Facilitating Scientific Understanding Using Computer Simulations

Educational Applications of Cognitive Science

We have begun to explore educational practices that allow students to attain sophisticated, abstract conceptualizations by initially grounding them in concrete, perceptual domains for which the student has already developed tools.¬†¬†Our laboratory has studied how concrete simulations allow a learner to acquire abstract scientific concepts.¬†¬†This research program originated from Rob Goldstone‚Äôs work in¬†developing interactive, dynamic computational simulations¬†for his course “Complex Adaptive Systems.”¬†The theoretical issues addressed by this work deal with the nature of abstract reasoning, the perceptual grounding of symbolic abstractions, and factors that influence analogical transfer from one situation to another.¬†¬†However, the research also has practical consequences for how simulations should be designed to maximize their educational impact.¬†¬†For example, we find that the comprehension and transfer of an abstract scientific principle such as simulated annealing is facilitated when the graphical elements comprising a simulation of the principle are idealized rather than perceptually detailed (Goldstone & Sakamoto, 2003).¬†The assumption of ‚Äúthe more realistic, the better‚ÄĚ that has been adopted by many virtual reality researchers is apparently not correct when it comes to teaching students abstract scientific concepts.¬†¬†Another technique that we have had success with is ‚Äúconcreteness fading,‚ÄĚ according to which originally rich and detailed representations are gradually idealized, allowing students to simultaneously ground their knowledge and transport it to superficially unrelated by deeply connected domains (Goldstone & Son, in press).


Our Selected Papers Relevant to Perceptual Learning, Conceptual Learning, and Conceptual Representation

Visit our paper repository for abstracts.  Clicking on the paper below will download a PDF version of the paper, but the repository has additional formats.

Goldstone, R. L., & Son, J. Y. (in press).  The transfer of scientific principles using concrete and idealized simulations.  The Journal of the Learning Sciences.

Goldstone, R. L, & Son, J. (in press).  Similarity.  In K. Holyoak & R. Morrison (Eds.).  Cambridge Handbook of Thinking and Reasoning. Cambridge: Cambridge University Press.

Goldstone, R. L. (2003).  Learning to perceive while perceiving to learn.  in R. Kimchi, M. Behrmann, and C. Olson (Eds.) Perceptual Organization in Vision: Behavioral and Neural Perspectives.  Mahwah, New Jersey.  Lawrence Erlbaum Associates. (pp. 233-278).

Goldstone, R. L., & Sakamoto, Y. (2003). The Transfer of Abstract Principles Governing Complex Adaptive Systems.  Cognitive Psychology, 46, 414-466.

Goldstone, R. L., & Johansen, M. K. (2003). Conceptual development from origins to asymptotes.  In D. Rakison & L. Oakes (Eds.) Categories and concepts in early development.  (pp. 403-418).  Oxford, England: Oxford University Press.

Goldstone, R. L., & Kersten, A. (2003). Concepts and Categories. In A. F. Healy & R. W. Proctor (Eds.) Comprehensive handbook of psychology, Volume 4: Experimental psychology.  (pp. 591-621).  New York: Wiley.

Goldstone, R. L. (1998). Perceptual Learning.  Annual Review of Psychology, 49, 585-612.

Goldstone, R. L., & Barsalou, L. (1998). Reuniting perception and conception. Cognition, 65, 231-262.

Schyns, P. G., Goldstone, R. L., & Thibaut, J-P (1998). Development of features in object concepts.  Behavioral and Brain Sciences, 21, 1-54.

Goldstone, R. L. (1994). The role of similarity in categorization: Providing a groundwork. Cognition, 52, 125-157.



Other Related Research on Educational Applications of Cognitive Science

John Bransford, now at University of Washington, has studied the impact of technology on learning, and in the course of doing so, has created many intelligent learning environments and curricular media.

Ken Koedinger has developed numerous computer-aided instructions systems for algebra, geometry, and the sciences.

Squeakland looks like an interesting software project aimed at teaching students the elements of programming and modeling.

David Uttal conducts research, some with his long-ago advisor Judy Deloache, on factors that promote symbolic understanding in children.

Uri Wilensky has developed Netlogo, a pedagogically oriented software package for creating complex systems and dynamically interacting with them. Students can relatively easily program their own complex system models and conduct experiments on them.  Our own software for complex systems is inspired by Netlogo, and its predecessor Starlogo.