Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-12-03T01:15:59.778Z Has data issue: false hasContentIssue false
  • English
  • Français

Précis of Semantic Cognition: A Parallel Distributed Processing Approach

Published online by Cambridge University Press:  11 December 2008

Timothy T. Rogers  and
James L. McClelland
Timothy T. Rogers
Affiliation:
Department of Psychology, University of Wisconsin-Madison, Madison, WI [email protected]://concepts.psych.wisc.edu
James L. McClelland
Affiliation:
Department of Psychology and Center for Mind, Brain, and Computation, Stanford University, Stanford, CA [email protected]://psychology.stanford.edu/~jlm
Get access Rights & Permissions [Opens in a new window]

Abstract

In this précis of our recent book, Semantic Cognition: A Parallel Distributed Processing Approach (Rogers & McClelland 2004), we present a parallel distributed processing theory of the acquisition, representation, and use of human semantic knowledge. The theory proposes that semantic abilities arise from the flow of activation among simple, neuron-like processing units, as governed by the strengths of interconnecting weights; and that acquisition of new semantic information involves the gradual adjustment of weights in the system in response to experience. These simple ideas explain a wide range of empirical phenomena from studies of categorization, lexical acquisition, and disordered semantic cognition. In this précis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the “theory-theory” approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form “good” or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (5) how concepts reorganize between the ages of 4 and 10, and (6) the relationship between causal knowledge and semantic knowledge. The explanations our theory offers for these phenomena are illustrated with reference to a simple feed-forward connectionist model. The relationships between this simple model, the broader theory, and more general issues in cognitive science are discussed.

