37.584, Confs: DGKL 2026 Theme Session: Computational Approaches to Language Dynamics - Cognitive and Constructional Perspectives (Germany)

Thu Feb 12 12:05:02 UTC 2026

LINGUIST List: Vol-37-584. Thu Feb 12 2026. ISSN: 1069 - 4875.

Subject: 37.584, Confs: DGKL 2026 Theme Session: Computational Approaches to Language Dynamics - Cognitive and Constructional Perspectives (Germany)

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Date: 10-Feb-2026
From: Stefan Hartmann [hartmast at hhu.de]
Subject: DGKL 2026 Theme Session: Computational Approaches to Language Dynamics - Cognitive and Constructional Perspectives

DGKL 2026 Theme Session: Computational Approaches to Language Dynamics
- Cognitive and Constructional Perspectives

Date: 31-Aug-2026 - 02-Sep-2026
Location: Bielefeld, Germany
Contact: Stefan Hartmann
Contact Email: hartmast at hhu.de
Meeting URL:
https://www.uni-bielefeld.de/fakultaeten/linguistik-literaturwissenschaft/forschung/arbeitsgruppen/germanistische-grammatikf/dgkl2026/index.xml

Linguistic Field(s): Computational Linguistics; Historical
Linguistics; Language Acquisition; Text/Corpus Linguistics

