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|
|Categories||categorize this paper)|
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Citations of this work BETA
Untangling Invariant Object Recognition.James J. DiCarlo & David D. Cox - 2007 - Trends in Cognitive Sciences 11 (8):333-341.
General Cognitive Principles for Learning Structure in Time and Space.Michael H. Goldstein, Heidi R. Waterfall, Arnon Lotem, Joseph Y. Halpern, Jennifer A. Schwade, Luca Onnis & Shimon Edelman - 2010 - Trends in Cognitive Sciences 14 (6):249-258.
How Position Dependent is Visual Object Recognition?Dwight J. Kravitz, Latrice D. Vinson & Chris I. Baker - 2008 - Trends in Cognitive Sciences 12 (3):114-122.
A Computational Account of the Development of the Generalization of Shape Information.Leonidas A. A. Doumas & John E. Hummel - 2010 - Cognitive Science 34 (4):698-712.
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