Integrating Induction and Instruction: Connectionist Advice Taking

David C. Noelle, Garrison W. Cottrell

Humans improve their performance by means of a variety of learning strategies, including both gradual statistical induction from experience and rapid incorporation of advice. In many learning environments, these strategies may interact in complementary ways. The focus of this work is on cognitively plausible models of multistrategy learning involving the integration of inductive generalization and learning "by being told." Such models might be developed by starting with an architecture for which advice taking is relatively easy, such as one based upon a sentential knowledge representation, and subsequently adding some form of inductive learning mechanism. Alternatively, such models might be grounded in a statistical learning framework appropriately extended to operationalize instruction. This latter approach is taken here. Specifically, connectionist back-propagation networks are made to instantaneously modify their behavior in response to quasi-linguistic advice.


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