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Cascade correlation (CC) has proven to be an effective tool for simulating human learning. One important class of problem solving tasks can be thought of as establishing appropriate connections between inputs and outputs. A CC network initially attempts to solve the task with a minimal network configuration, but when the task cannot be solved, it is powered up by recruiting a hidden unit to capture the uncaptured aspects of the input-output relationship until a satisfactory degree of performance is reached. Knowledge-based CC (KBCC) has a similar mechanism, but instead of recruiting hidden units, it can recruit other networks previously trained with similar tasks. In this paper we demonstrate the usefulness of these network tools for simulating learning behavior by human subjects.
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