interacting participants develop more systematic languages over time, and do so faster and more consistently than small groups. Small groups also vary more in their linguistic behaviours, suggesting that small communities are more vulnerable to drift. These results show that community size predicts patterns of language diversity, and suggest that an increase in community size might have contributed to language evolution.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Larger communities create more systematic languages

2019

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“…In this field, the main focus is on social influence on the dissemination of attitudes (e.g. regarding sustainable energy use (Moglia et al, 2018;Niamir et al, 2018) or organic farming (Kaufmann et al, 2009)), culture (Flache & Macy, 2011;Keijzer et al, 2018), language (Ke et al, 2008;Lou-Magnuson & Onnis, 2018), opinions (Biondo et al, 2018;Lu et al, 2009;Piedrahita et al, 2018), trends (Schlaile et al, 2018;Weng et al, 2012) or information (Chareunsy, 2018;Frank et al, 2018).…”

Section: Diffusionmentioning

confidence: 99%

Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review

Will

Groeneveld

Frank

et al. 2020

SESMO

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Agent-based modelling (ABM) and social network analysis (SNA) are both valuable tools for exploring the impact of human interactions on a broad range of social and ecological patterns. Integrating these approaches offers unique opportunities to gain insights into human behaviour that neither the evaluation of social networks nor agent-based models alone can provide. There are many intriguing examples that demonstrate this potential, for instance in epidemiology, marketing or social dynamics. Based on an extensive literature review, we provide an overview on coupling ABM with SNA and evaluating the integrated approach. Building on this, we identify current shortcomings in the combination of the two methods. The greatest room for improvement is found with regard to (i) the consideration of the concept of social integration through networks, (ii) an increased use of the co-evolutionary character of social networks and embedded agents, and (iii) a systematic and quantitative model analysis focusing on the causal relationship between the agents and the network. Furthermore, we highlight the importance of a comprehensive and clearly structured model conceptualization and documentation. We synthesize our findings in guidelines that contain the main aspects to consider when integrating social networks into agent-based models.

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“…Such population dynamics must have played out over long temporal and vast population scales, but through these iterated learning paradigms such processes are to some limited degree brought under experimental control. A current challenge is to extend the limits of such paradigms and study how the same constraints can give rise to novel emergent structure at larger scales of interaction (e.g., Lou-Magnuson & Onnis, 2018;Lupyan & Dale, 2010;Raviv, Meyer, & Lev-Ari, 2019). Such extension requires currently absent automated methods for the study of complex dynamic signals as part of communicative systems.…”

Section: Introductionmentioning

confidence: 99%

Multiscale kinematic analysis reveals structural properties of change in evolving manual languages in the lab

Pouw¹,

Dingemanse²,

Motamedi³

et al. 2020

Preprint

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Reverse engineering how language emerged is a daunting interdisciplinary project. Experimental cognitive science has contributed to this effort by eliciting in the lab constraints likely playing a role for language emergence; constraints such as iterated transmission of communicative tokens between agents. Since such constraints played out over long phylogenetic time and involved vast populations, a crucial challenge for iterated language learning paradigms is to extend its limits. In the current approach we perform a multiscale quantification of kinematic changes of an evolving silent gesture system. Silent gestures consist of complex multi-articulatory movement that have so far proven elusive to quantify in a structural and reproducable way, and is primarily studied through human coders meticulously interpreting the referential content of gestures. Here we reanalyzed video data from a silent gesture iterated learning experiment (Motamedi et al. 2019), which originally showed increases in systematicity of gestural form over language transmissions. We applied a signal-based approach, first utilizing computer vision techniques to quantify kinematics from videodata. Then we performed a multiscale kinematic analysis showing that over generations of language users, silent gestures became more efficient and less complex in their kinematics. We further detect systematicity of the communicative tokens’s interrelations which proved itself as a proxy of systematicity obtained via human observation data. Thus we demonstrate the potential for a signal-based approach of language evolution in complex multi-articulatory gestures.

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Social Network Limits Language Complexity

Cited by 10 publications

References 44 publications

Larger communities create more systematic languages

Larger communities create more systematic languages

Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review

Multiscale kinematic analysis reveals structural properties of change in evolving manual languages in the lab

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