EPAM is a theory of the processes of human perception and memory, first programmed for a computer by E. A. Feigenbaum in 1959, that has shown an excellent fit to experimental data from a wide variety of psychological tasks. Over the years, it has been progressively extended to new domains without essential change in its central mechanisms. This article examines EPAM IV, a version extended to account for expert memory, especially the work in recent years by Chase and Ericsson (1981, 1982) and Staszewski (1988a, 1988b, 1990). EPAM IV has also been adapted to deal with numerous other short-term and long-term memory tasks, which will be reported elsewhere. The main modifications of EPAM that are relevant to the serial recall task examined in this article are a schema in long-term memory (called a retrieval structure) created by the expert's learning and the addition of an associative search process in long-term memory. These new components operate in close interaction with the other EPAM structures to match the observed behavior. EPAM IV reproduces all of the phenomena explained previously by EPAM III and in addition gives an accurate detailed account of the performance (studied by Staszewski) of an expert recalling long sequences of digits. The theory substantially revises, improves, and extends Chase and Simon's earlier "chunking" explanation of expert memory.

If strategy shifts speed up performance, learning curves should show discontinuities where such shifts occur. Relatively smooth curves appear consistently in the literature, however. To explore this incongruity, we examined learning when multiple strategies were used. We plotted power law learning curves for aggregated data from four mental arithmetic experiments and then plotted similar curves separately for each participant and strategy. We then evaluated the fits achieved by each group of curves. In all four experiments, plotting separately by strategy produced significantly better fits to individual participants' data than did plotting a single power function. We conclude that improvement of solution time is better explained by practice on a strategy than by practice on a task, and that careful assessment of trial-by-trial changes in strategy can improve understanding of the effects of practice on learning. 0The generality and precision simultaneously achieved by expressing empirical regularities as mathematical functions facilitates theoretical development, testing, and the application of scientific knowledge. Although mathematical laws are more prevalent in the physical sciences than in the social sciences, psychology's search for quantitative laws that describe human behavior is long-standing, dating back to the 1850s. A few notable successes have been achieved, including Fitts's law (1954) and the Hick-Hyman law (Hick, 1952;Hyman, 1953). Newell and Rosenbloom (1981) proposed another candidate for the status of quantitative psychological law. They argued that the power law of practice 1 offers a sufficiently accurate, general, and useful characterization of human skill acquisition. This article examines that proposal in the light of empirical evidence that strategy changes sometimes play an important role in cognitive skill acquisition. Such evidence raises questions about the adequacy of the regular power law as a complete descriptor of the temporal course of complex human learning. Our goal is to describe the tension arising between the general formulation of the regular power law and the data on strategy shifts and then to suggest a way to reconcile the two bodies of evidence.It is well established that practice on a task almost always improves performance, both by reducing the number of errors and by reducing the time required to perform the task. Many longitudinal studies using performance time (e.g., solution time for problems, reaction time to stimuli) to measure skill acquisition have shown a remarkable regular-0.1. This regularity was first noted by Lewis (1976) in an unpublished manuscript that Newell acknowledged reading.

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Lifetime performance data of 388 baseball players active in 1965 were analyzed to determine the age of peak performance for skills required to play baseball, to derive age-performance curves for athletic productivity, and to assess the magnitude of individual differences among elite and less able players. Cross-sectional and longitudinal analyses show that athletic performance on key indicators rises relatively quickly from age 19 to a peak age of 27 and then declines. The primary difference between elite and less able players is that performance of the elite players remains high for a longer period of time and decays more gradually. The performance of the most elite players is superior to that of less able players even at very early ages. These results parallel findings reported for other achievement domains and can be explained in terms of basic developmental processes involving the interaction of experience, physiological capacity, and motivation.