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Curiosity and the Development of the Hidden Foundations of Cognition
How do human infants develop complex cognition? We propose that artificial intelligence (AI) provides crucial insight into human curiosity-driven learning and the development of infant cognition. Deep learning—a technology that ha...
How do human infants develop complex cognition? We propose that artificial intelligence (AI) provides crucial insight into human curiosity-driven learning and the development of infant cognition. Deep learning—a technology that has revolutionised AI—involves the acquisition of informative internal representations through pre-training, as a critical precursory step to learning any specific task. We propose that, similarly, curiosity guides human infants to develop ‘hidden’ mature mental representations through pre-training well before the manifestation of behaviour. To test this proposal, for the first time we will use neuroimaging to measure the hidden changes in representations during infancy and compare these to predictions from deep learning in machines. Research Question 1 will ask how infants guide pre-training through directed curiosity, by testing quantitative models of curiosity adapted from developmental robotics. We will also test the hypothesis from pilot data that the fronto-parietal brain network guides curiosity from the start. Research Question 2 will further test the parallel with deep learning by characterising the developing infant’s mental representations within the visual system using the powerful neuroimaging technique of representational similarity analysis. Research Question 3 will investigate how individual differences in curiosity affect later cognitive performance, and test the prediction from deep learning that the effects of early experience during pre-training grow rather than shrink with subsequent experience. Finally, Research Question 4 will test the novel prediction from deep learning that, following perinatal brain injury, pre-training creates resilience provided that curiosity is intact. The investigations will answer the overarching question of how pre-training learning lays the foundations for cognition and pioneer the new field of Computational Developmental Cognitive Neuroscience.
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Duración del proyecto: 81 meses
Fecha Inicio: 2018-09-24
Fecha Fin: 2025-06-30
Fecha Fin: 2025-06-30
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2018-09-24
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2017-ADG:
ERC Advanced Grant
Cerrada
hace 7 años
Presupuesto
El presupuesto total del proyecto asciende a
3M€
Líder del proyecto
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Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
111.78K€ |
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