Document Detail

Spatial biases and computational constraints on the encoding of complex local image structure.
MedLine Citation:
PMID:  19146252     Owner:  NLM     Status:  MEDLINE    
The decomposition of visual scenes into elements described by orientation and spatial frequency is well documented in the early cortical visual system. How such 2nd-order elements are sewn together to create perceptual objects such as corners and intersections remains relatively unexplored. The current study combines information theory with structured deterministic patterns to gain insight into how complex (higher-order) image features are encoded. To more fully probe these mechanisms, many subjects (N = 24) and stimuli were employed. The detection of complex image structure was studied under conditions of learning and attentive versus preattentive visual scrutiny. Strong correlations (R(2) > 0.8, P < 0.0001) were found between a particular family of spatially biased measures of image information and human sensitivity to a large range of visual structures. The results point to computational and spatial limitations of such encoding. Of the extremely large set of complex spatial interactions that are possible, the small subset perceivable by humans were found to be dominated by those occurring along sets of one or more narrow parallel lines. Within such spatial domains, the number of pieces of visual information (pixel values) that may be simultaneously considered is limited to a maximum of 10 points. Learning and processes involved in attentive scrutiny do little if anything to increase the dimensionality of this system.
Ryan R L Taylor; Ted Maddess; Yoshinori Nagai
Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't     Date:  2008-07-15
Journal Detail:
Title:  Journal of vision     Volume:  8     ISSN:  1534-7362     ISO Abbreviation:  J Vis     Publication Date:  2008  
Date Detail:
Created Date:  2009-01-16     Completed Date:  2009-05-05     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101147197     Medline TA:  J Vis     Country:  United States    
Other Details:
Languages:  eng     Pagination:  19.1-13     Citation Subset:  IM    
ARC Centre of Excellence in Vision Science and Centre for Visual Sciences, Research School of Biological Sciences, Australian National University, Canberra, Australia.
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MeSH Terms
Attention / physiology*
Computer Simulation*
Learning / physiology
Pattern Recognition, Visual / physiology*
Visual Cortex / physiology*

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