Towards structural systematicity in distributed, statically bound visual representations

Cognitive Science

Volumn 27, Issue 1, 2003, Pages 73-109

  Edelman, Shimon ^a   Intrator, Nathan ^b,c  

^a Department of Molecular Biology and Genetics   (United States)

^b BROWN UNIVERSITY   (United States)

^c TEL AVIV UNIVERSITY   (Israel)

Author keywords

Cognition; Compositionality; Systematicity

Indexed keywords

EID: 0037232127     PISSN: 03640213     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0364-0213(02)00114-3     Document Type: Article

References (128)

1. Aho, A. V., Hopcroft, J. E., & Ullman, J. D. (1974). The design and analysis of computer algorithms. Reading, MA: Addison-Wesley.
2. Arbib, M. A. (1979). Feature detectors, visuomotor coordination, and efferent control. In D. Albrecht (Ed.), Recognition of pattern and form: Lecture notes in biomathematics (Vol. 44, pp. 100-110). Berlin: Springer.
3. Bar, M., & Biederman, I. (1998). Subliminal visual priming. Psychological Science, 9(6), 464-469.
4. Barlow, H. B. (1990). Conditions for versatile learning, Helmholtz's unconscious inference, and the task of perception. Vision Research, 30, 1561-1571.
5. Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577-660.
6. Biederman, I. (1987). Recognition by components: A theory of human image understanding. Psychology Review, 94, 115-147.
7. Bienenstock, E. (1996). Composition. In A. Aertsen & V. Braitenberg (Eds.), Brain theory-biological basis and computational theory of vision (pp. 269-300). Amsterdam: Elsevier.
8. Bienenstock, E., & Geman, S. (1995). Compositionality in neural systems. In M. A. Arbib (Ed.), The handbook of brain theory and neural networks (pp. 223-226). Cambridge, MA: MIT Press.
9. Bienenstock, E., Geman, S., & Potter, D. (1997). Compositionality, MDL priors, and object recognition. In M. C. Mozer,M. I. Jordan, & T. Petsche (Eds.), Neural information processing systems (Vol. 9). Cambridge, MA: MIT Press.
10. Burl, M. C.,Weber, M., & Perona, P. (1998). A probabilistic approach to object recognition using local photometry and global geometry. In H. Burkhardt & B. Neumann (Eds.), Proceedings of the 4th European conference on computer vision (Vol. 1406-1407, pp. 628-641), LNCS-Series. Springer-Verlag.
11. Cave, K. R., Pinker, S., Giorgi, L., Thomas, C. E., Heller, L. M.,Wolfe, J. M., & Lin, H. (1994). The representation of location in visual images. Cognitive Psychology, 26, 1-32.
12. Chen, S. S., Donoho, D. L., & Saunders, M. A. (1999). Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20, 33-61.
13. Chipere, N. (1997). Individual differences in syntactic skill. Working Papers in English and Applied Linguistics, 4, 1-32.
14. Church, A. (1941). The calculi of lambda conversion. Princeton, NJ: Princeton University Press.
15. Clark, A. (2000). A theory of sentience. Oxford: Oxford University Press.
16. Connor, C. E., Preddie, D. C., Gallant, J. L., & Van Essen, D. C. (1997). Spatial attention effects in macaque area V4. Journal of Neuroscience, 17, 3201-3214.
17. Crawford, L. E., Regier,T.,& Huttenlocher, J. (2000). Linguistic and non-linguistic spatial categorization. Cognition, 75, 209-235.
18. Dickinson, S., Bergevin, R., Biederman, I., Eklundh, J., Munck-Fairwood, R., Jain, A.,& Pentland, A. (1997). Panel report: The potential of geons for generic 3-D object recognition. Image and Vision Computing, 15, 277-292.
19. Dill, M., & Edelman, S. (2001). Imperfect invariance to object translation in the discrimination of complex shapes. Perception, 30, 707-724.
20. Eco, U. (1976). A theory of semiotics. Bloomington, IN: Indiana University Press.
21. Edelman, S. (1994). Biological constraints and the representation of structure in vision and language. Psycology, 5(57). Available: http://www.cogsci.soton.ac.uk/cgi/psyc/newpsy?5.57.
22. Edelman, S. (1997). Computational theories of object recognition. Trends in Cognitive Science, 1, 296-304.
23. Edelman, S. (1998). Representation is representation of similarity. Behavioral and Brain Sciences, 21, 449-498.