Keywords

categorizationcausal knowledgeconceptsconnectionismdevelopmentinnatenesslearningsemanticsmemorytheory-theory
Type
Main Articles
Information
Behavioral and Brain Sciences , Volume 31 , Issue 6 , December 2008 , pp. 689 - 714
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahn, W. (1998) Why are different features central for natural kinds and artifacts? The role of causal status in determining feature centrality. Cognition 69:135–78.CrossRefGoogle ScholarPubMed
Ahn, W., Marsh, J. K. & Luhmann, C. C. (2002) Effect of theory-based feature correlations on typicality judgments. Memory and Cognition 30(1):107–18.CrossRefGoogle ScholarPubMed
Anderson, J. R. (1990) The adaptive character of thought. Erlbaum.Google Scholar
Barsalou, L., Simmons, W., Barbey, A. & Wilson, C. D. (2003) Grounding conceptual knowledge in modality-specific systems. Trends in Cognitive Sciences 7(2):84–91.CrossRefGoogle ScholarPubMed
Bomba, P. C. & Siqueland, E. R. (1983) The nature and structure of infant form categories. Journal of Experimental Child Psychology 35:294–328.CrossRefGoogle Scholar
Boyd, R. (1986) Natural kinds, homeostasis, and the limits of essentialism. Unpublished manuscript, Cornell University.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. MIT Press.Google Scholar
Carey, S. & Spelke, E. (1994) Domain-specific knowledge and conceptual change. In: Mapping the mind: Domain specificity in cognition and culture, ed. Hirschfeld, L. A. & Gelman, S., pp. 169–200. Cambridge University Press.CrossRefGoogle Scholar
Chomsky, N. (1980) Rules and representations. Behavioral and Brain Sciences 3:1–61.CrossRefGoogle Scholar
Cleeremans, A. (1993) Mechanisms of implicit learning: Connectionist models of sequence processing. MIT Press.CrossRefGoogle Scholar
Cleeremans, A. & McClelland, J. L. (1991) Learning the structure of event sequences. Journal of Experimental Psychology: General 120:235–53.CrossRefGoogle ScholarPubMed
Collins, A. M. & Loftus, E. F. (1975) A spreading-activation theory of semantic processing. Psychological Review 82:407–28.CrossRefGoogle Scholar
Collins, A. M. & Quillian, M. R. (1969) Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior 8:240–47.CrossRefGoogle Scholar
Damasio, A. R. (1989) The brain binds entities and events by multiregional activation from convergence zones. Neural Computation 1:123–32.CrossRefGoogle Scholar
Eimas, P. D. & Quinn, P. C. (1994) Studies on the formation of perceptually based basic-level categories in young infants. Child-Development 65(3):903–17.CrossRefGoogle ScholarPubMed
Elman, J. L. (1990) Finding structure in time. Cognitive Science 14:179–211.CrossRefGoogle Scholar
Elman, J. L. (1991) Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning 7:194–220.CrossRefGoogle Scholar
Fodor, J. (2000) The mind doesn't work that way: The scope and limits of computational psychology. MIT Press/Bradford Books.CrossRefGoogle Scholar
Garcia, J. & Koelling, R. A. (1966) Relation of cue to consequence in avoidance learning. Psychonomic Science 4(3):123–24.CrossRefGoogle Scholar
Gelman, R. & Williams, E. M. (1998) Enabling constraints for cognitive development and learning: A domain-specific epigenetic theory. In: Handbook of child psychology, Vol. II: Cognition, perception and development, 5th edition, ed. Kuhn, D. & Siegler, R., pp. 575–630. Wiley.Google Scholar
Gelman, S. A. & Wellman, H. M. (1991) Insides and essences: Early understandings of the nonobvious. Cognition 38:213–44.CrossRefGoogle ScholarPubMed
Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Schulz, T. & Danks, D. (2004) A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review 111(1):131.CrossRefGoogle ScholarPubMed
Gopnik, A. & Meltzoff, A. N. (1997) Words, thoughts, and theories. MIT Press.Google Scholar
Gopnik, A. & Sobel, D. M. (2000) Detecting blickets: How young children use information about novel causal powers in categorization and induction. Child Development 71(5):1205–22.CrossRefGoogle ScholarPubMed
Gopnik, A. & Wellman, H. M. (1994) The theory theory. In: Mapping the mind: Domain specificity in cognition and culture, ed. Hirschfeld, L. A. & Gelman, S. A.. Cambridge University Press.Google Scholar
Hampton, J. A. (1993) Prototype models of concept representation. In: Categories and concepts: Theoretical views and inductive data analysis, ed. Van Mechelen, I., Hampton, J. A., Michalski, R. S. & Theuns, P., pp. 64–83. Academic Press.Google Scholar
Hinton, G. E. (1981) Implementing semantic networks in parallel hardware. In: Parallel models of associative memory, ed. Hinton, G. E. & Anderson, J. A., pp. 161–87. Erlbaum.Google Scholar
