Document Detail

Pattern recognition in correlated and uncorrelated noise.
MedLine Citation:
PMID:  19884919     Owner:  NLM     Status:  MEDLINE    
This study examined how correlated, or filtered, noise affected efficiency for recognizing two types of signal patterns, Gabor patches and three-dimensional objects. In general, compared with the ideal observer, human observers were most efficient at performing tasks in low-pass noise, followed by white noise; they were least efficient in high-pass noise. Simulations demonstrated that contrast-dependent internal noise was likely to have limited human performance in the high-pass conditions for both signal types. Classification images showed that observers were likely adopting different strategies in the presence of low-pass versus white noise. However, efficiencies were underpredicted by the linear classification images and asymmetries were present in the classification subimages, indicating the influence of nonlinear processes. Response consistency analyses indicated that lower contrast-dependent internal noise contributed somewhat to higher efficiencies in low-pass noise for Gabor patches but not objects. Taken together, the results of these experiments suggest a complex interaction among signals, external noise spectra, and internal noise in determining efficiency in correlated and uncorrelated noise.
Brianna Conrey; Jason M Gold
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Journal of the Optical Society of America. A, Optics, image science, and vision     Volume:  26     ISSN:  1520-8532     ISO Abbreviation:  J Opt Soc Am A Opt Image Sci Vis     Publication Date:  2009 Nov 
Date Detail:
Created Date:  2009-11-03     Completed Date:  2010-01-12     Revised Date:  2014-09-15    
Medline Journal Info:
Nlm Unique ID:  9800943     Medline TA:  J Opt Soc Am A Opt Image Sci Vis     Country:  United States    
Other Details:
Languages:  eng     Pagination:  B94-109     Citation Subset:  IM    
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MeSH Terms
Computer Simulation
Differential Threshold / classification
Discrimination Learning
Models, Statistical
Monte Carlo Method
Normal Distribution
Pattern Recognition, Visual*
Reproducibility of Results
Signal Detection, Psychological*
Grant Support
EY015787/EY/NEI NIH HHS; EY019265/EY/NEI NIH HHS; R01 EY019265/EY/NEI NIH HHS; R01 EY019265-01/EY/NEI NIH HHS

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