August 2014
Volume 14, Issue 10
Vision Sciences Society Annual Meeting Abstract  |   August 2014
Low-level image properties of visual objects explain category-selective patterns of neural response across the ventral visual pathway
Author Affiliations
  • Tim Andrews
    University of York
Journal of Vision August 2014, Vol.14, 1459. doi:10.1167/14.10.1459
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      Tim Andrews; Low-level image properties of visual objects explain category-selective patterns of neural response across the ventral visual pathway. Journal of Vision 2014;14(10):1459. doi: 10.1167/14.10.1459.

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      © ARVO (1962-2015); The Authors (2016-present)

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Neuroimaging research over the past 20 years has begun to reveal a picture of how the human visual system is organized. A key organizing principle that has arisen from these studies is the distinction between low-level and high-level visual regions. Low-level regions are organized into visual field maps that are tightly linked to the image properties of the stimulus. In contrast, high-level visual areas are thought to be arranged in modules that are selective for particular object categories. It is unknown, however, whether this selectivity is truly based on object category, or whether it reflects tuning for low-level features that are common to images from a particular category. To address this issue, we compared the pattern of neural response elicited by each object category with the corresponding low-level properties of images from each object category. We found a strong positive correlation between the neural patterns and the underlying low-level image properties. Importantly, the correlation was still evident when the within-category correlations were removed from the analysis. Next, we asked whether low-level image properties could also explain variation in the pattern of response to exemplars from individual object categories (faces or scenes). Again, a positive correlation was evident between the similarity in the pattern of neural response and the low-level image properties of exemplars from individual object categories. These results suggest that the pattern of response in high-level visual areas may be better explained by the image statistics of visual stimuli than by their associated categorical or semantic properties.

Meeting abstract presented at VSS 2014


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