Hinton, G. E. (1986) Learning distributed representations of concepts. In: Proceedings of the 8th Annual Conference of the Cognitive Science Society, pp. 1–12. Erlbaum.Google Scholar
Hinton, G. E. & Sejnowski, T. J. (1986) Learning and relearning in Boltzmann machines. In: Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1, ed. Rumelhart, D. E. & McClelland, J. L., pp. 282–317. MIT Press.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. C. (1979) Semantic and conceptual development: An ontological perspective. Harvard University Press.CrossRefGoogle Scholar
Keil, F. C. (1989) Concepts, kinds, and cognitive development. MIT Press.Google Scholar
Keil, F. C. (1991a) The emergence of theoretical beliefs as constraints on concepts. In: The epigenesis of mind: Essays on biology and cognition, ed. Carey, S. & Gelman, R.. Erlbaum.Google Scholar
Keil, F. C. (1994) The birth and nurturance of concepts by domains: The origins of concepts of living things. In: Mapping the mind: Domain specificity in cognition and culture, ed. Hirschfeld, L. A. & Gelman, S. A., pp. 234–54. Cambridge University Press.CrossRefGoogle Scholar
Kruschke, J. K. (1992) ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review 99(1):22–44.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
MacKay, D. J. (1992) A practical Bayesian framework for backpropagation networks. Neural Computation 4:448–72.CrossRefGoogle Scholar
Mandler, J. M., Bauer, P. J. & McDonough, L. (1991) Separating the sheep from the goats: Differentiating global categories. Cognitive Psychology 23:263–98.CrossRefGoogle Scholar
Mandler, J. M. & McDonough, L. (1993) Concept formation in infancy. Cognitive Development 8:291–318.CrossRefGoogle Scholar
Mandler, J. M. & McDonough, L. (1996) Drinking and driving don't mix: Inductive generalization in infancy. Cognition 59:307–55.CrossRefGoogle ScholarPubMed
Mareschal, D. (2000) Infant object knowledge: Current trends and controversies. Trends in Cognitive Science 4:408–16.CrossRefGoogle ScholarPubMed
Marr, D. (1971) Simple memory: A theory for archicortex. Philosophical Transactions of the Royal Society of London, B 262:23–81.Google ScholarPubMed
Marr, D. (1982) Vision. Freeman.Google Scholar
Martin, A. & Chao, L. L. (2001) Semantic memory in the brain: Structure and processes. Current Opinion in Neurobiology 11:194–201.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):307–17.CrossRefGoogle Scholar
McClelland, J. L. (1989) Parallel distributed processing: Implications for cognition and development. In: Parallel distributed processing: Implications for psychology and neurobiology, ed. Morris, R. G. M., pp. 8–45. Oxford University Press.Google Scholar
McClelland, J. L. (1991) Stochastic interactive activation and the effect of context on perception. Cognitive Psychology 23:1–44.CrossRefGoogle Scholar
McClelland, J. L. (1994) Learning the general but not the specific. Current Biology 4:357–58.CrossRefGoogle Scholar
McClelland, J. L. (1998) Connectionist models and Bayesian inference. In: Rational models of cognition, ed. Oaksford, M. & Chater, N., pp. 21–53. Oxford University Press.Google 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 102:419–57.CrossRefGoogle ScholarPubMed
McClelland, J. L. & Rumelhart, D. E. (1986) A distributed model of human learning and memory. In: Parallel distributed processing: Explorations in the microstructure of cognition, vol. 2, ed. McClelland, J. L., Rumelhart, D. E. & the PDP Research Group, pp. 170–215. MIT Press.Google Scholar
McClelland, J. L., St. John, M. F. & Taraban, R. (1989) Sentence comprehension: A parallel distributed processing approach. Language and Cognitive Processes 4:287–335.CrossRefGoogle Scholar
Mervis, C. B. (1987) Child basic object categories and early lexical development. In: Concepts and conceptual development: Ecological and intellectual factors in categorization, ed. Neisser, U.. Cambridge University Press.Google Scholar
Movellan, J. & McClelland, J. L. (2001) The Morton-Massaro law of information integration: Implications for models of perception. Psychological Review 108:113–48.CrossRefGoogle ScholarPubMed
Mummery, C. J., Patterson, K., Price, C. J., Ashburner, J., Frackowiak, R. S. J. & Hodges, J. (2000) A voxel-based morphometry study of semantic dementia: Relationship between temporal lobe atrophy and semantic memory. Annals of Neurology 47(1):36–45.3.0.CO;2-L>CrossRefGoogle ScholarPubMed
Munakata, Y. & McClelland, J. L. (2003) Connectionist models of development. Developmental Science 6(4):413–29.CrossRefGoogle Scholar
