Objective
Intelligent agents make good decisions in novel environments. Understanding how humans deal with novelty
is a key problem in the cognitive and neural sciences, and building artificial agents that behave effectively
with 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 by
previously 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 existing
neural 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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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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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H2020-EU.1.1. - EXCELLENT SCIENCE - 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.
ERC-COG - Consolidator Grant
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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-2016-COG
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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.
OX1 2JD Oxford
United Kingdom
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.