Deconstructing visual scenes in cortex: gradients of object and spatial layout information.

Citation data:

Cerebral cortex (New York, N.Y. : 1991), ISSN: 1460-2199, Vol: 23, Issue: 4, Page: 947-57

Publication Year:
2013
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Citations 62
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Repository URL:
https://works.bepress.com/assaf_harel/28; https://corescholar.libraries.wright.edu/psychology/243
PMID:
22473894
DOI:
10.1093/cercor/bhs091; 10.1093/cercor/bhs09
PMCID:
PMC3593580
Author(s):
Harel, Assaf; Kravitz, Dwight J.; Baker, Chris I.
Publisher(s):
Oxford University Press (OUP)
Tags:
Neuroscience; fMRI; LOC; PPA; RSC; Scene Recognition; fMRI; LOC; PPA; RSC; Scene Recognition; Medical Sciences; Medicine and Health Sciences; Neurosciences; Psychology; Social and Behavioral Sciences
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article description
Real-world visual scenes are complex cluttered, and heterogeneous stimuli engaging scene- and object-selective cortical regions including parahippocampal place area (PPA), retrosplenial complex (RSC), and lateral occipital complex (LOC). To understand the unique contribution of each region to distributed scene representations, we generated predictions based on a neuroanatomical framework adapted from monkey and tested them using minimal scenes in which we independently manipulated both spatial layout (open, closed, and gradient) and object content (furniture, e.g., bed, dresser). Commensurate with its strong connectivity with posterior parietal cortex, RSC evidenced strong spatial layout information but no object information, and its response was not even modulated by object presence. In contrast, LOC, which lies within the ventral visual pathway, contained strong object information but no background information. Finally, PPA, which is connected with both the dorsal and the ventral visual pathway, showed information about both objects and spatial backgrounds and was sensitive to the presence or absence of either. These results suggest that 1) LOC, PPA, and RSC have distinct representations, emphasizing different aspects of scenes, 2) the specific representations in each region are predictable from their patterns of connectivity, and 3) PPA combines both spatial layout and object information as predicted by connectivity.