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H.Steven Scholte, Sennay Ghebreab, Arnold Smeulders, Victor Lamme; Lateral Occipital cortex responsive to correlation structure of natural images. Journal of Vision 2010;10(7):1363. doi: 10.1167/10.7.1363.
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© ARVO (1962-2015); The Authors (2016-present)
The distribution of features around any location in natural images adheres to the Weibull distribution (Geusebroek & Smeulders, 2005), which is a family of distribution deforming from normal to a power-law distribution with 2 free parameters, beta and gamma. The gamma parameter from the Weibull distribution indicates whether the data has a more power-low or more normal distribution. We recently showed that the brain is capable of estimating the beta and gamma value of a scene by summarizing the X and Y cell populations of the LGN (Scholte et al., 2009) and that this explains 85% of the variance in the early ERP. Here we investigate to what degree the brain is sensitive to differences in the global correlation (gamma) of a scene by presenting subjects with a wide range of natural images while measuring BOLD-MRI. Covariance analysis of the single-trial BOLD-MRI data with the gamma parameter showed that only the lateral occipital cortex (LO), and no other areas, responds stronger to low gamma values (corresponding to images with a power-law distribution) than high gamma values (corresponding to images with a normal distribution). The analysis of the covariance matrix of the voxel-pattern cross-correlated single-trial data further revealed that responses to images containing clear objects are more similar in their spatial structure than images that do not contain objects. This data is consisted with a wide range of literature over object perception and area LO (Grill-Spector et al., 2001) and extend our understanding of object recognition by showing that the global correlation structure of a scene is (part of) the diagnostics that are used by the brain to detect objects.
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