Formal models of cognition and cognitive development.One of the most important contributions of the “cognitive revolution” of the past 40 years or so was the development of rigorous ways to explore cognitive processes and to state theories about them. This approach, called the “information processing approach” actually is quite varied in both scope and precision. In Klahr (1989) and Klahr (1992), I attempt to lay out the scope and content of this type of research by distinguishing between what I called “soft-core” and “hard-core” IP approaches. Within the “hard-core” or computationally-based approach two types of theory building have emerged. One, the “symbolic” approach, posits models that are composed of knowledge units, typically cast as if-then rules. The other, “connectionist” or “sub-symbolic” or “parallel distributed processing (PDP)” approach seeks both a finer grain size and a closer link to neural orgaization. My own research has taken only the symbolic approach, although in Klahr & MacWhinney (1998), we describe the strengths and weaknesses of each. Many studies on production systems were conducted after they were first proposed as computational models of human problem-solving behavior by Allen Newell in the late 60's, and our aim in Klahr, Langley, & Neches (1987), was to provide examples of this type of research. Although production systems remain the underlying theoretical and computational basis for some of the leading models of cognition (e.g, work by my departmental colleagues John Anderson and Marcel Just), I have not been involved with computational models for quite a while, my most recent effort being a production system account of how young children acquire quantity conservation rules (Simon & Klahr, 1995; Simon, Newell, and Klahr). (Note: the “Simon” in these two papers is Tony Simon, who was a post-doc at CMU for several years; unrelated to Herb Simon). Klahr, D., Langley , P., & Neches , R. (Eds.). (1987). Production System Models of Learning and Development. Cambridge , MA : MIT Press. Neches, R., Langley, P., & Klahr, D. (1987) Learning, Development, and Production Systems. In Klahr, D., Langley , P., & Neches , R. (Eds.), Production System Models of Learning and Development. Cambridge , MA : MIT Press. pp 1-53 Klahr, D. (1989). Information processing approaches. In R. Vasta (Ed.), Annals of Child Development (pp. 131-185). JAI Press, Inc. Simon, T. J., Newell, A., & Klahr, D. (1991). A computational account of children's learning about number conservation. In D. Fisher & M. Pazzani (Eds.), Concept formation: knowledge and experience in unsupervised learning . San Mateo , CA : Morgan Kauffman. pp. 423-461 Klahr, D. (1992). Information processing approaches to cognitive development. In M. H. Bornstein & M. E. Lamb (Eds.), Developmental Psychology: An Advanced Textbook, 3rd Edition . Hillsdale , N.J. : Erlbaum. pp. 273-335 Simon, T. J.& Klahr, D.(1995) A computational theory of children's learning about number conservation. In Simon, T.J.; Halford, G.S.; Ed; Developing cognitive competence: New approaches to process modeling . Hillsdale , NJ , England : Erlbaum Klahr, D., & MacWhinney, B. (1998) Information Processing. In D. Kuhn & R. S. Siegler (Eds.), W. Damon (Series Ed.). Handbook of child psychology (5th ed.): Vol. 2: Cognition, perception, and language. New York : Wiley. (631-678) Klahr, D. (2004) Commentary: new kids on the connectionist modeling block. Developmental Science, 7(2), 165-166.
|
|---|