Objective
How do infants learn their first language(s)? The popular yet controversial 'statistical learning hypothesis' posits that they learn by gradually collecting statistics over their language inputs. This is strickingly similar to how current AI's Large Language Models (LLMs) learn and shows that simple statistical mechanisms may be sufficient to attain adult-like language competence. But does it? Estimates of language inputs to children show that by age 3, they have received 2 or 3 orders of magnitude less data than LLMs of similar performance. And the gap grows exponentially larger with children's age. Worse, when models are fed with speech instead of text they learn even slower. How are infants so efficient learners?
This project tests the hypothesis that in addition to statistical learning, infants benefit from 3 mechanisms that accelerate their learning rate. (1) They are born with a \textit{vocal tract} which helps them understand the link between abstract motor commands and speech sounds, and decode noisy speech inputs more efficiently. (2) They have an \textit{episodic memory} enabling them to learn from unique events, instead of gradually learning from thousands of repetitions. (3) They start with a \textit{evolved learning architecture} optimized for generalisation from few and noisy inputs.
Our approach is to build a computational model of the learner (an infant simulator), which when fed by realistic language input produces outcome measures comparable to children's (laboratory experiments, vocabulary estimates). This gives a quantitative estimate of the efficiency of each 3 mechanisms, as well as new testable predictions. We start with English and French that have both accessible large annotated speech corpora and documented acquisition landmarks and focus on the first three years of life. We then help building similar resources across a larger set of languages by fostering a cross-disciplinary community that shares tools, data and analysis methods.
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2023-ADG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
75270 Paris
France
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.