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5 - Semantics without categorization

Published online by Cambridge University Press:  05 June 2012

By
Timothy T. Rogers  and
James L. McClelland
Edited by
Emmanuel M. Pothos  and
Andy J. Wills
Emmanuel M. Pothos
Affiliation:
Swansea University
Andy J. Wills
Affiliation:
University of Exeter
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Summary

Human beings have a remarkable ability to attribute meaning to the objects and events around them. Without much conscious effort, we are able to recognize the items in our environment as familiar ‘kinds’ of things, and to attribute to them properties that have not been observed directly. We know, for instance, that the banana on the kitchen counter has a skin that easily peels off, and that beneath the peel we will find a soft yellow-white interior. We know that the banana is meant to be eaten, and can anticipate what it will taste like. Such inferences spring readily to mind whether we observe the banana itself or, as with this paragraph, simply read or hear statements referring to bananas. The cognitive faculty that supports these abilities is sometimes referred to as ‘semantic memory’, and a key goal of much research in cognitive psychology is to understand the processes that support this aspect of human cognition.

One long-standing hypothesis places categorization at the heart of human semantic abilities. The motivation for this view is that categorization can provide an efficient mechanism for storing and generalizing knowledge about the world. As Rosch (1978) put it, ‘…what one wishes to gain from one's categories is a great deal of information about the environment while conserving finite resources as much as possible.’

Type
Chapter
Information
Formal Approaches in Categorization , pp. 88 - 119
Publisher: Cambridge University Press
Print publication year: 2011

