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Marieke Mur; High-level vision: from category selectivity to representational geometry. Journal of Vision 2021;21(9):79. doi: https://doi.org/10.1167/jov.21.9.79.
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Over the last two decades, functional magnetic resonance imaging (fMRI) has provided important insights into the organization and function of the human visual system. In this talk, I will reflect on what fMRI has taught us about high-level visual processes, with an emphasis on object recognition. The discovery of object-selective and category-selective regions in high-level visual cortex suggested that the visual system contains functional modules specialized for processing behaviourally relevant object categories. Subsequent studies, however, showed that distributed patterns of activity across high-level visual cortex also contain category information. These findings challenged the idea of category-selective modules, suggesting that these regions may instead be clusters in a continuous feature map. Consistent with this organizational framework, object representations in high-level visual cortex are at once categorical and continuous: the representational code emphasizes category divisions of longstanding evolutionary relevance while still distinguishing individual images. This body of work provides important insights on the nature of high-level visual representations, but it leaves open how these representations are dynamically computed from images. In recent years, deep neural networks have begun to provide a computationally explicit account of how the ventral visual stream may transform images into meaningful representations. I will close off with a discussion on how neuroimaging data can benefit the development of the next generation of computational models of human vision and how understanding the temporal dynamics of object recognition will play an important role in this endeavor.
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