Abstract
Brain imaging studies in humans have reported each object category was associated with different neural response pattern reflecting visual, structure or semantic attributes of visual appearance, and the representation of an object is distributed across a broader expanse of cortex rather than a specific region. These findings suggest the feature-map model of object representation. The present object categorization study provided another evidence for feature-map representation of objects. Linear Support Vector Machine (SVM) was used to analyze the functional magnetic resonance imaging (fMRI) data when subjects viewed four representative categories of objects (house, face, car and cat) to investigate the representation of different categories of objects in human brain. We designed 6 linear SVM classifiers to discriminate one category from the other one (1 vs. 1), 12 linear SVM classifiers to discriminate one category from other two categories (1 vs. 2), 3 linear SVM classifiers to discriminate two categories of objects from the other two categories (2 vs. 2). Results showed that objects with visually similar features have lower classification accuracy under all conditions, which may provide new evidences for the feature-map representation of different categories of objects in human brain.
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Acknowledgments
This research was financially supported by Young Scientist Fund of National Natural Science Foundation of China (NSFC) (31300924), NSFC General program (61375116), the Fund of University of JiNan (XKY1508, XKY1408).
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Song, S., Zhang, J., Tong, Y. (2017). Objects Categorization on fMRI Data: Evidences for Feature-Map Representation of Objects in Human Brain. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_10
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DOI: https://doi.org/10.1007/978-3-319-70772-3_10
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