Strategically Logical: Experience-Related Changes in Strategy Use on Deductive Problems
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We propose a conceptual framework for explaining logical reasoning in terms of competing strategies. The Logical Strategy Model (LSM; Morris & Schunn, in press). describes a series of strategies that differ in their functionality. Algorithmic strategies (e.g., tokenbased, verbal) are more costly (i.e., longer processing time) but more accurate, while heuristic strategies (e.g., analogies, matching rules, knowledge-based rules) are less costly but less accurate. The LSM was tested with 45 undergraduates, 23 graduate students, and 15 children ages 8-11. Each subject was given 24 deductive problems and asked to reflect on how they solved a problem by selecting one of five descriptions of a strategy. We conducted this experiment to test three predictions of the LSM: (1) subjects should report using a variety of strategies across the problem set, (2) the use of algorithmic strategies should be associated with more correct responses while the converse should be true for heuristic strategies, and (3) increasing experience should be associated with greater use of algorithmic strategies. Prediction #1Variation in strategy use Figure 1 shows that all strategies were reported as being used. The most commonly reported strategies were as follows: KBH for grad students and children, and matching rules for undergrads.