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Neural mechanisms of learning in the infant brain : from Statistics to Rules and Symbols


With BABYLEARNGhislaine Dehaene-Lambertz seeks to explain the specificities of the functional neural architecture that are critical for learning in humans from the very beginning of the cortical circuits.

european funding: European Research Council (ERC) Advanced Grant

Call ERC-2015-ADG

Published on 1 September 2016


The newborn is the most powerful learner: He learns in a few months to master language, complex social interactions, etc. Powerful statistical algorithms, acting simultaneously at different levels of functional hierarchies, have been proposed to explain learning. Ghislaine Dehaene proposes that two other elements are crucial. The first is the particular human brain architecture that constrains statistical calculations. The second is the human capacity to access a rich symbolic system. Six working groups are planned. They use the complementary information offered by non-invasive brain imaging techniques (EEG, MRI and optical topography) to understand the neural bases of statistical computation and symbolic competence in the infant, from 6 months of gestation to the end of the first year of life.

WP1 studies at what preterm age statistical inferences can be demonstrated.
WP2 studies the consequences of a different preterm environment (in-utero versus ex-utero) on early statistical computations in the visual and auditory domains and their consequences on ongoing brain activity throughout the first year of life.
WP3 explores the neural basis of how children infer word meaning and word category, and in particular the role of left perisylvian areas and their particular connectivity.
WP4 investigates the symbolic competence of infants. Several criteria are proposed (generalization, bidirectionality, use of algebraic rules and logical operations) and tested in successive experiments to clarify the symbolic abilities of infants in the first half of life.
Work Packages 5-6 are transversal to Work Packages 1-4: Work Package 5 uses MRI to obtain precise functional localization and maturation markers correlated with functional outcomes. In WP6, new tools are developed to combine and analyze multimodal brain images.

Ghislaine Deheane-Lambertz hopes to clarify the specificities of neuronal functional architecture that are essential for human learning from the beginning of cortical circuits.

Project duration
5 years
2,5 millions €


Starting date
September 01, 2016

Researcher : Ghislaine Dehaene-Lambertz

Contact :  Ghislaine Dehaene

agreement id : 695710


ERC Advanced Grants enable outstanding researchers of established reputation, regardless of nationality or age, to pursue innovative, high-risk projects that break new ground in their discipline or other fields.

These grants are intended for researchers who have already established themselves as distinguished independent investigators.

There are no nationality or age requirements: applicants must be scientifically independent and have a recent research record and profile that establishes them as leaders in their field(s) of specialization.