In a previous study (Hickman & Laikin, 1990), we introduced a within-trial analogy mechanism called internal analogy that transfers both success and failure experiences between corresponding parts of the search tree for a single problem. In this paper, we describe powerful extensions to the learning procedure and their consequences on problem solving behavior First, we explain how our similarity metric can be naturally augmented to provide a more flexible partial match. To overcome the need for a static measure, however, we propose a mechanism that learns the appropriate level of partial match through feedback from previous analogical reasoning. Second, we show how this partial match mechanism controls the problem solver's search. Protocol dau from a subject working in a geometry theorem-proving domain provide support for the psychological fidelity of the extended internal analogy model.

Understanding problem solving and methods for learning is a main goal of cognitive science. Analogical reasoning simplifies problem solving by transferring previously learned knowledge from a source problem to the current target problem in order to reduce search. To provide a more detailed analysis of the mechanisms of transfer, w e describe a process called internal analogy that transfers experience from a completed subgoal in the same problem to solve the current target subgoal. W e explain what constitutes an appropriate source problem and what knowledge to transfer from that source, in addition to examining the associated memory organization. Unlike case-based reasoning methods, this process does not require large amounts of accumulated experience before it is effective; it provides useful search control at the outset of problem solving. Data from a study of subjects solving DC-circuit problems designed to facilitate transfer supports the psychological validity of the mechanism.