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Emergence of complex internal representations in humans


Human learning is uniquely complex and powerful in terms of its efficiency, its level of abstraction and its range of skills compared to any other species. The proposed research will investigate the fundamental cognitive and computational foundations of human sensory learning in adults and infants in an integrated interdisciplinary manner. We will carry out behavioural studies, computational analyses and theoretical work, and the questions in each of these domains will be rigorously linked to the topics explored in the other domains. The behavioural studies will question the generally upheld notion that low-level statistical learning of regularities and high-level “rule learning” of explicit rules, hierarchical memories, and emerging abstract social notions must be two fundamentally different and separate types of processes in the brain. Together with the computational analyses, they will explore how short and long-term memory is involved in acquiring new knowledge based on sensory experience. In the domain of theory, we will explore the idea that instead of simple associative learning, what human cortical learning can be best captured by is an optimal probabilistic computation based on previously developed internal models of the world. Finally, the inclusion of both adult and child studies that are essentially identical in their structure, we will test the generality of our findings giving a uniquely synergetic flavor to our approach.

Call for proposal

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Nador Utca 9
1051 Budapest

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Activity type
Higher or Secondary Education Establishments
Administrative Contact
Gabor Zsuzsanna (Ms.)
EU contribution
€ 100 000