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Understanding What We See: How We Derive Meaning From Vision.


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Authors

Tyler, Lorraine K 

Abstract

Recognising objects goes beyond vision, and requires models that incorporate different aspects of meaning. Most models focus on superordinate categories (e.g., animals, tools) which do not capture the richness of conceptual knowledge. We argue that object recognition must be seen as a dynamic process of transformation from low-level visual input through categorical organisation to specific conceptual representations. Cognitive models based on large normative datasets are well-suited to capture statistical regularities within and between concepts, providing both category structure and basic-level individuation. We highlight recent research showing how such models capture important properties of the ventral visual pathway. This research demonstrates that significant advances in understanding conceptual representations can be made by shifting the focus from studying superordinate categories to basic-level concepts.

Description

Keywords

Concepts, category, fusiform gyrus, perirhinal cortex, semantics, ventral visual pathway, Brain, Comprehension, Humans, Pattern Recognition, Visual, Semantics, Vision, Ocular, Visual Pathways

Journal Title

Trends Cogn Sci

Conference Name

Journal ISSN

1364-6613
1879-307X

Volume Title

19

Publisher

Elsevier BV
Sponsorship
European Research Council (249640)
We thank William Marslen-Wilson for his helpful comments on this manuscript. The research leading to these results has received funding to LKT from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013)/ ERC Grant agreement n° 249640.