36.1635, Confs: Artificial Languages in the Linguist’s Toolbox (Germany)

[The LINGUIST List]

LINGUIST List: Vol-36-1635. Sat May 24 2025. ISSN: 1069 - 4875.

Subject: 36.1635, Confs: Artificial Languages in the Linguist’s Toolbox (Germany)

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Date: 21-May-2025
From: Julia Heine [julia.heine at fu-berlin.de]
Subject: Artificial Languages in the Linguist’s Toolbox


Artificial Languages in the Linguist’s Toolbox

Date: 25-Sep-2025 - 26-Sep-2025
Location: Berlin, Germany
Contact: Julia Heine
Contact Email: julia.heine at fu-berlin.de
Meeting URL:
https://www.geisteswissenschaften.fu-berlin.de/en/v/exrean/news/Artificial-language.html

Linguistic Field(s): Historical Linguistics; Language Acquisition;
Neurolinguistics; Psycholinguistics; Typology

Workshop: Artificial Languages in the Linguist’s Toolbox
We are delighted to invite you to attend the second workshop of the
EXREAN project* at the Freie Universität Berlin which will explore the
application of artificial languages as a tool in linguistic research.
Artificial language learning experiments test how far language users
can extract rule-like generalizations from structured input
(Culbertson, 2023). Since Reber’s (1967) seminal artificial grammar
(AG) study, which aimed at investigating implicit learning mechanisms,
artificial language (AL) paradigms have become more diverse in their
implementation and have been proposed as a versatile tool for
addressing an increasingly broad range of research topics within areas
as varied as second language acquisition, language typology, and
language change.
Despite this extensive body of research, the nature of the knowledge
acquired in AL experiments as well as the effects of different
training methods implemented in these studies still need to be
debated, given their crucial implications for the linguistic research
questions that can sensibly be addressed through these studies.
The aim of this workshop is to explore the scope of artificial
languages as a tool in linguistic research. In particular, we are
interested in the implications of different AL training methods and
learning outcomes for possible application areas of ALs within
different subfields of linguistics and concerning multifaceted
research questions.
We are especially interested in the following questions:
 - What kinds of linguistic knowledge can be acquired through
artificial language paradigms, and how comparable is it to the
knowledge of a native language?
 - How much do different training methods (e.g., implicit vs. explicit
training) affect the nature of the knowledge that is acquired?
 - What kind of linguistic research questions can be addressed through
artificial languages?
 - How far could studies involving these paradigms inform our
understanding of mechanisms related to language change and language
typology?
 - Taking into consideration the many new technological possibilities
that have emerged since early AG experiments: What can
participant-friendly implementations of artificial language designs
look like in view of rather extensive training procedures?
Speakers:
Kara Morgan-Short (University of Illinois Chicago)
Kenny Smith (University of Edinburgh)
Jennifer Culbertson (University of Edinburgh)
Mora Maldonado (Université de Nantes)
Zara Harmon (Max Planck Institute for Psycholinguistics, Nijmegen)
Gareth Roberts (University of Pennsylvania)
Marieke Schouwstra (University of Amsterdam)
Registration (open until 23 September 2025; no registration fee):
https://docs.google.com/forms/d/e/1FAIpQLScF2wQU9DVwKw4VPH7VdqxbILbUV30rPTxowX4Ob8GiQdZWww/viewform?usp=sharing
Location: Freie Universität Berlin, Berlin, Habelschwerdter Allee 45
(Rostlaube), J 32/102 (Sep 25,) / J 27/14 (Sep 26)
* The aim of the EXREAN project is to experimentally replicate
historically attested reanalysis processes in a laboratory setting by
training participants from typologically diverse languages through
experimental paradigms from natural language comprehension and
artificial language learning
(https://www.geisteswissenschaften.fu-berlin.de/en/v/exrean/index.html).



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LINGUIST List: Vol-36-1635
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