24. Edelman, S. (1999). Representation and recognition in vision. Cambridge, MA: MIT Press.
25. Edelman, S. (2002). Constraints on the nature of the neural representation of the visual world. Trends in Cognitive Sciences, 6, 125-131.
26. Edelman, S., & Duvdevani-Bar, S. (1997a). A model of visual recognition and categorization. Philosophical Transactions of the Royal Society of London Series (B), 352(1358), 1191-1202.
27. Edelman, S., & Duvdevani-Bar, S. (1997b). Similarity-based viewspace interpolation and the categorization of 3D objects. In Proceedings of the similarity and categorization workshop (pp. 75-81), Department of AI, University of Edinburgh.
28. Edelman, S., Hiles, B. P., Yang, H., & Intrator, N. (2002). Probabilistic principles in unsupervised learning of visual structure: Human data and a model. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems (Vol. 14, 19-26). Cambridge, MA: MIT Press.
29. Edelman, S., & Intrator, N. (1997). Learning as extraction of low-dimensional representations. In D. Medin, R. Goldstone, & P. Schyns (Eds.), Mechanisms of perceptual learning (pp. 353-380). New York: Academic Press.
30. Edelman, S.,& Intrator,N. (2000). (Coarse coding of shape fragments)+ (retinotopy) ≈ representation of structure. Spatial Vision, 13, 255-264.
31. Edelman, S., & Intrator, N. (2001). A productive, systematic framework for the representation of visual structure. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems (Vol. 13, pp. 10-16). Cambridge, MA: MIT Press.
32. Edelman, S., Intrator, N., & Jacobson, J. S. (in press). Unsupervised learning of visual structure. In H. H. Bülthoff, C. Wallraven, S.-W. Lee, & T. Poggio (Eds.), Proceedings of the 2nd international workshop on biologically motivated computer vision (Vol. 2525). Berlin: Springer. Lecture Notes in Computer Science.
33. Elliffe, M. C. M., Rolls, E. T., & Stringer, S. M. (2002). Invariant recognition of feature combinations in the visual system. Biological Cybernetics, 86, 59-71.
34. Field, D. J. (1993). Scale-invariance and self-similar wavelet transforms: An analysis of natural scenes and mammalian visual systems. In M. Farge, J. Hunt, & T. Vassilicos (Eds.), Wavelets, fractals and fourier transforms: New developments and new applications (pp. 151-193). Oxford University Press.
35. Fodor, J. A. (1987). Psychosemantics. Cambridge, MA: MIT Press.
36. Fodor, J. A. (1998). Concepts: Where cognitive science went wrong. Oxford: Clarendon Press.
37. Fodor, J., & McLaughlin, B. (1990). Connectionism and the problem of systematicity: Why Smolensky's solution doesn't work. Cognition, 35, 183-204.
38. Fodor, J., & Pylyshyn, Z. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3-71.
39. Frege, G. (1891/1993). On sense and reference. In P. Geach & M. Black (Eds.), Translations from the philosophical writings of G. Frege (pp. 56-78). Oxford: Blackwell, Translated as "On Sense and Meaning".
40. Fujita, I., Tanaka, K., Ito, M., & Cheng, K. (1992). Columns for visual features of objects in monkey inferotemporal cortex. Nature, 360, 343-346.
41. Fukushima, K. (1988). Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural Networks, 1, 119-130.
42. Gallant, J. L., Shoup, R. E., & Mazer, J. A. (2000). A human extrastriate area functionally homologous to macaque V4. Neuron, 27, 227-235.
43. Geman, S. (1996). Minimum description length priors for object recognition. Challenging the frontiers of knowledge using statistical science (Proc. JSM'96).
44. Gilmore, G., Hersh, H., Caramazza, A., & Griffin, J. (1979). Multidimensional letter similarity derived from recognition errors. Perception and Psychophysics, 25, 425-431.
45. Hadley, R. F. (1994). Systematicity revisited. Mind and Language, 9, 431-444.
46. Hadley, R. E. (1997). Cognition, systematicity, and nomic necessity. Mind and Language, 12, 137-153.
47. Harnad, S. (1990). The symbol grounding problem. Physica D, 42, 335-346.
48. Hayward, W. G., & Tarr, M. J. (1995). Spatial language and spatial representation. Cognition, 55, 39-84.
49. Heeger, D. J., Simoncelli, E. P., & Movshon, J. A. (1996). Computational models of cortical visual processing. Proceedings of the National Academy of Science, 93, 623-627.
