A framework for skill acquisition is proposed that includes two major stages in the development of a cognitive skill: a declarative stage in which facts about the skill domain are interpreted and a procedural stage in which the domain knowledge is directly embodied in procedures for performing the skill. This general framework has been instantiated in the ACT system in which facts are encoded in a propositional network and procedures are encoded as productions. Knowledge compilation is the process by which the skill transits from the declarative stage to the procedural stage. It consists of the subprocesses of composition, which collapses sequences of productions into single productions, and proceduralization, which embeds factual knowledge into productions. Once proceduralized, further learning processes operate on the skill to make the productions more selective in their range of applications. These processes include generalization, discrimination, and strengthening of productions. Comparisons are made to similar concepts from past learning theories. How these learning mechanisms apply to produce the power law speedup in processing time with practice is discussed. It requires at least 100 hours of learning and practice to acquire any significant cognitive skill to a reasonable degree of proficiency. For instance, after 100 hours a student learning to program a computer has achieved only a very modest facility in the skill. Learning one's primary language takes tens of thousands of hours. The psychology of human learning has been very thin in ideas about what happens to skills under the impact of this amount of learning—and for obvious reasons. This article presents a theory about the changes in the nature of a skill over such large time scales and about the basic learning processes that are responsible.

This article presents a theory in which automatization is construed as the acquisition of a domain-specific knowledge base, formed of separate representations, instances, of each exposure to the task. Processing is considered automatic if it relies on retrieval of stored instances, which will occur only after practice in a consistent environment. Practice is important because it increases the amount retrieved and the speed of retrieval; consistency is important because it ensures that the retrieved instances will be useful. The theory accounts quantitatively for the power-function speed-up and predicts a power-function reduction in the standard deviation that is constrained to have the same exponent as the power function for the speed-up. The theory accounts for qualitative properties as well, explaining how some may disappear and others appear with practice. More generally, it provides an alternative to the modal view of automaticity, arguing that novice performance is limited by a lack of knowledge rather than a scarcity of resources. The focus on learning avoids many problems with the modal view that stem from its focus on resource limitations. Automaticity is an important phenomenon in everyday men-tal life. Most of us recognize that we perform routine activities quickly and effortlessly, with little thought and conscious aware-ness--in short, automatically (James, 1890). As a result, we of-ten perform those activities on "automatic pilot " and turn our minds to other things. For example, we can drive to dinner while conversing in depth with a visiting scholar, or we can make coffee while planning dessert. However, these benefits may be offset by costs. The automatic pilot can lead us astray, caus-ing errors and sometimes catastrophes (Reason & Myceilska, 1982). If the conversation is deep enough, we may find ourselves and the scholar arriving at the office rather than the restaurant, or we may discover that we aren't sure whether we put two or three scoops of coffee into the pot. Automaticity is also an important phenomenon in skill acqui-sition (e.g., Bryan & Harter, 1899). Skills are thought to consist largely of collections of automatic processes and procedures

We present data and argument to show that in Tetris-a real-time, interactive video game-certain cognitive and perceptual problems ore more quicktv, easily, and reliably solved by performing actions in the world than by performing com-putational actions in the head atone. We have found that some of the translations and rotations made by players of this video game are best understood as actions that use the world to improve cognition. These actions are not used to implement a plan, or to implement a reaction; they are used to change the world in order to simplify the problem-solving task. Thus, we distinguish pragmatic octions--actions performed to bring one physically closer to a goal-from epistemic actions-actions performed to uncover informatioan that is hidden or hard to compute mentally. To illustrate the need for epistemic actions, we first develop a standard information-processing model of Tetris cognition and show that it cannot explain performance data from human players of the game-even when we relax the assumption of fully sequential processing. Standard models disregard many actions taken by players because they appear unmotivated or superfluous. How-ever, we show that such actions are actually far from superfluous; they play a valuable role in improving human performance. We argue that traditional accounts are limited because they regard action as having o single function: to change the world. By recognizing a second function of action-an epistemic func-tion-we can explain many of the actions that a traditional model cannot. Al-though our argument is supported by numerous examples specifically from Tetris, we outline how the new category of epistemic action can be incorporated into theories of action more generally. In this article, we introduce the general idea of an epistemic action and discuss its role in Tetris, a real-time, interactive video game. Epistemic actions-physical actions that make mental computation easier, faster, or more We thank Steve Haehnichen for his work on the initial implementations of Tetris and

Modeling and predicting user surfing paths involves tradeoffs between model complexity and predictive accuracy. In this paper we explore predictive modeling techniques that attempt to reduce model complexity while retaining predictive accuracy. We show that compared to various Markov models, longest repeating subsequence models are able to significantly reduce model size while retaining the ability to make accurate predictions. In addition, sharp increases in the overall predictive capabilities of these models are achievable by modest increases to the number of predictions made. 1. Introduction Users surf the World Wide Web (WWW) by navigating along the hyperlinks that connect islands of content. If we could predict where surfers were going (that is, what they were seeking) we might be able to improve surfers' interactions with the WWW. Indeed, several research and industrial thrusts attempt to generate and utilize such predictions. These technologies include those for searching thro...

