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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.

eeg    linguistics    world    skills    experimentation    employ    psychometrically    machine    axes    computational    regularities    priori    predictions    israel    statistics    biologically    sl    principles    individuals    taiwan    street    shape    underpinning    neural    learners    extract    hebrew    track    contrasting    neurobiology    turn    informative    regarding    mutually    learning    shapes    generate    learned    environments    assimilation    writing    meg    precisely    exposure    tunes    modalities    types    l2    framework    languages    conduct    detecting    interdisciplinary    theoretical    neurobiological    inspired    plausible    parallel    fmri    l2stat    overarching    neuroimaging    behavioral    exposed    statistical    environment    acquisition    representation    linguistic    spanish    launches    probe    understand    first    techniques    sensitivity    sites    mechanisms    reading    literacy    spain    english    tests    auditory    longitudinally    characterization    capacities    chinese    neurally    time    visual    models    representations   

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.
telephone: 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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