Connectionist Models in Spatial Cognition

  • Thea Ghiselli-Crippa
  • Stephen C. Hirtle
  • Paul Munro
Part of the GeoJournal Library book series (GEJL, volume 32)


This chapter reviews neural network approaches to the study of spatial cognition and spatial language with a focus on the representations and processes that are used by humans for encoding, storing, accessing, and referencing spatial knowledge. Such processes are used for recall of spatial information, for navigation through space, for spatial decision making, and for generating spatial descriptions. Connectionist models for representing the structure of cognitive maps and understanding the language of spatial relations are discussed in detail, using the representation adopted by the model to evaluate the usefulness of each approach. In addition, the functional differences within neural network models, such as distributed versus local representations, are discussed. Finally, an agenda for future development of connectionist models, including the exploration of alternative network structures and data types, is proposed.


Spatial Relation Semantic Representation Spatial Cognition Connectionist Model Computational Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Angéniol, B., Vaubois, G., and Texier, J.-Y. (1988). Self-organizing feature maps and the travelling salesman problem. Neural Networks 1, 289–293.CrossRefGoogle Scholar
  2. Cosic, C. and Munro, P. W. (1988). Learning to represent and understand locative prepositional phrases. In Proceedings of the 10th Annual Conference of the Cognitive Science Society, pp. 257–262. Hillsdale, NJ: Erlbaum.Google Scholar
  3. Elman, J. L. (1990). Finding structure in time. Cognitive Science 14, 179–211.CrossRefGoogle Scholar
  4. Fischer, M. M. (1993). Expert systems and artificial neural networks for spatial analysis and modelling: Essential components for knowledge based geographical information systems. Geographical Systems 1, 221–235.Google Scholar
  5. Ghiselli-Crippa, T. B., and Munro, P. W. (1994). Emergence of global structure from local associations. In Advances in Neural Information Processing Systems 6 (J.D. Cowan, G. Tesauro, and J. Alspector, eds.), San Francisco, CA: Morgan Kaufmann.Google Scholar
  6. Gopal, S., Klatzky, R. L., and Smith, T. R. (1989). Navigator-A psychologically based model of environmental learning through navigation. Journal of Environmental Psychology 9, 309–331.CrossRefGoogle Scholar
  7. Halmari, P. M., and Lundberg, C. G. (1991). Bridging inter-and intra-corporate information flows with neural networks. Paper presented at the Annual meeting of the Association of American Geographers, Miami, April 13–17.Google Scholar
  8. Heidorn, P. B., and Hirtle, S. C. (1993). Is spatial information imprecise or just coarse? Behavioral and Brain Sciences 16, 246–247Google Scholar
  9. Herskovits, A. (1986). Language and Spatial Cognition. Cambridge: Cambridge University Press.Google Scholar
  10. Hinton, G. E., McClelland, J. L., and Rumelhart, D. E. (1986). Distributed representations. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations (D. E. Rumelhart, J. L. McClelland, and The PDP Research Group), pp. 77–109). Cambridge, MA: MIT Press.Google Scholar
  11. Hirtle, S. C, and Heidorn, P. B. (1993). The structure of cognitive maps: Representations and processes. In Behavior and Environment: Psychological and Geographical Approaches (T. Garling and R. G. Golledge, eds.), pp. 170–192. Amsterdam: North-Holland.Google Scholar
  12. Hirtle, S. C, and Jonides, J. (1985). Evidence of hierarchies in cognitive maps. Memory and Cognition 13, 208–217.Google Scholar
  13. Jordan, M. I. (1986). Attractor dynamics and parallelism in a connectionist sequential machine. In Proceedings of the Eighth Annual Conference of the Cognitive Science Society, 531–546.Google Scholar
  14. Kaplan, S., and Kaplan, R. (1982). Cognition and Environment: Functioning in an Uncertain World. New York, NY: Praeger.Google Scholar
