MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • CSAIL Digital Archive
  • CSAIL Technical Reports (July 1, 2003 - present)
  • View Item
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • CSAIL Digital Archive
  • CSAIL Technical Reports (July 1, 2003 - present)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A Bayesian inference theory of attention: neuroscience and algorithms

Author(s)
Chikkerur, Sharat; Serre, Thomas; Poggio, Tomaso
Thumbnail
DownloadMIT-CSAIL-TR-2009-047.pdf (1.224Mb)
Additional downloads
MIT-CSAIL-TR-2009-047.ps (1.941Mb)
Other Contributors
Center for Biological and Computational Learning (CBCL)
Advisor
Tomaso Poggio
Metadata
Show full item record
Abstract
The past four decades of research in visual neuroscience has generated a large and disparate body of literature on the role of attention [Itti et al., 2005]. Although several models have been developed to describe specific properties of attention, a theoretical framework that explains the computational role of attention and is consistent with all known effects is still needed. Recently, several authors have suggested that visual perception can be interpreted as a Bayesian inference process [Rao et al., 2002, Knill and Richards, 1996, Lee and Mumford, 2003]. Within this framework, topdown priors via cortical feedback help disambiguate noisy bottom-up sensory input signals. Building on earlier work by Rao [2005], we show that this Bayesian inference proposal can be extended to explain the role and predict the main properties of attention: namely to facilitate the recognition of objects in clutter. Visual recognition proceeds by estimating the posterior probabilities for objects and their locations within an image via an exchange of messages between ventral and parietal areas of the visual cortex. Within this framework, spatial attention is used to reduce the uncertainty in feature information; feature-based attention is used to reduce the uncertainty in location information. In conjunction, they are used to recognize objects in clutter. Here, we find that several key attentional phenomena such such as pop-out, multiplicative modulation and change in contrast response emerge naturally as a property of the network. We explain the idea in three stages. We start with developing a simplified model of attention in the brain identifying the primary areas involved and their interconnections. Secondly, we propose a Bayesian network where each node has direct neural correlates within our simplified biological model. Finally, we elucidate the properties of the resulting model, showing that the predictions are consistent with physiological and behavioral evidence.
Date issued
2009-10-03
URI
http://hdl.handle.net/1721.1/49416
Series/Report no.
CBCL-280MIT-CSAIL-TR-2009-047

Collections
  • CSAIL Technical Reports (July 1, 2003 - present)

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.