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Strategy Shifts Without Impasses: A Computational Model of the Sum-to-Min Transition

Abstract

The SuM-to-MiN transition that children exhibit when learning to add provides an ideal domain for studying naturally occurring discovery processes. We discuss a computational model that accounts for this transition, including the appropriate intermediate strategies. In order to account for all of these shifts, the model must sometimes learn without the benefit of impasses. Our model smoothly integrates impasse-driven and impasse free learning in a single, simple learning mechanism.

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