Lane, P.C.R., Gobet, F. and Cheng, P.C-H. (in press).

Learning-based constraints on schemata. Proceedings of the Twenty Second Annual Meeting of the Cognitive Science Society, Philadelphia, USA, 2000.



Schemata are frequently used in cognitive science as a descriptive framework for explaining the units of knowledge within humans. However, the specific properties which comprise a schema usually vary between authors. In this paper we attempt to ground the concept of a schema based on constraints arising from issues of learning. To do this, we consider the different forms of schemata used in computational models of learning. We propose a framework for comparing forms of schemata which is based on the underlying representation used by each model, and the mechanisms used for learning and retrieving information from its memory. Based on these three characteristics, we compare examples from three classes of model, identified by their underlying representations, specifically: neural network, production-rule and symbolic network models.