Morphological processing without semantics: An ERP study with spoken words E. Beyersmann, D. Bolger, C. Pattamadilok, B. New, J. Grainger and J. C. Ziegle.r

2019 Cortex

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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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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Information-oriented and cross-language aspects on speech and cortical rhythms

Posted on 08/14/201802/20/2019 by ilcb team

During the last two decades a growing body of evidence has shown a close relationship between temporal structure of speech and neural oscillatory activities, especially in the theta and gamma bands. More specifically, several recent models suggest that the neural capacity to track speech dynamics and rhythmic patterns is crucial for speech processing and understanding. […]