Anderson and Milson (1989) derived optimal performance functions for memory based on assumptions about the goals of memory, the computational costs of achieving those goals, and the statistical structure of the environment. Based on these assumptions, and a good deal of Bayesian analysis, they accounted for a substantial number of empirical findings. Here we started with the same assumptions about the goals of memory, but instead of simulating the statistical structure of the environment, we analyzed it directly. It was found that the factors that govern memory performance also predict the probability with which words are spoken in children's linguistic environments. These factors include frequency, recency, and spacing between exposures. The ability of these factors to predict word use was analyzed in the context of four laboratory memory phenomena: 1) the power law of practice; 2) the power law of forgetting; 3) the interaction between study spacing and retention interval and 4) the combined effects of practice and retention. These factors predict information demand and lend strong support to Anderson and Milson's claim that memory behavior can be understood in terms of the statistical structure of the environment.

Central to the rational analysis of m e m o r y is the proposal that memory's sensitivity to statistical structure in the environment enables it to optimally estimate the odds that a m e m o r y trace will be needed n o w (Anderson, 1990). These odds are based on (1) the pattern of prior use of the m e m o r y (e.g., h o w recently it has been needed) and (2) the similarity of the current context to the previous contexts in which it has been needed. W e have analyzed three sources of informational demand in the environment: (1) speech to children; (2) word usage in front page headlines; and (3) the daily distribution of authors of electronic mail. W e found that the factors that govern m e m o r y performance, including recency, also predict the odds that an item (e.g., a word or author) will be encountered now. Here w e tested a basic prediction the theory makes about the independence between context and recency in the environment by extending our previous analysis of recency in the N e w York Times. Though the results of four behavioral experiment were inconsistent with this independence assumption, the combination of the rational and environmental analyses were able to account for 9 4 % of the variance in these experiments.

Three experiments investigate the influence of feedback timing on skill acquisition in the context of learning LISP. In experiment 1 subjects receiving immediate feedback went through the training material in 4 0 % less time than did those receiving delayed feedback, but learning was not impaired. A second experiment involved the use of an improved editor and less supportive testing conditions. Though subjects in the immediate condition went through the training problems 1 8% faster (han did those in the delay condition, they were slower on the test problems and made twice as many errors. The results of experiment 3, a partial replication of the first two experiments, indicated a general advantage for delayed feedback in terms of errors, time on task, and the jjercentage of errors that Subjects self-corrected. A protocol analysis suggests that immediate feedback competes for wodcing memory resources, forcing out information necessary for operator compilation. In addition, more delayed feedback appears to foster the development of secondary skills such as error detection and self-correction, skills necessary for successful performance once feedback has been withdrawn (Schmidt, Young, Swinnen, & Shapiro, 1989).

This paper describes our model for diagnosis of student errors in statistical problem solving.A simulation of that diagnosis, GIDE, is presented together with empirical validation on student solutions. The model consists of two components. An "intention based"diagnostic component analyzes solutions and locates errors by trying to synthesize student solutions from knowledge about the goal structure of the problem and related knowledge about planning errors. This approach can account for about 8 2 % of the lines and over 9 5 % of the goals in a set of 60 student t-tests. When solutions contain errors in procedural implementation such plan-based analysis is quite effective. In many cases,however, students do not pursue an "appropriate" solution path. The diagnostic model,therefore, includes a second component which is used to determine which type of problem the student is using, it is modeled by a spreading activation network of statistical knowledge.On a sample of 38 student solutions, the simulation correctly identified 8 6 % of the problem types. The model appears to account for a wide range of problem-solving behavior within the domain studied. The preliminary performance data suggest that our model may serve as a useful part of an intelligent tutoring system.

We present the project aimed at creating a database of detailed architectural process models of memory-based decision models. Those models are implemented in the cognitive architecture ACT-R. In creating this database, we have identified commonalities and differences of various decision models in the literature. The model database can provide insights into the interrelation among decision models and can be used in future research to address debates on inferences from memory, which are hard to resolve without specifying the processing steps at the level of precision that a cognitive architecture provides.