Calendrier

• Simon DEVYLDER (Arctic University of Norway (UiT), Tromsø)

dans le cadre DILIS

Link
https://cnrs.zoom.us/j/94083009678?pwd=dzZjbE1KSHZQdElSNEhYVnVYUHl4dz09

Abstract

Does language adapt to its environment? One of the most influential hypotheses of language adaptation – often referred to as the Linguistic Niche Hypothesis (Lupyan & Dale 2010) – proposes an explanation of linguistic diversity that is based on mechanisms of cognitive pressures associated with social dynamics. It claims that if we know the size of an ethnolinguistic community, and the proportion of adult second language learners in a population, we can predict the morphological complexity of a language. The hypothesis is based on the logic that L2 speakers rely more on lexical storage and less on combinatorial processing of morphologically complex words than speakers who learned the same language as their first (Silva & Clahsen 2008, Clahsen et al. 2010), which makes the paradigms of highly inflected languages difficult to learn for adults (i.e., the cognitive pressure).  

However, what is crucially missing from all investigations that have addressed the question of language adaptation is a level of granularity that would come close to capturing the complexity of the two core predictors: the measures for the environmental pressures (i.e., social dynamics) and the measures for cognitive pressures (i.e., the language learning profiles of speakers). Aside from demographic estimates, there is simply no primary data available, which would come close to capturing the structural complexity of social dynamics. Such methods exist in Network Science, they have just never been integrated into models aiming to account for the structural evolution of language. The measures for cognitive constraints are also very coarse. In the current state-of-the-art, speakers of a language are divided into L1 vs. L2 speakers. This binary categorization cannot accurately reflect how the diversity of learning profiles varies in ways that may impact language structure. Yet precise measures of a speaker’s learning profile are well established in the field of heritage language and multilingualism with instruments like the LEAP-Q (Kaushanskaya, Blumenfeld, & Marian 2020). The LEAP-Q provides fine-grained indexes of a speaker’s patterns of language use and exposure at various life stages and in various contexts (e.g., degree and type of language used at home vs. at school).

I will present the methodological implementation of the first study that integrates the strength of Personal Network Analysis with methods from Multilingual Language Acquisition to address questions of language change. This study was performed with 120 speakers of Paamese, Vanuatu who participated in a behavioural experiment, where they were asked to produce linguistic descriptions of various events prompted by animated elicitation stimuli. I will also present the preliminary results of this study, which seem to indicate that the variation of the morphosyntactic complexity of the linguistic descriptions can be explained by variables pertaining to the structure of the participant’s personal network in combination with their language learning profile. For example, participants with a fragmented personal network structure combined with a low exposure to Paamese in childhood, are more likely to produce morphosyntactically reduced possessive structures.

I will take these preliminary results to start a discussion of a broader scope and argue that these preliminary findings bring support to the hypothesis that language adapt to its environment (Nölle et al. 2020, Trudgill 2010, Nettle 2012). Furthermore, I will argue that the integration of Personal Network Analysis into models of language evolution has the potential to help the field make more accurate predictions about the rates and directions of language change.

References

Clahsen, Harald, Felser, Claudia, Neubauer, Kathleen, Sato, M., & Silva, R. 2010. Morphological structure in native and non-native language processing. Language learning, 60(1), 21-43.

Kaushanskaya, M., Blumenfeld, H. K., & Marian, V. (2020). The language experience and proficiency questionnaire (leap-q): Ten years later. Bilingualism: Language and Cognition, 23(5), 945-950.

Lupyan, Gary, & Dale, Rick. 2010. Language structure is partly determined by social structure. PloS one, 5(1), e8559.

McCarty, C., Lubbers, M. J., Vacca, R., & Molina, J. L. (2019). Conducting personal network research: A practical guide. Guilford Publications.

Nettle, Daniel. (2012). Social scale and structural complexity in human languages. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 367(1597), 1829–1836.

Nölle, Jonas, Fusaroli, Riccardo., Mills, Gregory J., & Tylén, Kristian. 2020. Language as shaped by the environment: linguistic construal in a collaborative spatial task. Palgrave Communications, 6(1), 1-10.

Silva, Renita, & Clahsen, Harald. 2008. Morphologically complex words in L1 and L2 processing: Evidence from masked priming experiments in English. Bilingualism: Language and Cognition, 11(2), 245-260

Trudgill, Peter (2011). Sociolinguistic typology: Social determinants of linguistic complexity. Oxford University Press