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
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Development Neurocognition Disorders
Linguistic Diversity and its Sources
