Symbolic and Sub-Symbolic Representations in Computational Models of Human Cognition: What Can be Learned from Biology?

Theory & Psychology

Volumn 13, Issue 6, 2003, Pages 847-860

  Kelley, Troy D ^a  

^a U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

evolutionary psychology; hybrid architectures; integrated cognitive architectures; knowledge representation

Indexed keywords

EID: 0347568230     PISSN: 09593543     EISSN: 14617447     Source Type: Journal    
DOI: 10.1177/0959354303136005     Document Type: Article

References (48)

1. Anderson, J.R., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Lawrence Erlbaum Associates,
2. Barnden, J.A., & Holyoak, K.J. (Eds.). (1994). Advances in connectionist and neural computation theory: Vol. 3. Analogy, metaphor and reminding. Norwood, NJ: Ablex.
3. Beaumont, J.G., Kenealy, P.M., & Rogers, M.J.C. (Eds.). (1996). The Blackwell dictionary of neuropsychology. Malden, MA: Blackwell.
4. Fodor, J.A. (1975). The language of thought. New York: Crowell.
5. Fodor, J.A., & Pylyshyn, Z.W. (1988). Connectionism and the cognitive architecture: A critical analysis. Cognition, 28, 3–71.
6. Gallant, S.I. (1993). Neural network learning and expert systems. Cambridge, MA: MIT Press.
7. Gardner, B.T., & Gardner, R.A. (1971). Two way communication with an infant chimpanzee. In A.M. Schrier & F. Stollnitz (Eds.), Behavior of nonhuman primates (Vol. 4, pp. 117–135). New York: Academic Press.
8. Goonatilake, S., & Khebbal, S. (1995). Intelligent hybrid systems. Chichester: Wiley.
9. Harnad, S. (1989). Minds, machines and Searle. Journal of Theoretical and Experimental Artificial Intelligence, 1, 5–25.
10. Hayes, K.J., & Hayes, C. (1953). Picture perception in a home-raised chimpanzee. Journal of Comparative and Physiological Psychology, 46, 470–474.
11. Herman, L.M., Matus, E.Y.K., Herman, M.I., & Pack, A.A. (2001). The bottlenosed dolphin's (Tursiops truncatus) understanding of gestures as symbolic representations of its body parts. Animal Learning and Behavior, 29, 250–264.
12. Kelley, T.D. (2001). An overview of modeling and integration of neurological dynamics with symbolic structures (MINDSS). In W.F. Waite (Ed.), Proceedings of 2000 Summer Computer Simulation Conference. (pp. 730–735 ). Society for Computer Simulation, San Diego, CA.
13. Lenat, D.B., & Guha, R.V. (1989). Building large knowledge-based systems: Representation and inference in the Cyc project. New York: Addison Wesley Longman.
14. Marin-Padilla, M. (1988). Early ontogensis of the human cerebral cortex. In A. Peters & E.G. Jones (Eds.), Cerebral cortex (Vol. 7, pp. 1–34). New York: Plenum.
15. Matsumoto, R., Walker, B., Walker, J.M., & Hughes, H.C. (1990). Fundamentals of neuroscience. In J.T. Cacioppo & L.G. Tassinary (Eds.), Principles of psycho-physiology (pp. 58–112). New York: Cambridge University Press.
16. Matsuzawa, T. (1989). Spontaneous pattern construction in a chimpanzee. In P. Heltne & L.A. Marquardt (Eds.), Understanding chimpanzees (pp. 252–265). Cambridge, MA: Harvard University Press.
17. McCullogh, W.S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.
18. Medsker, L.R. (1994). Hybrid neural networks and expert systems. Boston, MA: Kluwer Academic.
19. Medsker, L.R. (1995). Hybrid intelligent systems. Boston, MA: Kluwer Academic.
20. Metter, E.J., Riege, W.H., Hanson, W.R., Jackson, C.A., Kempler, D., & VanLancker, D. (1988). Subcortical structures in aphasia: An analysis based on (F-18)–florodoxyglucose positron emission tomography, and computed tomography. Archives of Neurology, 45, 1229–1234.
21. Miikkulainen, R. (1993). Subsymbolic natural language processing. Cambridge, MA: MIT Press,
22. Pylyshyn, Z. (1984). Computation and cognition: Toward a foundation for cognitive science. Cambridge, MA: MIT Press/Bradford.
23. Pylyshyn, Z. (1989). Computing in cognitive science. In M. Posner (Ed.), Foundations of cognitive science (pp. 51–91). Cambridge, MA: MIT Press.
24. Reilly, R.G., & Sharkey, N.E. (1992). Connectionist approaches to natural language processing. Hillsdale, NJ: Erlbaum.
