Language evolution/Language development/Cognitive development

PhD supervisor : Isabelle Dautriche

Labotary : Laboratoire de Psychologie Cognitive

Keywords : Infants behavioural experiments; comparative cognition; core knowledge; language primitives; communication; language design

Expected competences of the candidate : Essential - A PhD in developmental psychology/developmental linguistics/neurolinguistics/cognitive sciences - Experience in carrying out empirical research (e.g., experimental design, recruitment, participant testing, quantitative data analysis, report writing). - Expertise using statistical packages and/or programming languages to analyse data - A demonstrable ability to present research ideas clearly to people from a range of disciplinary backgrounds. - Excellent written and spoken English. Desirable - Knowledge of French (to interact with the participating families and for your general well-being in France) is preferred but not required.

Summary of a pre-proposal : During the second year of life, children undergo massive changes in their language abilities: they do not only learn words at an impressive rate but they also understand words faster. This project will investigate how specific cognitive non-linguistic factors (i.e., working memory, executive functions) and linguistic factors (e.g., onset of word production) may contribute to the development of word comprehension abilities in the second year of life. Ideally, young infants will be longitudinally evaluated using a set of linguistic and non-linguistic experimental and neuropsychological tasks that may account for individual differences in language comprehension developmental trajectory. The research will be conducted in our brand new Babylab and will involve behavioral (eye-tracking) and neuroimaging (EEG) methods. -- Please contact me if you have a project of your own.

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Formal syntactic dependencies syntax and syntax/semantics interface, the structure and computation

PhD supervisor : Viviane Deprez

Labotary : Laboratoire Parole et Langage

Keywords : Syntax, semantics, negation, questions, language deficiencies and acquisition, motor system

Expected competences of the candidate : experimental design, statistical analysis, basic linguistic knowledge

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Towards a quantitative theory of child conversational development

PhD supervisorAbdellah Fourtassi

Labotary : Laboratoire d'Informatique & Systèmes

Keywords : child conversational development, deep learning modeling, unmoderated data collection, cross-cultural comparison, cognitive mechanisms 

Expected competences of the candidate : The project is highly interdisciplinary and we accept applications from researchers with an experimental background to supervise data collection and analysis and researchers with a background in computational modeling (especially in deep learning techniques) to help with modeling child conversational coordination from multimodal data 

Summary of the pre-proposal : How do children become able to use language to engage in coordinated conversations? The answer to this question has a far-reaching societal impact. Indeed, this development allows us to achieve crucial — socially mediated — goals such as learning from more knowledgeable people, expressing thoughts and needs, convincing others, collaborating with peers. If impaired, it can have negative consequences from the risk of developing mental health issues to the quality of academic attainment and employability (Murphy et al., 2004). Research has shown that this development takes several years to mature, spanning most of middle childhood (7 to 12 years). Yet, while there is a large body of research investigating children’s acquisition of linguistic structures such as phonology and syntax, in comparison, little is known about how children learn to translate this knowledge into conversational skills such as turn-taking management, negotiating shared understanding with the interlocutor, and the ability for a coherent exchange. This slow scientific progress can be attributed largely to methodological limitations in traditional research methods typically used to study this question. Our team ( proposes a research approach that goes far beyond the limitations of existing methods, allowing breakthroughs in our understanding of this phenomenon. Our approach combines two highly innovative methods in the field: 1) Unmoderated designs allowing large-scale cross-cultural data acquisition from children and 2) Deep learning modeling techniques, which will allow us to bridge across several dimensions of conversational complexity and to identify their cognitive mechanisms. This research approach will allow us to lay the groundwork for the first quantitative cognitive theory of conversational development. This theory will provide a formal framework that will allow us (and other researchers in the field) to articulate, contrast, and adjudicate between several hypotheses, answering crucial, lingering scientific questions about the universals of conversational development and its cognitive mechanisms. We coordinate data collection across 20 countries with members of our international community of Multimodal Child Conversation (MCC) ( 


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