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References

Anderson, J.R. (1991). The adaptive nature of human categorization. Psychological Review, 98 (3), 409–426.CrossRefGoogle Scholar
Ashby, F.G., & Alfonso-Reese, L.A. (1995). Categorization as probability density estimation. Journal of Mathematical Psychology, 39 (2), 216–233.CrossRefGoogle Scholar
Barsalou, L. (1993). Flexibility, structure, and linguistic vagary in concepts: manifestations of a compositional system of perceptual symbols. In Collins, A.F., Gathercole, S.E., Conway, M.A., & Morris, P.E. (eds.), Theories of Memory. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Brown, R. (1958). How shall a thing be called? Psychological Review, 65, 14–21.CrossRefGoogle Scholar
Carey, S. (1985). Conceptual Change in Childhood. Cambridge, MA: MIT Press.Google Scholar
Collins, A.M., & Loftus, E.F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82, 407–428.CrossRefGoogle Scholar
Collins, A.M., & Quillian, M.R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8, 240–247.CrossRefGoogle Scholar
Gelman, R., & Williams, E.M. (1998). Enabling constraints for cognitive development and learning: a domain-specific epigenetic theory. In Damon, W. (ed.), Handbook of Child Psychology, Vol. II: Cognition, Perception and Development (pp. 575–630). New York: John Wiley and Sons.Google Scholar
Gelman, S., & Coley, J.D. (1990). The importance of knowing a dodo is a bird: categories and inferences in 2-year-old children. Developmental Psychology, 26, 796–804.CrossRefGoogle Scholar
Gelman, S.A., & Markman, E.M. (1986). Categories and induction in young children. Cognition, 23, 183–209.CrossRefGoogle ScholarPubMed
Gelman, S.A., & Wellman, H.M. (1991). Insides and essences: early understandings of the nonobvious. Cognition, 38, 213–244.CrossRefGoogle Scholar
Griffiths, T.L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J.B. (2010). Probabilistic models of cognition: exploring representations and inductive biases. Trends in Cognitive Sciences, 14 (8), 357–364.CrossRefGoogle ScholarPubMed
Griffiths, T.L., Sanborn, A.N., Canini, D.J., & Navarro, D.J. (2008). Categorization as nonparametric Bayesian density estimation. In Chater, N. & Oaksford, M. (eds.), The Probabilistic Mind: Prospects for a Bayesian Cognitive Science. Oxford: Oxford University Press.Google Scholar
Hinton, G.E. (1981). Implementing semantic networks in parallel hardware. In Hinton, G.E. & Anderson, J.A. (eds.), Parallel Models of Associative Memory (pp. 161–187). Hillsdale, NJ: Erlbaum.Google Scholar
Hinton, G. E. (1986). Learning distributed representations of concepts. In Proceedings of the Cognitive Science Society (pp. 1–12). Hillsdale, NJ: LEA.Google Scholar
Hinton, G.E., & McClelland, J.L. (1988). Learning representations by recirculation. In Anderson, D.Z. (ed.), Neural Information Processing Systems (pp. 358–366). New York: American Institute of Physics.Google Scholar
Jolicoeur, P., Gluck, M., & Kosslyn, S.M. (1984). Pictures and names: making the connection. Cognitive Psychology, 19, 31–53.Google Scholar
Jones, S.S., Smith, L.B., & Landau, B. (1991). Object properties and knowledge in early lexical learning. Child Development, 62 (3), 499–516.CrossRefGoogle ScholarPubMed
Keil, F. (1979). Semantic and Conceptual Development: an Ontological Perspective. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Kemp, C., & Tenenbaum, J.B. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences, 105 (31), 10687–10692.CrossRefGoogle ScholarPubMed
Kruschke, J.K. (1992). ALCOVE: an exemplar-based connectionist model of category learning. Psychological Review, 99 (1), 22–44.CrossRefGoogle ScholarPubMed
Love, B.C., Medin, D.L., & Gureckis, T.M. (2004). SUSTAIN: a network model of category learning. Psychological Review, 111 (2), 3009–3332.CrossRefGoogle ScholarPubMed
Macario, J.F. (1991). Young children's use of color in classification: foods and canonically colored objects. Cognitive Development, 6, 17–46.CrossRefGoogle Scholar
Mandler, J.M. (2000). Perceptual and conceptual processes in infancy. Journal of Cognition and Development, 1, 3–36.CrossRefGoogle Scholar
Mandler, J.M., & Bauer, P.J. (1988). The cradle of categorization: is the basic level basic?Cognitive Development, 3, 247–264.CrossRefGoogle Scholar
Mandler, J.M., & McDonough, L. (1996). Drinking and driving don't mix: inductive generalization in infancy. Cognition, 59, 307–355.CrossRefGoogle ScholarPubMed
Massey, C.M., & Gelman, R. (1988). Preschooler's ability to decide whether a photographed unfamiliar object can move by itself. Developmental Psychology, 24 (3), 307–317.CrossRefGoogle Scholar
McClelland, J.L., Botvinick, M.B., Noelle, D., Plaut, D.C., Rogers, T.T., Seidenberg, M., & Smith, L. (2010). Letting structure emerge: connectionist and dynamical systems approaches to cognition. Trends in Cognitive Sciences, 14 (8), 348–356.CrossRefGoogle Scholar
McClelland, J.L., McNaughton, B.L., & O'Reilly, R.C. (1995). Why there are complementary learning-systems in the hippocampus and neocortex – insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102 (3), 419–457.CrossRefGoogle ScholarPubMed
McClelland, J.L., & Rumelhart, D.E. (1986). A distributed model of human learning and memory. In McClelland, J.L., Rumelhart, D.E. & Group, the PDP Research (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 2, pp. 170–215). Cambridge, MA: MIT Press.Google Scholar
McClelland, J.L., & Rumelhart, D.E. (1988). Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises. Cambridge, MA: MIT Press.Google Scholar
Medin, D.L., & Schaffer, M.M. (1978). Context theory of classification learning. Psychological Review, 85, 207–238.CrossRefGoogle Scholar
Mervis, C.B. (1987). Child basic object categories and early lexical development. In Neisser, U. (ed.), Concepts and Conceptual Development: Ecological and Intellectual Factors in Categorization. Cambridge: Cambridge University Press.Google Scholar
