David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Cognitive Science 23 (1):73-110 (2003)
The problem of representing the spatial structure of images, which arises in visual object processing, is commonly described using terminology borrowed from propositional theories of cognition, notably, the concept of compositionality. The classical propositional stance mandates representations composed of symbols, which stand for atomic or composite entities and enter into arbitrarily nested relationships. We argue that the main desiderata of a representational system — productivity and systematicity — can (indeed, for a number of reasons, should) be achieved without recourse to the classical, proposition-like compositionality. We show how this can be done, by describing a systematic and productive model of the representation of visual structure, which relies on static rather than dynamic binding and uses coarsely coded rather than atomic shape primitives.
|Keywords||Compositionality Systematicity Cognition|
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Michael H. Goldstein, Heidi R. Waterfall, Arnon Lotem, Joseph Y. Halpern, Jennifer A. Schwade, Luca Onnis & Shimon Edelman (2010). General Cognitive Principles for Learning Structure in Time and Space. Trends in Cognitive Sciences 14 (6):249-258.
Leonidas A. A. Doumas & John E. Hummel (2010). A Computational Account of the Development of the Generalization of Shape Information. Cognitive Science 34 (4):698-712.
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