A Connectionist Model of Instructed Learning

Abstract

The focus of this research is on how people blend knowledge gained through explicit instruction with knowledge gained through experience. The product of this work will be a cognitively plausible computational learning model which integrates instructed learning with inductive generalization from examples. The suc-cess of this model will require the attainment of both a technical and a scientific goal. The technical goal is the design of a computational mechanism in which induction and instruction are smoothly integrated. The design of such a multistrat-egy learner might be implemented within a symbolic rule-based framework (Huffman, Miller, & Laird 1993), within a framework strong in inductive generalization, such as connectionism (Noelle & Cottrell 1995), or