Proceedings of the Annual Meeting of the Cognitive Science Society

Using a process model of skill acquisition allo- wed us to examine the microstructure of subjects' performance of a scheduling task. The model, im- plemented in the Soar-architecture, fits many qua- litative (e.g., learning rate) and quantitative (e.g., solution time) effects found in previously collec- ted data. T h e model's predictions were tested with data from a new study where the identical task was given to the model and to 14 subjects. Again a general fitof the model was found with the restrictions that the task is easier for the m o - del than for subjects and its performance impro- ves more quickly. T h e episodic memory chunks it learns while scheduling tasks show h o w acquisition of general rules can be performed without resort to explicit declarative rule generation. T h e model also provides an explanation of the noise typically found when fittinga set of data to a power law — it is the result of chunking over actual knowledge rather than "average" knowledge. Only when the data are averaged (over subjects here) does the smooth power law appear.