Munakata, Y., McClelland, J. L., Johnson, M. H. & Siegler, R. (1997) Rethinking infant knowledge: Toward an adaptive process account of successes and failures in object permanence tasks. Psychological Review 104:686–713.CrossRefGoogle ScholarPubMed
Murphy, G. L. (2002) The big book of concepts. MIT Press.CrossRefGoogle Scholar
Murphy, G. L. & Medin, D. L. (1985) The role of theories in conceptual coherence. Psychological Review 92:289–316.CrossRefGoogle ScholarPubMed
Nisbett, R. E. & Wilson, T. D. (1977) Telling more than we can know: Verbal reports on mental processes. Psychological Review 84(3):231–59.CrossRefGoogle Scholar
Nosofsky, R. M. (1984) Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition 10:104–10.Google ScholarPubMed
Nosofsky, R. M. (1986) Attention, similarity and the identification-categorization relationship. Journal of Experimental Psychology: General 115(1):39–61.CrossRefGoogle ScholarPubMed
Oaksford, M. & Chater, N., eds. (1998) Rational models of cognition. Oxford University Press.Google Scholar
Pauen, S. (2002a) Evidence for knowledge-based category discrimination in infancy. Child Development 73(4):1016–33.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):492–99.CrossRefGoogle Scholar
Quinn, P. C. & Johnson, M. H. (2000) Global-before-basic object categorization in connectionist networks and 2-month-old infants. Infancy 1:31–46.CrossRefGoogle ScholarPubMed
Rips, L. J., Shoben, E. J. & Smith, E. E. (1973) Semantic distance and the verification of semantic relations. Journal of Verbal Learning and Verbal Behavior 12:1–20.CrossRefGoogle Scholar
Rogers, T. T., Lambon Ralph, M., Garrard, P., Bozeat, S., McClelland, J. L., Hodges, J. R. & Patterson, K. (2004) The structure and deterioration of semantic memory: A computational and neuropsychological investigation. Psychological Review 111 (1):205–35.CrossRefGoogle Scholar
Rogers, T. T. & McClelland, J. L. (2004) Semantic cognition: A parallel distributed processing approach. MIT Press.CrossRefGoogle Scholar
Rohde, D. L. T. & Plaut, D. C. (1999) Language acquisition in the absence of explicit negative evidence: How important is starting small? Cognition 72(1):67–109.CrossRefGoogle ScholarPubMed
Rosch, E. R. & Mervis, C. B. (1975) Family resemblances: Studies in the internal structure of categories. Cognitive Psychology 7:573–605.CrossRefGoogle Scholar
Rosch, E. R., 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: An introduction to neural and electronic networks, ed. Zornetzer, S. F., Davis, J. L. & Lau, C., pp. 405–20. Academic Press.Google Scholar
Rumelhart, D. E., Durbin, R., Golden, R. & Chauvin, Y. (1995) Backpropagation: The basic theory. In: Back-propagation: Theory, architectures, and applications, ed. Chauvin, Y. & Rumelhart, D. E., pp. 1–34. Erlbaum.Google Scholar
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986a) Learning representations by back-propagating errors. Nature 323(9):533–36.CrossRefGoogle Scholar
Rumelhart, D. E., Smolensky, P., McClelland, J. L. & Hinton, G. E. (1986c) Schemata and sequential thought processes in PDP models. In: Parallel distributed processing: Explorations in the microstructure of cognition, vol. 2, ed. McClelland, J. L., Rumelhart, D. E. & the PDP Research Group, pp. 7–57. MIT Press.CrossRefGoogle Scholar
Rumelhart, D. E. & Todd, P. M. (1993) Learning and connectionist representations. In: Attention and performance XIV: Synergies in experimental psychology, artificial intelligence, and cognitive neuroscience, ed. Meyer, D. E. & Kornblum, S., pp. 3–30. MIT Press.CrossRefGoogle Scholar
Smith, E. E. & Medin, D. L. (1981) Categories and concepts. Harvard University Press.CrossRefGoogle Scholar
Smith, L. B. (2000) From knowledge to knowing: Real progress in the study of infant categorization. Infancy 1(1):91–97.CrossRefGoogle Scholar
Smolensky, P. (1986) Information processing in dynamical systems: Foundations of harmony theory. In: Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1, ed. Rumelhart, D. E. & McClelland, J. L., pp. 194–281. MIT Press.Google Scholar
Spelke, E. S., Breinlinger, K., Macomber, J. & Jacobson, K. (1992) Origins of knowledge. Psychological Review 99(4):605–32.CrossRefGoogle ScholarPubMed
St. John, M. F. (1992) The story gestalt: A model of knowledge-intensive processes in text comprehension. Cognitive Science 16:271–306.Google Scholar
Wellman, H. M. & Gelman, S. A. (1997) Knowledge acquisition in foundational domains. In: Cognition, perception and development, 5th ed., vol. 2, ed. Kuhn, D. & Siegler, R., pp. 523–73. Wiley.Google Scholar
Wilson, R. A. & Keil, F. C. (2000) The shadows and shallows of explanation. In: Explanation and cognition, ed. Keil, F. C. & Wilson, R. A., pp. 87–114. MIT Press.CrossRefGoogle Scholar