Modèles computationnels du développement cognitif


Research center

45 rue d’Ulm
75230 Paris
Marc Mézard


Ecole Normale Supérieure
Ecole des Hautes Etudes en Sciences Sociales
ED 158


Laboratoire de Sciences Cognitives et Psycholinguistique
UMR 8554
Labex IEC

Mots clefs

Neurosciences computationnelles
acquisition du langage
machine learning
traitement du signal


Ngon, Martin, #Dupoux, et al .(2013). Nonwords, nonwords, nonwords. Developmental Science, 16(1), 24-34.

Minagawa-Kawai, ... & #Dupoux (2013). Insights on NIRS sensitivity. Frontiers in Language Sciences, 4(170.

Martin , Peperkamp, & #Dupoux (2013). Learning Phonemes with a Proto-lexicon. Cognitive Science, 37, 103-124.

Jansen, #Dupoux, et al. (2013). Zero resource speech technologies and models of early language acquisition. Proceedings of ICASSP 2013.

Cristia, #Dupoux, et al. (2013). An online database of infant functional Near InfraRed Spectroscopy. PLoS One, 8(3), e58906.

Fields of research

Cognitive neurosciences / neuropsychology /neuroeconomy

Research Theme

During the last thirty years, developmental psychology have documented the surprising speed and robustness with which babies learn the linguistics and social characteristics of their maternal environment. During the first year alone, infants achieve impressive landmarks regarding three key language components (see Figure 1). First, they tune into the phonemic categories (consonants and vowels) of their language, i.e., they lose the ability to distinguish some fine phonetic contrasts that belong to the same category, enhance their ability to distinguish some between-category contrasts, and refine their ability to ignore acoustic variations due to speaker characteristics. Second, infants refine their ability to segment the speech stream, from large prosodic units to small ones, and determine the suprasegmental features that are relevant for word recognition. Third, infants start to extract very frequent word patterns from their environnment, and compile segmentation strategies that help them constructing a recognition lexicon. These aspects of language are learned efforlessly even through they are difficult for adults to learn later in life. Moroever, they are learned without direct parental supervision. Finally, and most suprisingly, the acquisition of these three components is not done sequentially, but in a largely overlapping fashion.


Etudiants ENP

Maria Julia CARBAJAL

Membres de l'équipe

CAO Xuan-Nga

Lab rotation

Coding of speech sounds in the brain: data from humans, brains, and machines

Chercheur responsable: 

DUPOUX Emmanuel


1 September 2016 - 31 December 2016

Date limite de candidature: 

1 September 2016

Lab rotation proposal: 3 months 

~ Sep-Dec 2016 ~ Jan-March 2017 ~ Apr-June 2017


How the brain encodes speech sounds is still not well known. The internship will consist in analysing the response characteristics of brain cells (Ecog recordings in ferrets) and artificial deep neural nets in representing speech sounds, analyzed in terms of their ability to sustain invariant phonetic representations. The internship requires good command of high dimensional data analysis (Matlab or R). See the web site of the team for more details on the context of this project (

Address:  Laboratoire de Sciences Cognitives et Psycholinguistique - Ecole Normale Supérieure - 29, rue d'Ulm 75005 Paris 

Phone number: +33 1 44 32 26 17 ;



Emmanuel DUPOUX