David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
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)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
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.
Similar books and articles
Tony Vladusich (2001). Perceptual Filling-in and the Resonant Binding of Distributed Cortical Representations. Behavioral and Brain Sciences 24 (6):1136-1137.
S. Phillips (1998). Are Feedforward and Recurrent Networks Systematic? Analysis and Implications for a Connectionist Cognitive Architecture. Philosophical Explorations.
David J. Chalmers (1993). Connectionism and Compositionality: Why Fodor and Pylyshyn Were Wrong. Philosophical Psychology 6 (3):305-319.
Shimon Edelman & Nathan Intrator (2002). Visual Processing of Object Structure. In M. Arbib (ed.), The Handbook of Brain Theory and Neural Networks. Mit Press.
W. F. G. Haselager & J. F. H. Van Rappard (1998). Connectionism, Systematicity, and the Frame Problem. Minds and Machines 8 (2):161-179.
Jay L. Garfield (1997). Mentalese Not Spoken Here: Computation, Cognition, and Causation. Philosophical Psychology 10 (4):413-35.
Jennifer D. Ryan & Neal J. Cohen (2001). The Existence of Internal Visual Memory Representations. Behavioral and Brain Sciences 24 (5):1002-1003.
Chris Eliasmith, Structure Without Symbols: Providing a Distributed Account of High-Level Cognition.
David J. Chalmers (1990). Syntactic Transformations on Distributed Representations. Connection Science 2:53-62.
Ronald Giere (2002). 15 Scientific Cognition as Distributed Cognition. In Peter Carruthers, Stephen P. Stich & Michael Siegal (eds.), The Cognitive Basis of Science. Cambridge University Press. 285.
Bruce Bridgeman (1999). Implicit and Explicit Representations of Visual Space. Behavioral and Brain Sciences 22 (5):759-760.
Manish Singh & Barbara Landau (1998). Parts of Visual Shape as Primitives for Categorization. Behavioral and Brain Sciences 21 (1):36-37.
Jerry A. Fodor & Zenon W. Pylyshyn (1988). Connectionism and Cognitive Architecture. Cognition 28 (1-2):3-71.
Added to index2010-12-22
Total downloads4 ( #255,916 of 1,101,679 )
Recent downloads (6 months)3 ( #116,934 of 1,101,679 )
How can I increase my downloads?