50. Heisele, B., Serre, T., Pontil, M., Vetter, T., & Poggio, T. (2002). Categorization by learning and combining object parts. In S. Becker (Ed.), Advances in neural information processing systems (Vol. 14, 1239-1245). Cambridge, MA: MIT Press.
51. Hinton, G. E. (1984). Distributed representations. Technical Report CMU-CS 84-157. Pittsburgh, PA: Department of Computer Science, Carnegie-Mellon University.
52. Hummel, J. E. (2000). Where view-based theories of human object recognition break down: The role of structure in human shape perception. In E. Dietrich & A. Markman (Eds.), Cognitive dynamics: Conceptual change in humans and machines (Chapter 7). Hillsdale, NJ: Lawrence Erlbaum.
53. Hummel, J. E. (2001). Complementary solutions to the binding problem in vision: Implications for shape perception and object recognition. Visual Cognition, 8, 489-517.
54. Hummel, J. E., & Biederman, I. (1992). Dynamic binding in a neural network for shape recognition. Psychological Review, 99, 480-517.
55. Huttenlocher, J., Hedges, L.,& Duncan, S. (1991). Categories and particulars: Prototype effects in estimating spatial location. Psychological Review, 98, 352-376.
56. Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991). Adaptive mixtures of local experts. Neural Computation, 3, 79-87.
57. Johnston, J. C., & Pashler, H. (1990). Close binding of identity and location in visual feature perception. Journal of Experimental Psychology: Human Perception and Performance, 16, 843-856.
58. Jolicoeur, P., Gluck, M.,& Kosslyn, S. M. (1984). Pictures and names: Making the connection.Cognitive Psychology, 16, 243-275.
59. Kahneman, D., Treisman, A., & Gibbs, B. J. (1992). The reviewing of object files: Object-specific integration of information. Cognitive Psychology, 24, 175-219.
60. Katz, J. J., & Fodor, J. (1963). The structure of a semantic theory. Language, 39, 17-210.
61. Kaye, L. J. (1995). The languages of thought. Philosophy of Science, 62, 92-110.
62. Kobatake, E., & Tanaka, K. (1994). Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. Journal of Neurophysiology, 71, 856-867.
63. Lakoff, G. (1971). On generative semantics. In D. D. Steinberg & L. A. Jakobowitz (Eds.), Semantics (pp. 232-296). Cambridge University Press.
64. Langacker, R.W. (1990). Concept, image, and symbol: The cognitive basis of grammar. Berlin: Mouton de Gruyter.
65. Lappin, S., & Zadrozny, W. (1999). Compositionality, synonymy, and the systematic representation of meaning. Unpublished manuscript, King's College , London.
66. Lewicki, M.S., & Sejnowski, T. J. (1998). Learning nonlinear overcomplete representations for efficient coding. In M. I. Jordan, M. J. Kearns, & S.A. Solta (Eds.), Advances in neural information processing systems (Vol. 10). Cambridge, MA: MIT Press.
67. Logan. G. D. (1994). Spatial attention and the apprehension of spatial relations. Journal of Experimental Psychology: Human Perception and Performance, 20 1015-1036.
68. Logothetis, N. K., & Sheinberg, D. L. (1996). A structural account of global and local processing. Cognetive Psychology, 38, 291-316.
69. Love, B. C., Rouderm J. N., & Lovitt, J. B. (1993). Comparison of intrinsic connections in different areas of macaque cerebral cortex. Cerebral Cortex, 3, 148-162.
70. Mishkin, M., Ungerleider, L. G., & Macki, K. A. (1983). Object vision and spatial vision: Two cortical pathways. Trends in Neurosciences, 4, 414-417.
71. Navon, D. (1977). Forest before trees: The precedence of global features in visual perception. Cognitive Psychology, 9, 353-383.
72. Nazir, T. & O'Regan, J. K. (1990). Some results on translation invariance in the human visual system. Spatial Vision, 5, 81-100.
73. Neronne, J. (1995). Computational semantics-linguistics and processing. In S. Lappin (Ed.), Handbook of contemporary semantic theory (pp. 461-484). London: Blackwell.
74. Niklasson, L.,& Boden, M. (1997). Representing structure and structured representations in connectionist networks. In A. Browne (Ed.), Neural network perspectives on cognition and adaptive robotics (pp. 20-50). Bristol, UK: Institute of Physics Press.