  15. Kaplan, S., Weaver, M., and French, R. (1990). Active symbols and internal models: Towards a cognitive connectionism. AI and Society 4, 51–71.CrossRefGoogle Scholar
  16. Kuipers, B. J., and Levitt, T. S. (1988). Navigation and mapping in large-scale space. AI Magazine 9, 25–43.Google Scholar
  17. Landau, B. and Jackendoff, R. (1993). “What” and “where” in spatial language and spatial cognition. Behavioral and Brain Sciences 16, 217–265.CrossRefGoogle Scholar
  18. Levenick, J. R. (1985). Knowledge Representation and Intelligent Systems: From Semantic Networks to Cognitive Maps. Unpublished Ph.D. Dissertation, Department of Computer and Communication Sciences, University of Michigan.Google Scholar
  19. McClelland, J. L., and Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: Part 1. An account of basic findings. Psychological Review 88, 375–407.CrossRefGoogle Scholar
  20. McNamara, T. P. (1986). Mental representations of spatial relations. Cognitive Psychology 18, 87–121.CrossRefGoogle Scholar
  21. McNamara, T. P., Halpin, J. A., and Hardy, J. K. (1992). Spatial and temporal contributions to the structure of spatial memory. Journal of Experimental Psychology: Learning, Memory, and Cognition 18, 555–564.CrossRefGoogle Scholar
  22. McNamara, T. P., Ratcliff, R., and McKoon, G. (1984). The mental representation of knowledge acquired from maps. Journal of Experimental Psychology: Learning, Memory, and Cognition 10, 723–732.CrossRefGoogle Scholar
  23. Munro, P. W., Cosic, C. and Tabasko, M. (1991). A network for encoding, decoding and translating locative prepositions. Connection Science 3, 225–240.CrossRefGoogle Scholar
  24. Munro, P. W., and Hirtle, S. C. (1989). An interactive activation model for priming of geographical information. In Proceedings of the 11th Annual Conference of the Cognitive Science Society, pp. 773–780. Hillsdale, NJ: Erlbaum.Google Scholar
  25. Munro, P. W., and Tabasko, M. (1991). Translating locative prepositions. In Advances in Neural Information Processing Systems 3 (R.P. Lippman, J. E. Moody, and D. S. Touretzky, eds.), pp. 598–604. San Francisco, CA: Morgan Kaufmann.Google Scholar
  26. O’Neill, M. (1991). A biologically based model of spatial cognition and wayfinding. Journal of Environmental Psychology 11, 299–320.CrossRefGoogle Scholar
  27. Pearlmutter, B. A. (1989). Learning state space trajectories in recurrent neural networks. Neural Computation 1, 263–269.Google Scholar
  28. Retz-Schmidt, B. (1988). Various views on spatial prepositions. AI Magazine 9, 95–105.Google Scholar
  29. Siegel, A. W., and White, S. H. (1975). The development of spatial representations of large-scale environments. In Advances in Child Development and Behavior (H.W. Reese, ed.), pp. 9–55. New York: Academic Press.Google Scholar
  30. Touretzky, D. S., Redish, A. D., and Wan, H. S. (1993). Neural representation of space using sinusoidal arrays. Neural Computation 5, 869–884.Google Scholar
  31. Wender, K. F. (1989). Connecting analog and verbal representations for spatial relations. Paper presented at the 30th Annual Meeting of the Psychonomic Society, Atlanta, GA.Google Scholar
  32. Wender, K. F., and Wagener, M. (1990). Zur Verarbeitung räumlicher Informationen: Modelle und Experimente (The processing of spatial information: models and experiments). Kognitionswissenschaft 1, 4–14.Google Scholar
  33. Williams, R. J., and Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1, 270–280.CrossRefGoogle Scholar
  34. Yeap, W. K. (1988). Towards a computational theory of cognitive maps. AI 34, 297–360.Google Scholar
  35. Zipser D. (1986). Biologically plausible models of place recognition and goal location. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 2: Psychological and Biological Models (J.L. McClelland, D.E. Rumelhart, and The PDP Research Group), pp. 432–470. Cambridge, MA: MIT Press.Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Thea Ghiselli-Crippa
    • 1
  • Stephen C. Hirtle
    • 1
  • Paul Munro
    • 1
  1. 1.Department of Information ScienceUniversity of PittsburghPittsburghUSA

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