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L2STAT SIGNED

Statistical learning and L2 literacy acquisition: Towards a neurobiological theory of assimilating novel writing systems

Total Cost €

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EC-Contrib. €

0

Partnership

0

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 L2STAT project word cloud

Explore the words cloud of the L2STAT project. It provides you a very rough idea of what is the project "L2STAT" about.

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Project "L2STAT" data sheet

The following table provides information about the project.

Coordinator
THE HEBREW UNIVERSITY OF JERUSALEM 

Organization address
address: EDMOND J SAFRA CAMPUS GIVAT RAM
city: JERUSALEM
postcode: 91904
website: www.huji.ac.il

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
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 Coordinator Country Israel [IL]
 Total cost 2˙500˙000 €
 EC max contribution 2˙500˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-AdG
 Funding Scheme ERC-ADG
 Starting year 2016
 Duration (year-month-day) from 2016-07-01   to  2021-06-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    THE HEBREW UNIVERSITY OF JERUSALEM IL (JERUSALEM) coordinator 1˙700˙000.00
2    BCBL BASQUE CENTER ON COGNITION BRAIN AND LANGUAGE ES (SAN SEBASTIAN) participant 800˙000.00

Map

 Project objective

The overarching goal of L2STAT is to understand L2 literacy acquisition by bringing together, for the first time, recent advances in the neurobiology of statistical learning (SL), a detailed statistical characterization of the world’s writing systems, and neurally-plausible general principles of learning, representation, and processing. L2STAT aims to provide a new theoretical framework that considers L2 learning and SL a two-way street: SL, on the one hand, tunes learners to the regularities of a new linguistic environment, and on the other hand, L2 environment shapes learners’ sensitivity to its specific types of statistical properties. The project will focus on the assimilation of reading skills in four novel linguistic environments, and investigate how exposure to their distinct writing systems shape, in turn, SL. L2STAT is an interdisciplinary project that launches in parallel five mutually informative research axes: 1) we employ advanced methods from computational linguistics and machine learning to precisely characterize the statistics of four highly contrasting writing systems (English, Spanish, Hebrew, Chinese). 2) We study the learning that results from biologically-inspired computational models that are exposed to these statistics, to generate a priori predictions regarding what statistical properties can (or cannot) be learned, and how neural mechanisms constrain the representations learned during L2 literacy acquisition. 3) We develop psychometrically reliable behavioral tests of individuals’ capacities to extract regularities in the visual and auditory modalities. 4) We use state of the art neuroimaging techniques including EEG, MEG, fMRI to probe the neurobiological underpinning for detecting regularities in the visual and auditory modalities. 5) We conduct behavioral experimentation in four sites (Israel, Spain, Taiwan to track literacy acquisition longitudinally in the four different languages.

 Publications

year authors and title journal last update
List of publications.
2018 Noam Siegelman, Louisa Bogaerts, Amit Elazar, Joanne Arciuli, Ram Frost
Linguistic entrenchment: Prior knowledge impacts statistical learning performance
published pages: 198-213, ISSN: 0010-0277, DOI: 10.1016/j.cognition.2018.04.011
Cognition 177 2019-10-09
2019 Henry Brice, William Einar Mencl, Stephen J. Frost, Atira Sara Bick, Jay G. Rueckl, Kenneth R. Pugh, Ram Frost
Neurobiological signatures of L2 proficiency: Evidence from a bi-directional cross-linguistic study
published pages: 7-16, ISSN: 0911-6044, DOI: 10.1016/j.jneuroling.2018.02.004
Journal of Neurolinguistics 50 2019-08-30
2016 Noam Siegelman, Louisa Bogaerts, Morten H. Christiansen, Ram Frost
Towards a theory of individual differences in statistical learning
published pages: 20160059, ISSN: 0962-8436, DOI: 10.1098/rstb.2016.0059
Philosophical Transactions of the Royal Society B: Biological Sciences 372/1711 2019-06-14
2017 Martijn Baart, Blair C. Armstrong, Clara D. Martin, Ram Frost, Manuel Carreiras
Cross-modal noise compensation in audiovisual words
published pages: 42055, ISSN: 2045-2322, DOI: 10.1038/srep42055
Scientific Reports 7 2019-06-14
2016 Blair C. Armstrong, Ram Frost, Morten H. Christiansen
The long road of statistical learning research: past, present and future
published pages: 20160047, ISSN: 0962-8436, DOI: 10.1098/rstb.2016.0047
Philosophical Transactions of the Royal Society B: Biological Sciences 372/1711 2019-06-14
2017 Noam Siegelman, Louisa Bogaerts, Ofer Kronenfeld, Ram Frost
Redefining “Learning” in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?
published pages: , ISSN: 0364-0213, DOI: 10.1111/cogs.12556
Cognitive Science 2019-06-14
2018 Louisa Bogaerts, Noam Siegelman, Tali Ben-Porat, Ram Frost
Is the Hebb repetition task a reliable measure of individual differences in sequence learning?
published pages: 17470218.2017.1, ISSN: 1747-0218, DOI: 10.1080/17470218.2017.1307432
Quarterly Journal of Experimental Psychology 2019-06-14

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