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A Graph-Dynamic Model of the Power Law of Practice and the Problem-Solving Fan-Effect

1988; American Association for the Advancement of Science; Volume: 242; Issue: 4877 Linguagem: Inglês

10.1126/science.3175664

1095-9203

Jeff Shrager, Tad Hogg, Bernardo A. Huberman,

Evolutionary Algorithms and Applications

Numerous human learning phenomena have been observed and captured by individual laws, but no unified theory of learning has succeeded in accounting for these observations. A theory and model are proposed that account for two of these phenomena: the power law of practice and the problem-solving fan-effect. The power law of practice states that the speed of performance of a task will improve as a power of the number of times that the task is performed. The power law resulting from two sorts of problem-solving changes, addition of operators to the problem-space graph and alterations in the decision procedure used to decide which operator to apply at a particular state, is empirically demonstrated. The model provides an analytic account for both of these sources of the power law. The model also predicts a problem-solving fan-effect, slowdown during practice caused by an increase in the difficulty of making useful decisions between possible paths, which is also found empirically.