[CogSci] Deadline extension: International Conference on Error-Driven Learning in Language (EDLL 2021)

Jessie Nixon jessie.nixon at uni-tuebingen.de
Fri Dec 4 02:36:37 PST 2020

Dear colleagues,

the deadline for the International Conference on Error-Driven Learning  
in Language (EDLL 2021) has been extended. The new deadline is Sunday,  
20 December, 2020. (Midnight GMT -12).

Please see below for further details.

Date: 10-Mar-2021 - 12-Mar-2021
Location: Online /Tübingen, Germany
Website: https://quantling.org/EDLL2021/

International Conference on Error-Driven Learning in Language (EDLL 2021).

Error-driven learning models, such as Rescorla and Wagner (1972) and  
Widrow and Hoff (1960) have had a major influence on many areas of  
psychology related to human and animal learning. However, research on  
language learning took a separate path for a long time. Recently,  
insights from error-driven learning have begun to be applied to a  
broad range of language phenomena with very promising results. For  
example, error-driven learning models have addressed questions  
relating to reading, spoken word comprehension, colour and number  
acquisition, word learning, first and second language speech sound  
acquisition, morphological processing, sentence processing, neural  
correlates of prediction error and more.

The International Conference on Error-Driven Learning in Language  
(EDLL 2021) aims to bring together researchers interested in  
error-driven learning in speech and language. The conference will be  
held online 10-12 March, 2021.

Call for Papers:

We are inviting experimental, computational or theoretical abstracts  
on any topic in error-driven learning of speech or language. Suitable  
topics include but are not limited to:
The role of error in
- first and second language acquisition
- learning or processing phonetic, morphological, syntactical or  
lexical information
- sentence processing, syntax and grammar acquisition and processing
as well as
- neural processing of error feedback during speech and language  
comprehension, production or learning
- the relationship between error-driven learning and information theory
- comparison of error-driven learning with different learning models  
such as Hebbian learning, statistical learning, Bayesian learning,  
distributional learning.

Invited speakers:
We are pleased to announce the following Keynote speakers:
Prof Adele Goldberg, University of Princeton
Prof Randall O’Reilly, University of California, Davis
Dr Petar Milin, University of Birmingham

Important dates:
Deadline extended! New deadline:  20 December, 2020
Notification of acceptance sent: 8 February, 2021
Conference: 10-12 March, 2021.

There will be no registration fee. Participation is free.
However, to help us with organising, participants are asked to  
register on the EDLL2021 website.

Submission guidelines:
Please submit your anonymous abstracts to  
Abstracts should be written in English and be no more than one page of  
text, with an optional second page for tables, figures and references.  
Please use 11 point Arial font for the whole abstract, including the  

We will publish abstracts in an online proceedings. Authors will  
retain copyright.

Programme committee
Jessie Nixon
Elnaz Shafaei-Bajestan
Harald Baayen

For inquiries contact:
jessie.nixon at uni-tuebingen.de
elnaz.shafaei-bajestan at uni-tuebingen.de

Jessie Nixon, PhD
Quantitative Linguistics Lab
Eberhard Karls University of Tübingen
Office 3.29
Wilhelmstraße 19
72074 Tübingen, Germany
jessie.nixon at uni-tuebingen.de

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