Submission Deadline: 15-Mar-2026

Computational approaches to language dynamics: Cognitive and
constructional perspectives
Theme session at the 11th International Conference of the German
Cognitive Linguistics Association, 31.08.-02.09.2026
Convenors:
Bastian Bunzeck, University of Bielefeld,
bastian.bunzeck at uni-bielefeld.de
Stefan Hartmann, HHU Düsseldorf, hartmast at hhu.de
Cognitive linguistics conceives of language as a complex adaptive
system (Beckner et al. 2009). Modelling the intricacies of this system
entails numerous challenges for any approach that aims at going beyond
the mere description of linguistic facts and instead tries to offer an
explanatory account of linguistic phenomena. Multifactorial
statistical methods for the analysis of corpus or experimental data
have proven highly insightful in recent years, but even they cannot
answer all research questions that emerge at the interface of
cognition, culture, and language use. For instance, when it comes to
social effects that shape the contemporary make-up of the grammar of a
language, or when it comes to pathways of language learning as
influenced by the input that an individual receives from many
different sources, there are many variables that we cannot control
for. Computational approaches have therefore become more and more
important in recent years in various domains. To mention only two
examples, computational modelling, which has been among the most
important approaches for testing hypotheses about the evolutionary
development of language(s) for decades (Smith et al. 2003, Kirby 2013,
Ruland et al. 2023), is increasingly being used to account for
phenomena of language variation and change (Sevenants et al. 2021,
Pijpops 2022). Secondly, machine-learning approaches in the form of
deep neural language models (LMs) have started to play an increasingly
important role in modelling language processing and language dynamics.
(L)LMs are not only being used as “copilots” in traditional linguistic
research (Torrent et al. 2023), but also open up novel ways of
modelling first language acquisition (e.g. Bunzeck et al. 2025,
Padovani et al. 2025) or for analyzing structural properties of
language that may inform theories of cognitively plausible linguistic
representations (cf. Warstadt & Bowman 2022, Futrell & Mahowald 2025).
Here, some researchers even argue that LMs provide a proof-of-concept
for usage-based theories of language (Ambridge & Blything 2024,
Goldberg 2024), a claim that remains contested (cf. Piantadosi et al.
2023 and its numerous replies), also due to the rule-based nature of
common linguistic benchmarks (Weissweiler et al. 2025).
This theme session aims at bringing together researchers using
computational methods to address research questions from cognitive
linguistics and Construction Grammar including, but not limited to,
the following:
 - How do social, cultural, and interactional factors shape grammar(s)
and language(s), both in an ontogenetic and a historical perspective?
 - How can computational approaches help us model the make-up of
constructional networks both on an individual level and at the level
of populations of language users?
 - To what extent can computational models including (Large) Language
Models give insights into emergent properties of language(s)?
 - How can we probe black-box models like (L)LMs, and how do prompts,
benchmarks and other tests relate to theories of language structure
and use?
The theme session aims at building bridges between different
computational approaches that are used to investigate language
dynamics at various timescales. The confirmed speakers so far cover
the full spectrum of the envisaged breadth of the session, with topics
ranging from agent-based modelling of language change to the use of
(large) language models for modelling language acquisition.
Please send an abstract (up to 500 words excl. references) to the
theme session organizers (bastian.bunzeck at uni-bielefeld.de,
hartmast at hhu.de) until March 15, 2026. Decisions will be sent out in
early April.
References:
Ambridge, Ben & Liam Blything. 2024. Large language models are better
than theoretical linguists at theoretical linguistics. Theoretical
Linguistics 50(1–2). 33–48. https://doi.org/10.1515/tl-2024-2002.
Beckner, Clay, Richard Blythe, Joan Bybee, Morten H. Christiansen,
William Croft, Nick C. Ellis, John Holland, Jinyun Ke, Diane
Larsen-Freeman & Tom Schoenemann. 2009. Language is a Complex Adaptive
System: Position Paper. Language Learning 59 Suppl. 1. 1–26.
https://doi.org/10.1111/j.1467-9922.2009.00533.x.
Bunzeck, Bastian, Daniel Duran & Sina Zarrieß. 2025. Do construction
distributions shape formal language learning in German BabyLMs? In
Proceedings of the 29th conference on computational natural language
learning, 169–186. Vienna, Austria: Association for Computational
Linguistics.
Futrell, Richard & Kyle Mahowald. 2025. How Linguistics Learned to
Stop Worrying and Love the Language Models. Behavioral and Brain
Sciences 1–98. https://doi.org/10.1017/S0140525X2510112X.
Goldberg, Adele E. 2024. Usage-based constructionist approaches and
large language models. Constructions and Frames 16(2). 220–254.
https://doi.org/10.1075/cf.23017.gol.
Kirby, Simon. 2013. Language, culture, and computation: An adaptive
systems approach to biolinguistics. In Cedric Boeckx & Kleanthes K.
Grohmann (eds.), The Cambridge handbook of biolinguistics, 460–477.
Cambridge: Cambridge University Press.
Padovani, Francesca, Jaap Jumelet, Yevgen Matusevych & Arianna
Bisazza. 2025. Child-Directed Language Does Not Consistently Boost
Syntax Learning in Language Models. Proceedings of the 2025 Conference
on Empirical Methods in Natural Language Processing, 19746–19767.
Suzhou, China: Association for Computational Linguistics.
doi:10.18653/v1/2025.emnlp-main.999.
Piantadosi, Steven T. 2024. Modern language models refute Chomsky’s
approach to language. Language Science Press.
https://doi.org/10.5281/ZENODO.12665933.
Pijpops, Dirk. 2022. Lectal contamination: Evidence from corpora and
from agent-based simulation. International Journal of Corpus
Linguistics 27(3). 259–290. https://doi.org/10.1075/ijcl.20040.pij.
Ruland, Marcel, Alejandro Andirkó, Iza Romanowska & Cedric Boeckx.
2023. Modelling of factors underlying the evolution of human language.
Adaptive Behavior. SAGE Publications.
https://doi.org/10.1177/10597123221147336.
Sevenants, Anthe & Dirk Speelman. 2021. Keeping up with the Neighbours
- An Agent-Based Simulation of the Divergence of the Standard Dutch
Pronunciations in the Netherlands and Belgium. Computational
Linguistics in the Netherlands Journal 11. 5–26.
Smith, Kenny, Simon Kirby & Henry Brighton. 2003. Iterated Learning: A
Framework for the Emergence of Language. Artificial Life 9. 371–386.
Torrent, Tiago Timponi, Thomas Hoffmann, Arthur Lorenzi Almeida & Mark
Turner. 2023. Copilots for Linguists: AI, Constructions, and Frames.
Cambridge University Press. https://doi.org/10.1017/9781009439190.
Warstadt, Alex & Samuel R. Bowman. 2022. What Artificial Neural
Networks Can Tell Us about Human Language Acquisition. Algebraic
Structures in Natural Language, 17–60. 1. edn. Boca Raton: CRC Press.
doi:10.1201/9781003205388-2.
Weissweiler, Leonie, Kyle Mahowald & Adele E. Goldberg. 2025.
Linguistic Generalizations are not Rules: Impacts on Evaluation of
LMs. In Claire Bonial, Melissa Torgbi, Leonie Weissweiler, Austin
Blodgett, Katrien Beuls, Paul Van Eecke & Harish Tayyar Madabushi
(Hrsg.), Proceedings of the Second International Workshop on
Construction Grammars and NLP, 61–74. Düsseldorf, Germany: Association
for Computational Linguistics.

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