Understanding What We See: How We Derive Meaning From Vision.
Trends Cogn Sci
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Clarke, A., & Tyler, L. (2015). Understanding What We See: How We Derive Meaning From Vision.. Trends Cogn Sci, 19 677-687. https://doi.org/10.1016/j.tics.2015.08.008
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.
concepts, semantics, perirhinal cortex, fusiform gyrus, ventral visual pathway, category
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.
European Research Council (249640)
External DOI: https://doi.org/10.1016/j.tics.2015.08.008
This record's URL: https://www.repository.cam.ac.uk/handle/1810/250309
Attribution 2.0 UK: England & Wales
Licence URL: http://creativecommons.org/licenses/by/2.0/uk/