Obiettivo Intelligent agents make good decisions in novel environments. Understanding how humans deal with noveltyis a key problem in the cognitive and neural sciences, and building artificial agents that behave effectivelywith novel settings remains an unsolved challenge in machine learning. According to one view, humans form abstract representations that encode latent variables pertaining to the high-level structure of the environment (a “model” of the world). These abstractions facilitate generalisation of extant task and category information to novel domains. For example, an individual who can ride a bicycle, or speak Spanish, will learn more rapidly to ride a motorcycle, or speak Portuguese. However, the neural basis for these abstractions, and the computational underpinnings of high-level generalisation, remain largely unexplored topics in cognitive neuroscience. In the current proposal, we describe 4 experimental series in which humans learn to perform structured decision-making tasks, and then generalise this behaviour to input domains populated bypreviously unseen stimuli, categories, or tasks. Building on extant pilot work, we will use representational similarity analysis (RSA) of neuroimaging (fMRI or EEG) data to chart the emergence of neural representations encoding abstract structure in patterns of brain activity. We will then assess how the formation of these abstractions at the neural level predicts successful human generalisation to previously unseen contexts. Our proposal is centered around a new theory, that task generalisation depends on the formation of low-dimensional population codes in the human dorsal stream, that are scaffolded by existingneural basis functions for space, value and number. The work will have important implications for psychologists and neuroscientists interested in decision-making and executive function, and for machine learning researchers seeking to build intelligent artificial agents. Campo scientifico natural sciencesbiological sciencesneurobiologycognitive neurosciencenatural sciencescomputer and information sciencesartificial intelligencemachine learning Programma(i) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Argomento(i) ERC-2016-COG - ERC Consolidator Grant Invito a presentare proposte ERC-2016-COG Vedi altri progetti per questo bando Meccanismo di finanziamento ERC-COG - Consolidator Grant Istituzione ospitante THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD Contribution nette de l'UE € 1 999 775,00 Indirizzo WELLINGTON SQUARE UNIVERSITY OFFICES OX1 2JD Oxford Regno Unito Mostra sulla mappa Regione South East (England) Berkshire, Buckinghamshire and Oxfordshire Oxfordshire Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 1 999 775,00 Beneficiari (1) Classifica in ordine alfabetico Classifica per Contributo netto dell'UE Espandi tutto Riduci tutto THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD Regno Unito Contribution nette de l'UE € 1 999 775,00 Indirizzo WELLINGTON SQUARE UNIVERSITY OFFICES OX1 2JD Oxford Mostra sulla mappa Regione South East (England) Berkshire, Buckinghamshire and Oxfordshire Oxfordshire Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 1 999 775,00