The theoretical framework presented in this article explains expert performance as the end result of individuals ' prolonged efforts to improve performance while negotiating motivational and external constraints. In most domains of expertise, individuals begin in their childhood a regimen of effortful activities (deliberate practice) designed to optimize improvement. Individual differences, even among elite performers, are closely related to assessed amounts of deliberate practice. Many characteristics once believed to reflect innate talent are actually the result of intense practice extended for a minimum of 10 years. Analysis of expert performance provides unique evidence on the potential and limits of extreme environmental adaptation and learning. Our civilization has always recognized exceptional individuals, whose performance in sports, the arts, and science is vastly superior to that of the rest of the population. Speculations on the causes of these individuals ' extraordinary abilities and performance are as old as the first records of their achievements. Early accounts commonly attribute these individuals' outstanding performance to divine intervention, such as the

The authors propose and test an exemplar-based random walk model for predicting response times in tasks of speeded, multidimensional perceptual classification. The model combines elements of R.M. Nosofsky's (1986) generalized context model of categorization and G. D. Logan's (1988) instance-based model of automaticity. In the model, exemplars race among one another to be retrieved from memory, with rates determined by their similarity to test items. The retrieved exemplars provide incremental information that enters into a random walk process for making classification decisions. The model predicts correctly effects of within- and between-categories similarity, individual-object familiarity, and extended practice on classification response times. It also builds bridges between the domains of categorization and automaticity. Models of multidimensional perceptual classification have grown increasingly powerful and sophisticated in recent years, providing detailed quantitative accounts of patterns of classifi-cation learning, transfer, and generalization (e.g., Anderson,

Soar is an architecture for a system that is intended to be capable of general intelligence. Chunking, a simple experience-based learning mechanism, is Soar's only learning mechanism. Chunking creates new items of information, called chunks, based on the results of problem-solving and stores them in the knowledge base. These chunks are accessed and used in appropriate later situations to avoid the problem-solving required to determine them. It is already well-established that chunking improves performance in Soar when viewed in terms of the subproblems required and the number of steps within a subproblem. However, despite the reduction in number of steps, sometimes there may be a severe degradation in the total run time. This problem arises due to expensive chunks, i.e., chunks that require a large amount of effort in accessing them from the knowledge base. They pose a major problem for Soar, since in their presence, no guarantees can be given about Soar's performance.

Abstract. A cognitive model is a set of production rules or skills encoded in intelligent tutors to model how students solve problems. It is usually generated by brainstorming and iterative refinement between subject experts, cognitive scientists and programmers. In this paper we propose a semi-automated method for improving a cognitive model called Learning Factors Analysis that combines a statistical model, human expertise and a combinatorial search. We use this method to evaluate an existing cognitive model and to generate and evaluate alternative models. We present improved cognitive models and make suggestions for improving the intelligent tutor based on those models. 1

3 experiments were designed to demonstrate that classifying new letter strings as grammatical (i.e., conforming to a set of rules called a synthetic grammar) or ungrammatical may proceed from fragmentary conscious knowledge of the bigrams constituting the grammatical strings displayed in the study phase, rather than from an unconscious structured representation of the grammar, as Reber (1989) contended. In Experiment 1, grammaticality judgments of subjects initially studying grammatical letter strings did not differ from judgments by subjects learning from a list of the bigrams making up these strings. In Experiment 2, judgments about nongram-matical strings composed of valid bigrams placed in invalid locations were extremely poor, although better than chance. In Experiment 3 the explicit knowledge of bigrams as assessed by a recognition procedure appeared sufficient to account for observed performance on a standard test of grammaticality. A widely held model of cognition endows human subjects with the ability to implicitly abstract the regularities or high-level rules embodied in richly structured stimulus domains. Over the last 20 years, this general model has received strong

Cognitive skills acquisition is acquiring the ability to solve problems in intellectual tasks, where success is determined more by the subjects' knowledge than their physical prowess. This chapter reviews reseach conducted in the last ten years on cognitive skill acquisition. It covers the initial stages of acquiring a single principle or rule, the initial stages of acquiring a collection of interacting pieces of knowledge, and the final stages of acquiring a skill, wherein practice causes increases speed and accuracy.