title: Understanding What We See: How We Derive Meaning From Vision.
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.
authors.affiliation: Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK.
authors.forename: Alex
authors.initials: A
authors.lastname: Clarke
authors.affiliation: Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK. Electronic address: lktyler@csl.psychol.cam.ac.uk.
authors.forename: Lorraine K
authors.initials: LK
authors.lastname: Tyler
mesh_terms: D001921:Brain / Q000502:physiology*; D032882:Comprehension; D006801:Humans; D010364:Pattern Recognition, Visual / Q000502:physiology*; D012660:Semantics; D014785:Vision, Ocular / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology*
keywords: Concepts; category; fusiform gyrus; perirhinal cortex; semantics; ventral visual pathway