Mervis, C.A., & Crisafi, M.A. (1982). Order of acquisition of subordinate-, basic-, and superordinate-level categories. Child Development, 53 (1), 258–266.CrossRefGoogle Scholar
Mervis, C.B., & Rosch, E. (1981). Categorization of natural objects. Annual Review of Psychology, 32, 89–115.CrossRefGoogle Scholar
Movellan, J.R., & McClelland, J.L. (1993). Learning continuous probability distributions with symmetric diffusion networks. Cognitive Science, 17, 463–496.CrossRefGoogle Scholar
Murphy, G.L., & Brownell, H.H. (1985). Category differentiation in object recognition: typicality constraints on the basic category advantage. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11 (1), 70–84.Google ScholarPubMed
Murphy, G.L., & Lassaline, M.E. (1997). Hierarchical structure in concepts and the basic level of categorization. In Lamberts, K. & Shanks, D. (eds.), Knowledge, Concepts and Categories (pp. 93–131). Hove: Psychology Press.Google Scholar
Murphy, G.L., & Medin, D.L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289–316.CrossRefGoogle ScholarPubMed
Murphy, G.L., & Smith, E.E. (1982). Basic level superiority in picture categorization. Journal of Verbal Learning and Verbal Behavior, 21, 1–20.CrossRefGoogle Scholar
Nosofsky, R. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 104–110.Google ScholarPubMed
Nosofsky, R.M. (1986). Attention, similarity and the identification-categorization relationship. Journal of Experimental Psychology: Learning, Memory, and Cognition, 115 (1), 39–57.CrossRefGoogle ScholarPubMed
O'Reilly, R.C. (1996). Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm. Neural Computation, 8, 895–938.CrossRefGoogle Scholar
Palmeri, T.J. (1999). Learning categories at different hierarchical levels: a comparison of category learning models. Psychonomic Bulletin & Review, 6, 495–503.CrossRefGoogle ScholarPubMed
Patterson, K., & Hodges, J. (2000). Semantic dementia: one window on the structure and organisation of semantic memory. In Cermak, J. (ed.), Handbook of Neuropsychology, Vol. 2: Memory and its Disorders (pp. 313–333). Amsterdam: Elsevier Science.Google Scholar
Patterson, K., Nestor, P.J., & Rogers, T. (2007). Where do you know what you know? The representation of semantic knowledge in the human brain. Nature Reviews Neuroscience, 8, 976–987.CrossRefGoogle ScholarPubMed
Pauen, S. (2002a). Evidence for knowledge-based category discrimination in infancy. Child Development, 73 (4), 1016–1033.CrossRefGoogle ScholarPubMed
Pauen, S. (2002b). The global-to-basic shift in infants' categorical thinking: first evidence from a longitudinal study. International Journal of Behavioural Development, 26 (6), 492–499.CrossRefGoogle Scholar
Pothos, E.M., & Chater, N. (2002). A simplicity principle in unsupervised human categorization. Cognitive Science, 26, 303–343.CrossRefGoogle Scholar
Rogers, T.T., Lambon Ralph, M.A., Garrard, P., Bozeat, S., McClelland, J.L., Hodges, J.R., et al. (2004). The structure and deterioration of semantic memory: a computational and neuropsychological investigation. Psychological Review, 111 (1), 205–235.CrossRefGoogle Scholar
Rogers, T.T., & McClelland, J.L. (2004). Semantic Cognition: A Parallel Distributed Processing Approach. Cambridge, MA: MIT Press.Google Scholar
Rogers, T.T., & McClelland, J.L. (2008). A simple model from a powerful framework that spans levels of analysis. Behavioral and Brain Sciences, 31, 729–749.CrossRefGoogle Scholar
Rogers, T.T., & Patterson, K. (2007). Object categorization: reversals and explanations of the basic-level advantage. Journal of Experimental Psychology: General, 136 (3), 451–469.CrossRefGoogle ScholarPubMed
Rosch, E. (1978). Principles of categorization. In Rosch, E. & Lloyd, B. (eds.), Cognition and Categorization. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Rosch, E., Mervis, C.B., Gray, W., Johnson, D., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382–439.CrossRefGoogle Scholar
Rumelhart, D.E. (1990). Brain style computation: learning and generalization. In Zornetzer, S.F., Davis, J.C., & Lau, C. (eds.), An Introduction to Neural and Electronic Networks (pp. 405–420). San Diego, CA: Academic Press.Google Scholar
Rumelhart, D.E., Durbin, R., Golden, R., & Chauvin, Y. (1995). Backpropagation: the basic theory. In Chauvin, Y. & Rumelhart, D.E. (eds.), Back-Propagation: Theory, Architectures, and Applications (pp. 1–34). Hillsdale, NJ: Erlbaum.Google Scholar
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986a). Learning representations by back-propagating errors. Nature, 323 (9), 533–536.CrossRefGoogle Scholar
Rumelhart, D.E., McClelland, J.L., & ,the PDP Research Group (1986b). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. I: Foundations & Vol. II: Psychological and Biological Models. Cambridge, MA: MIT Press.Google Scholar
Rumelhart, D.E., & Todd, P.M. (1993). Learning and connectionist representations. In Meyer, D.E. & Kornblum, S. (eds.), Attention and Performance XIV: Synergies in Experimental Psychology, Artificial Intelligence, and Cognitive Neuroscience (pp. 3–30). Cambridge, MA: MIT Press.Google Scholar
Tanaka, J., & Taylor, M. (1991). Object categories and expertise: is the basic level in the eye of the beholder?Cognitive Psychology, 23, 457–482.CrossRefGoogle Scholar
Tenenbaum, J.B., & Griffiths, T.L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24, 629–640.CrossRefGoogle ScholarPubMed
Tenenbaum, J.B., Griffiths, T.L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10 (7), 309–318.CrossRefGoogle ScholarPubMed
Verheyen, S., Ameel, E., Rogers, T.T., & Storms, G. (2008). Learning a hierarchical organization of categories. Paper presented at the Proceedings of the Cognitive Science Society, Amsterdam, the Netherlands.Google Scholar

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