OPTIMEYES

Optimal Control of Eye Movements

 Coordinatore THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE 

 Organization address address: The Old Schools, Trinity Lane
city: CAMBRIDGE
postcode: CB2 1TN

contact info
Titolo: Ms.
Nome: Renata
Cognome: Schaeffer
Email: send email
Telefono: +44 1223 333543
Fax: +44 1223 332988

 Nazionalità Coordinatore United Kingdom [UK]
 Totale costo 180˙603 €
 EC contributo 180˙603 €
 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-2009-IEF
 Funding Scheme MC-IEF
 Anno di inizio 2010
 Periodo (anno-mese-giorno) 2010-05-01   -   2012-04-30

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE

 Organization address address: The Old Schools, Trinity Lane
city: CAMBRIDGE
postcode: CB2 1TN

contact info
Titolo: Ms.
Nome: Renata
Cognome: Schaeffer
Email: send email
Telefono: +44 1223 333543
Fax: +44 1223 332988

UK (CAMBRIDGE) coordinator 180˙603.20

Mappa

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 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

performance    machine    experiments    strategy    humans    visual    environment    framework    learning    eye    human    planning    explore    movement    noisy    optimal    stimuli   

 Obiettivo del progetto (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'

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