75. Niklasson, L., & van Gelder, T. (1994). Can connectionist models exhibit non-classical structure sensitivity? In A. Ram & K. Eiselt (Ed.), Proceedings of the sixteenth annual conference of the cognitive science society (pp. 664-669). Hillsdale, NJ: Lawrence Erlbaum.
76. O'Brien, G., & Opie, J. (1999). A connectionist theory of phenomenal experience. Behavioral and Brain Sciences, 22, 127-148.
77. Op de Beeck, H., & Vogels, R. (2000). Spatial sensitivity of macaque inferior temporal neurons. Journal of Comparative Neurology, 426, 505-518.
78. O'Regan, J. K., & Noë, A. (2001). A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences, 24, 883-917.
79. O'Reilly, R. C., & Busby, R. S. (2002). Generalizable relational binding from coarse-coded distributed representations. In S. Becker (Ed.), Advances in neural information processing systems (Vol. 14, 75-82). Cambridge, MA: MIT Press.
80. Poggio, T., & Edelman, S. (1990). A network that learns to recognize three-dimensional objects. Nature, 343, 263-266.
81. Poggio, T., & Hurlbert, A. (1994). Observations on cortical mechanisms for object recognition and learning. In C. Koch & J. Davis (Eds.), Large scale neuronal theories of the brain (pp. 153-182). Cambridge, MA: MIT Press.
82. Pomerleau, D. (1993). Input reconstruction reliability estimation. In C. L. Giles, S. J. Hanson,&J. D. Cowan (Eds.), Advances in neural information processing systems (Vol. 5, pp. 279-286). Los Altos, CA: Morgan Kaufmann.
83. Prinz, J. (1994). Shared beliefs, infralingual thought, and nativism. Noesis. Available: online(#004986), http://noesis.evansville.edu.
84. Rainer, G., Asaad,W.,& Miller, E.K. (1998). Memory fields of neurons in the primate prefrontal cortex. Proceedings of the National Academy of Science, 95, 15008-15013.
85. Rao, S. C., Rainer, G., & Miller, E. K. (1997). Integration of what and where in the primate prefrontal cortex. Science, 276, 821-824.
86. Riesenhuber, M., & Dayan, P. (1997). Neural models for the part-whole hierarchies. In M. Jordan (Ed.), Advances in neural information processing (Vol. 9, pp. 17-23). Cambridge, MA: MIT Press.
87. Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2, 1019-1025.
88. Rock, I. (1973). Orientation and form. Cambridge, MA: MIT Press.
89. Rolls, E. T. (1996). Visual processing in the temporal lobe for invariant object recognition. In V. Torre & T. Conti (Eds.), Neurobiology (pp. 325-353). New York: Plenum Press.
90. Rolls, E. T., & Tovee, M. J. (1995). Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. Journal of Neurophysiology, 73, 713-726.
91. Roskies, A. L. (1999). The binding problem (review introduction). Neuron, 24, 7-9.
92. Saiki, J., & Hummel, J. E. (1996). Attribute conjunctions and the part configuration advantage in object category learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 22, 1002-1019.
93. Sali, E.,& Ullman, S. (1999). Detecting object classes by the detection of overlapping 2-D fragments. In Proceedings of the 10th British machine vision conference (Vol. 1, pp. 203-213).
94. Salinas, E., & Abbott, L. F. (1997). Invariant visual responses from attentional gain fields. Journal of Neurophysiology, 77, 3267-3272.
95. Sanocki, T. (1993). Time course of object identification: Evidence for a global-to-local contingency. Journal of Experimental Psychology: Human Perception and Performance, 19, 878-898.
96. Schlingensiepen, K.-H., Campbell, F.W., Legge, G. E., &Walker, T. D. (1986). The importance of eye movements in the analysis of simple patterns. Vision Research, 26, 1111-1117.
97. Shapiro, K. L., & Loughlin, C. (1993). The locus of inhibition in the priming of static objects: Object token versus location. Journal of Experimental Psychology: Human Perception and Performance, 19, 352-363.
98. Shepard, R. N. (1980). Multidimensional scaling, tree-fitting, and clustering. Science, 210, 390-397.
99. Smith, B. (2001). Fiat objects. Topoi, 20, 131-148.
100. Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic strutures in connectionist systems. Artificial Intelligence, 46, 159-216.
101. Solomon, K. O., & Barsalou, L. W. (2001). Representing properties locally. Cognitive Psychology, 43, 129-169.
102. Stainvas, I., & Intrator, N. (2000). Blurred face recognition via a hybrid network architecture. In Proceedings of the ICPR (Vol. 2, pp. 809-812).
103. Strang, G. (1989). Wavelets and dilation equations: A brief introduction. SIAM Review, 31, 614-627.
104. Talmy, L. (1983). How language structures space. In J. H. L. Pick & L. P. Acredolo (Eds.), Spatial orientation: Theory, research, and application (pp. 225-282). New York: Plenum Press.
105. Tanaka, K. (1996). Inferotemporal cortex and object vision. Annual Review of Neuroscience, 19, 109-139.
106. Tanaka, K., Saito, H., Fukada, Y., & Moriya, M. (1991). Coding visual images of objects in the inferotemporal cortex of the macaque monkey. Journal of Neurophysiology, 66, 170-189.
107. Thompson, P. (1980). Margaret Thatcher: A new illusion. Perception, 9, 483-484.
108. Touretzky, D. S. (1989). Connectionism and compositional semantics. Technical Report CMU-CS 89-147. Pittsburgh, PA: Department of Computer Science, Carnegie-Mellon University.
109. Treisman, A. (1992). Perceiving and re-perceiving objects. American Psychologist, 47, 862-875.
110. Treisman, A. (1996). The binding problem. Current Opinion in Neurobiology, 6, 171-178.
111. Treisman, A. M., & Kanwisher, N. G. (1998). Perceiving visually presented objects: Recognition, awareness, and modularity. Current Opinion in Neurobiology, 8, 218-226.
112. Tsunoda, K., Yamane, Y., Nishizaki, M., & Tanifuji, M. (2001). Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns. Nature Neuroscience, 4, 832-838.
113. Ullman, S. (1989). Aligning pictorial descriptions: An approach to object recognition. Cognition, 32, 193-254.
114. Ullman, S. (1998). Three-dimensional object recognition based on the combination of views. Cognition, 67, 21-44.
115. Ungerleider, L., & Haxby, J. V. (1994). "What"and "where" in the human brain. Current Opinion in Neurobiology, 4, 157-165.
116. van Gelder, T. (1990). Compositionality: A connectionist variation on a theme. Cognitive Science, 14, 355-384.
117. Venturino, M., & Gagnon, D. (1992). Information tradeoffs in complex stimulus structure: Local and global levels in naturalistic scenes. Perception and Psychophysics, 52, 425-436.
118. Vogels, R. (1999). Categorization of complex visual images by rhesus monkeys. Part 2. Single-cell study. European Journal of Neuroscience, 11, 1239-1255.
119. von der Malsburg, C. (1995). Binding in models of perception and brain function. Current Opinion in Neurobiology, 5, 520-526.
120. Wallach, H., & Austin-Adams, P. (1954). Recognition and the localization of visual traces. American Journal of Psychology, 67, 338-340.
121. Wang, Y., Fujita, I., & Murayama, Y. (2000). Neuronal mechanisms of selectivity for object features revealed by blocking inhibition in inferotemporal cortex. Nature Neuroscience, 3, 807-813.
122. Wang, G., Tanifuji, M., & Tanaka, K. (1998). Functional architecture in monkey inferotemporal cortex revealed by in vivo optical imaging. Neuroscience Research, 32, 33-46.
123. Ward, R., Danziger, S., Owen, V., & Rafal, R. (2002). Deficits in spatial coding and feature binding following damage to spatiotopic maps in the human pulvinar. Nature Neuroscience, 5, 99-101.
124. Wittgenstein, L. (1961). Tractatus logico-philosophicus. Routledge, London. Trans. D. F. Pears and B. F. McGuinness.
125. Wolfe, J. M.,& Bennett, S. C. (1997). Preattentive object files: Shapeless bundles of basic features. Vision Research, 37, 25-43.
126. Zadrozny, W. (1994). From compositional to systematic semantics. Linguistics and Philosophy, 17, 329-342.
127. Zadrozny, W. (1999). Minimum description length and compositionality. In H. Bunt & R. Muskens (Eds.), Computing meaning (Vol. 1, pp. 113-128). Dordrecht: Kluwer Academic Publishers.
128. Zemel, R. S., & Hinton, G. E. (1995). Developing population codes by minimizing description length. Neural Computation, 7, 549-564.