EMCOREP

Emergence of complex internal representations in humans

 Coordinatore KOZEP-EUROPAI EGYETEM 

 Organization address address: Nador utca 9
city: BUDAPEST
postcode: 1051

contact info
Titolo: Ms.
Nome: Gabor
Cognome: Zsuzsanna
Email: send email
Telefono: +36 1 235 6183
Fax: +36 1 328 3414

 Nazionalità Coordinatore Hungary [HU]
 Totale costo 75˙000 €
 EC contributo 75˙000 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-PEOPLE-2013-CIG
 Funding Scheme MC-CIG
 Anno di inizio 2013
 Periodo (anno-mese-giorno) 2013-09-01   -   2016-08-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    KOZEP-EUROPAI EGYETEM

 Organization address address: Nador utca 9
city: BUDAPEST
postcode: 1051

contact info
Titolo: Ms.
Nome: Gabor
Cognome: Zsuzsanna
Email: send email
Telefono: +36 1 235 6183
Fax: +36 1 328 3414

HU (BUDAPEST) coordinator 100˙000.00

Mappa


 Word cloud

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behavioural    learning    computational    sensory    human    domains    uniquely    explore   

 Obiettivo del progetto (Objective)

'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.'

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