25. Rosenblatt, F. (1958). The perceptron: A probabilistic method for information storage and organization in the brain. Psychological Review, 65, 386–408.
26. Rosenblatt, F. (1962). Principles of neurodynamics. New York: Spartan.
27. Rumbaugh, D.M., von Glasersfeld, E., Warner, H., Pisani, P.P., Gill, T.V., Brown, J.V., & Bell, C.L. (1973). A computer-controlled language training system for investigating the language skills of young apes. Behavioral Research Methods and Instrumentation, 5, 385–392.
28. Rumbaugh, D.M. (1981). Who feeds Clever Hans? In T.A. Sebeok & R. Rosenthal (Eds.), The Clever Hans phenomenon: Communication with horses, whales, apes and people [Special Issue]. Annals of the New York Academy of Sciences, 364, 26–34.
29. Rumbaugh, D.M., & Savage-Rumbaugh, E.S. (1980, Winter). A response to Herbert Terrace's article ‘Linguistic apes: What are they saying?’ New York University Education Quarterly, p. 33-33.
30. Savage-Rumbaugh, E.S. (1986). Ape language: From conditioned response to symbol. New York: Columbia University Press.
31. Savage-Rumbaugh, E.S. (1987). Communication, symbolic communication, and language: Reply to Seidenberg and Petitto. Journal of Experimental Psychology: General, 116, 288–292.
32. Savage-Rumbaugh, E.S., Murphy, J., Sevcik, R.A., Rumbaugh, D.M., Brakke, K.E. & Williams, S. (in press). Language comprehension in ape and child. Monographs of Society for Child Development.
33. Savage-Rumbaugh, E.S., Romski, M.A., Sevcik, R.A., & Pate, J.L. (1983). Assessing symbol usage versus symbol competency. Journal of Experimental Psychology, General, 112, 508–512.
34. Savage-Rumbaugh, E.S., & Rumbaugh, D.M. (1982). Ape language is alive and well. Anthropos, 77, 568–573.
35. Searle, J.R. (1979). What is an intentional state? Mind, 88, 72–94.
36. Sebeok, T.A., & Umiker-Sebeok, J. (1980). Speaking of apes: A critical analysis of two-way communication with man. New York: Plenum.
37. Shastri, L., & Ajjanagadde, V. (1993). From simple associations to systematic reasoning: A connectionist representation of rules, variables, and dynamic bindings using temporal synchrony. Behavioral and Brain Sciences, 16, 417–494.
38. Simpson, S.J., & White, P.R. (1990). Associative learning and locusts feeding: Evidence for a ‘learned hunger’ for protein. Animal Behavior, 40, 506–513.
39. Smolensky, P. (1995). Reply: Constituent structure and explanation in an integrated connectionist/symbolic cognitive architecture. In C. Macdonald & G. Macdonald (Eds.), Connectionism: Debates on psychological explanation (Vol. 2, pp. 223–290). Oxford/Cambridge, MA: Blackwell.
40. Sohn, M., Ursu, S., Anderson. J. R., Stenger, A.V., & Carter, C.S. (2000). The role of prefrontal cortex and posterior parietal cortex in task switching. Inaugural Articles of the National Academy of Sciences, PNAS, 97(24), 13448–13453.
41. Sun, R., & Alexandre, F. (1995). Proceedings of the workshop on connectionist-symbolic integration: From unified to hybrid approaches. Montreal: McGraw-Hill.
42. Sun, R., & Alexandre, F. (Eds.). (1997). Connectionist symbolic integration: From unified to hybrid approaches. Hillsdale, NJ: Erlbaum.
43. Sun, R., & Bookman, L.A. (1995). Computational architectures integrating neural and symbolic processes: A perspective on the state of the art. Norwell, MA: Kluwer Academic Publishers.
44. Sun, R., & Peterson, T. (1997). A hybrid agent architecture for reactive sequential decision making. In R. Sun & F. Alexandre (Eds.), Connectionist symbolic integration: From unified to hybrid approaches (pp. 113–138). Hillsdale, NJ: Erlbaum.
45. Terrace, H.S., Straub, R.O., Bever. T. G., & Seidenberg, M.S. (1977). Representation of a sequence by a pigeon. Bulletin of the Psychonomic Society, 10, 269-269.
46. Thompson, C.R., & Church, R.M. (1980). An explanation of the language of chimpanzee. Science, 208, 313–314.
47. Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59, 433–460.
48. Wermeter, S., Riloff, E., & Scheler, G. (1997). Connectionist, statistical and symbolic approaches to learning for natural language processing. Berlin: Springer.