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Principal Collaborator : Abdellah Fourtassi
Labotary : Laboratoire d'Informatique et Systèmes
Expected competencies 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? 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 limitations in traditional in-lab research methods typically used to study this question. Our team (cocodev.fr) proposes a research approach that goes beyond these limitations. First, we use more ecologically valid designs to collect child-caregiver conversations at home (either via zoom calls or face-to-face) across many cultures. Second, we use deep learning modeling techniques, which allow us to bridge several dimensions of conversational complexity and identify their cognitive mechanisms. This research approach provides a formal framework where we can articulate, contrast, and adjudicate between several hypotheses, answering crucial, lingering scientific questions about the universals of conversational development and its cognitive mechanisms.
Apply here: applications.ilcb.fr