Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Bayesian integration of visual and auditory signals for spatial localization
 

Summary: Bayesian integration of visual and auditory
signals for spatial localization
Peter W. Battaglia, Robert A. Jacobs, and Richard N. Aslin
Department of Brain and Cognitive Sciences and The Center for Visual Science, University of Rochester, Rochester,
New York 14627
Received September 11, 2002; revised manuscript received January 21, 2003; accepted February 20, 2003
Human observers localize events in the world by using sensory signals from multiple modalities. We evalu-
ated two theories of spatial localization that predict how visual and auditory information are weighted when
these signals specify different locations in space. According to one theory (visual capture), the signal that is
typically most reliable dominates in a winner-take-all competition, whereas the other theory (maximum-
likelihood estimation) proposes that perceptual judgments are based on a weighted average of the sensory sig-
nals in proportion to each signal's relative reliability. Our results indicate that both theories are partially
correct, in that relative signal reliability significantly altered judgments of spatial location, but these judg-
ments were also characterized by an overall bias to rely on visual over auditory information. These results
have important implications for the development of cue integration and for neural plasticity in the adult brain
that enables humans to optimally integrate multimodal information. 2003 Optical Society of America
OCIS codes: 330.0330, 330.1400, 330.4060, 330.7320.
1. INTRODUCTION
The ability to localize a stimulus in the environment is
based on a complex mapping of sensory signals that leads

  

Source: Aslin, Richard N. - Department of Brain and Cognitive Sciences, University of Rochester
Jacobs, Robert A. - Departments of Brain and Cognitive Sciences & Computer Science, University of Rochester

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences