Bringing together experts in linguistics, neuroscience, psychology, medicine and computer science to understand and to model the way that language functions.
The objective is to create a generic model of the processing of language and its cerebral bases.
FEATURED PUBLICATIONS
Error-based learning and lexical competition in word production: Evidence from multilingual naming Elin Runnqvist, Kristof Strijkers, Albert Costa
2019 PLOS/ONE
Constraints on the lexicons of human languages have cognitive roots present in baboons (Papio papio) Emmanuel Chemla, Isabelle Dautriche, Brian Buccola, and Joël Fagot. 2019.
PNAS
High-fidelity copying is not necessarily the key to cumulative cultural evolution: a study in monkeys and children Carmen Saldana, Joël Fagot, Simon Kirby, Kenny Smith, Nicolas Claidière. 2019.
Proceedings of the royal societty B
Which way to the dawn of speech?: Reanalyzing half a century of debates and data in light of speech science Louis-Jean Boë, Thomas R. Sawallis, Joël Fagot, Pierre Badin, Guillaume Barbier, Guillaume Captier, Lucie Ménard, Jean-Louis Heim, Jean-Luc Schwartz. 2019.
ScienceAdvances
A stimulus-brain coupling analysis of regular and irregular rhythms in adults with dyslexia and controls Anna Fiveash, Daniele Schön, Laure-Hélène Canette, Benjamin Morillon, Nathalie Bedoin, Barbara Tillmann. 2019.
ELSEVIER
MASTER
The MaSCo, a new MA in Cognitive Science, provides an advanced scientific curriculum on human cognition, as well as a technological and methodological expertise in evaluation, analysis and modeling of cognitive processes.
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PhD PROGRAM
An interdisciplinary focus on language research, with interdisciplinary theory and practice trainings at basic and advanced level courses in all relevant disciplines.
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SUMMER SCHOOL
Introductory and advanced classes in four core fields of Cognitive Science: applied mathematics, statistics & networks, neuroscience & behavior, language & cognition, computer science & machine learning.
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TRAINING
Advanced trainings are offered to the ILCB members
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Cross-cutting topics
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What can be learnt on language by studying animals without language?
Language being unique to the human cognitive system, one could logically consider that it can only be studied in the only species that features one: humans. Yet, a number of ILCB/BLRI researchers use animals including rodents, pet dogs, macaques and baboons to address the question of the cognitive and cerebral bases of language.
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The main goal is to closely examine several models of the anatomo-functional architecture of language in the brain (Friederici, Hagoort, Poeppel, Price) to determine if and how they are influenced by linguistic theories ((Bybee, Chomsky, Fillmore, Goldberg, Jackendoff, Kay…). This implies reviewing, understanding and synthesizing the different models and their implications. The ultimate goal is to reach a better understanding of how to bridge the gap between linguistics and neurolinguistics. The various expertise within the BLRI/ILCB are very well-suited to address these general issues.
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Do language and action share the same integrated system? Does Speech have a gestural origin? Could motor tasks help recovering from some language impairments?
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- Conversational interactions as the primary frame of reference for studies on brain-language relationships
- New paradigms for investigating the neurophysiological and cognitive bases of conversational interactions
- Analytical tools for the characterization of between-individual coordination and information transfer in conversational interactions
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In this Transversal Questions we will develop new methods to tackle with the challenge of extracting and characterizing brain functional connectivity networks and their temporal evolution, in strict relation with the underlying anatomy and the performed cognitive tasks. We will apply these new tools to the analysis of specific datasets, in particular about cognitive experiments involving language analysis and production.
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Machine learning and deep learning are powerful tools for analyzing and modeling data from neuroscience experiments in order to answer specific questions. All the work to be done to push forward the research in the field of the ILCB within this QT is grouped in three axes.
The first axis is about learning from data, Machine Learning and Deep Learning.
The second point concerns the design of machine learning systems for brain data.
The third axis focuses on the comparison of mental representations and computer representations
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In the first two years of life, most children develop the phonetic tools scaffolding first language acquisition, both at the segmental and prosodic level. In doing so, children face the following challenges…
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Cerebral Processing of Voice Information: From Monkeys to Deep Learning
How do we extract and process the treasure-trove of information in voices? Experiments involving techniques ranging from monkey neuroimaging to deep learning provide an increasingly detailed picture of the ‘vocal brain’ and its evolution in primates. Results suggest an organization in several ‘voice patches’ analogous to that of the face patch system. Pascal Belin, Institut […]
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