A Connectionist Model f Instructed Learning

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 success 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 multistrategy 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 within a hybrid architecture (Maclin & Shavlik 1994). This work pursues the second of these three general approaches. Following an intuition concerning the primacy of induction (which precedes linguistic rule following both phylogenetically and ontogenetically) and with an eye on future reduction to neurological explanations, the model proposed here is built within a wholly connection& framework. The scientific goal of this research is to account for certain interaction effects between instruction and induction that have been observed in humans, including: