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An information maximization model of eye movementsWe propose a sequential information maximization model as a general strategy for programming eye movements. The model reconstructs high-resolution visual information from a sequence of fixations, taking into account the fall-off in resolution from the fovea to the periphery. From this framework we get a simple rule for predicting fixation sequences: after each fixation, fixate next at the location that minimizes uncertainty (maximizes information) about the stimulus. By comparing our model performance to human eye movement data and to predictions from a saliency and random model, we demonstrate that our model is best at predicting fixation locations. Modeling additional biological constraints will improve the prediction of fixation sequences. Our results suggest that information maximization is a useful principle for programming eye movements.
Document ID
20050231781
Document Type
Reprint (Version printed in journal)
Authors
Renninger, Laura Walker (Smith-Kettlewell Eye Research Institute United States)
Coughlan, James
Verghese, Preeti
Malik, Jitendra
Date Acquired
August 23, 2013
Publication Date
January 1, 2005
Publication Information
Publication: Advances in neural information processing systems
Volume: 17
ISSN: 1049-5258
Subject Category
Life Sciences (General)
Funding Number(s)
CONTRACT_GRANT: H1-33G030080
CONTRACT_GRANT: NAG9-1461
Distribution Limits
Public
Copyright
Other
Keywords
NASA Discipline Neuroscience
Non-NASA Center