Uncovering the neural representation of multiple dimensions of object categorization in human visual cortex
- We rely on vision more than on any other sensory modality to interact with and make sense of the world. Our behavior and culture, as well as the data we generate all rely strongly on visual information to index and capture salient relationships in the world. Within this realm, categorization is a fundamental building block of our visual experience. It is due to this marvelous generalization process that we take the problem of perceiving and understanding trillions of entities in our world (objects and scenes) and reduce it to a more manageable magnitude by binning virtually everything we see into a few tens of thousands of categories. Thus, it becomes a fundamental problem in understanding human vision to elucidate the mechanisms by which our visual cortices extract such complex information from a noisy sea of colored dots encoded by our retinas when we look out into the world. But what represents a 'good' category and why do these distinctions emerge the way they do? Cognitively, useful distinctions between groups of items simultaneously maximize within-category similarity and between-category dissimilarity. The underlying hypothesis behind the work we put forward in this dissertation is that this key idea of similarity maximization also extends to the instantiation of neural patterns of representation in visual cortex. To this end, we use computational approaches in the context of several functional neuroimaging (fMRI) experiments to explore how behaviorally pervasive dimensions of object categorization, such as hierarchical organization and typicality, are represented in the brain and how they help us build a coherent picture of the world. Finally, we propose and test a model of neural object category processing based on the hypothesis that the cognitive utility of category structure partly drives information processing in visual cortex.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Iordan, Marius Cătălin
|Stanford University, Department of Computer Science.
|Li, Fei Fei, 1976-
|Li, Fei Fei, 1976-
|Statement of responsibility
|Marius Cătălin Iordan.
|Submitted to the Department of Computer Science.
|Thesis (Ph.D.)--Stanford University, 2016.
- © 2016 by Marius Catalin Iordan
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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