Objective
Optimal performance in a noisy and ambiguous environment requires that the human brain performs computations that are adapted to these conditions. Cognitive neuroscience has seen a major progress by applying Bayesian decision theory to explain human behaviour when humans were confronted with tasks where perception or behavioural outcomes were uncertain. In addition to these advancements, machine learning methods were successfully developed for handling noisy and incomplete datasets. In this research proposal we take an interdisciplinary approach, in which we design human motor control experiments and evaluate optimality of human performance by using machine learning techniques. Specifically, we will use eye-tracking experiments to explain how learning about visual stimuli supports the design of optimal eye movement strategies. Humans explore the visual environment by actively sampling the stimuli through performing a sequence of saccades. Limited time and resources require efficient eye movement planning and an optimal strategy necessitates the adaptation of the eye movement strategy both to the statistics of the stimuli and to the task performed. We develop a framework in which the contribution of top-down (task specific) and bottom-up information (low-level) to eye movement planning can be controlled and assessed. During the course of this proposal we intend to address three problems. First, we will explore how learning novel stimuli in the perceptual domain contributes to eye movement planning. Second, we will develop an optimal learner that relies on the same information that human participants have and assess whether human eye movements optimally exploit available information. Third, we will explore how bottom-up and top-down information is integrated and will use a probabilistic framework to analyze whether the optimal integration is compatible with human performance. This proposal strongly builds on a close collaboration between Prof. Wolpert and mysel
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.
- natural sciences biological sciences neurobiology cognitive neuroscience
- natural sciences computer and information sciences artificial intelligence machine learning
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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.
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.
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.
FP7-PEOPLE-2009-IEF
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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.
Coordinator
CB2 1TN CAMBRIDGE
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.