Problem solvers often have multiple operators available to them but must select just one to apply. We present three experiments that demonstrate that solvers use at least two sources of information to make operator selections in the building sticks task (BST): information from their past history of using the operators and information from the current context of the problem. Specifically, problem solvers are more likely to use an operator the more successful it has been in the past and the closer it takes the current state to the goal state. These two effects, respectively, represent the learning and performance processes that influence solvers ’ operator selections. A computational model of BST problem solving, developed within the ACT-R theory (Anderson, 1993), provides the unifying framework in which both types of processes can be integrated to predict solvers ’ selection tendencies. � 1996 Academic Press, Inc. Most problems can be approached in multiple ways but solved by only a few. Problem solving can be viewed, then, as finding one of the few paths that leads from a problem’s initial state to its goal state through some space of possible intermediate states (Newell & Simon, 1972). In this framework,

not be interpreted as representing the official policies, either expressed or implied, of the ONR or the U.S. government. Keywords: ACT-R, cognitive arithmetic, Bayesian learning, activation spreading, dynamical systems, parameter analysis, power law, machine learning, hybrid systems. Cognitive arithmetic, the study of the mental representation of numbers and arithmetic facts and the processes that create, access and manipulate them, offers a unique window into human cognition. Unlike traditional Artificial Intelligence (AI) tasks, cognitive arithmetic is trivial for computers but requires years of formal training for humans to master. Understanding the basic assumptions of the human cognitive system which make such a simple and well-understood task so challenging might in turn help us understand how humans perform other, more complex tasks and engineer systems to emulate them. The wealth of psychological data on every aspect of human performance of arithmetic makes precise computational modeling of the detailed error

From Ebbinghaus onward psychology has seen an enormous amount of research invested in the study of learning and memory. This research has produced a steady stream of results and, with a few "mini-revolutions " along the way, a steady increase in our understanding of how knowledge is acquired, retained, retrieved, and utilized. Throughout this history there has been a concern with the relationship of this research to its obvious application to education. The first author has written two textbooks (Anderson, 1995a, 1995b) summarizing some of this research. In both textbooks he has made efforts to identify the implications of this research for education. However, he left both textbooks feeling very dissatisfied-- that the intricacy of research and theory on the psychological side was not showing through in the intricacy of educational application. One finds in psychology many claims of relevance of cognitive psychology research for education. However, these claims are loose and vague and contrast sharply with the crisp theory and results that exist in the field. To be able to rigorously understand what the implications are of cognitive psychology research one needs a rigorous theory that bridges the gap between the detail of the laboratory experiment and the scale of the educational enterprise. This chapter is based on the ACT-R theory

Several studies show that despite experience, many users with basic command knowledge do not progress to an efficient use of complex computer applications. These studies suggest that knowledge of tasks and knowledge of tools are insufficient to lead users to become efficient. To address this problem, we argue that users also need to learn strategies in the intermediate layers of knowledge lying between tasks and tools. These strategies are (a) efficient because they exploit specific powers of computers, (b) difficult to acquire because they are suggested by neither tasks nor tools, and (c) general in nature having wide applicability. The above characteristics are first demonstrated in the context of aggregation strategies that exploit the iterative power of computers. A cognitive analysis of a real-world task reveals that even though such aggregation strategies can have large effects on task time, errors, and on the quality of the final product, they are not often used by even experienced users. We identify other strategies beyond aggregation that can be efficient and useful across computer applications and show how they were used to develop a new approach to train-Suresh Bhavnani specializes in computational design and human–computer interaction with a research focus on the identification, acquisition, and performance of efficient strategies to use complex computer systems; he is assistant professor at the School of Information in the University of Michigan. Bonnie John is an engineer and psychologist researching methods for usable systems design, especially computational models of human performance; she is an associate

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To Dandelion Kaczmarczyk, who always reminded me about the most important things in life. Acknowledgments There are so many people who supported, encouraged and mentored me while I worked on this dissertation. Most important, I would like to thank my advisor, Risto Miikkulainen. First, for supervising this interdisciplinary research. Second, for teaching me so much about conducting rigorous research and expressing my results with confidence. Third, for being a nice person. I would also like to thank the other members of my committee. Andrew Bernat, for his excellent advice on many occasions; Anthony Petrosino, for directing me to important resources on cognition and learning; Raymond Mooney, for his perspective on machine learning; Bradley Love for his perspective from cognitive psychology; Lowell Bethel, for his encouragement, especially during my early years at UT. Many other people supported me and my work at critical junctures. I want to especially thank Marilla Svinicki for her support during my comprehensive exams, and when I was developing my human subject study. Also, Jim Bednar, for his technical advice on numerous occasions, and Elaine Rich for her understanding of the importance of teaching and learning. Many of the staff in

this paper.

or thisris1qF it isbetter topurH9 an option involving automaticseart (Ritter 1991). Theseprse1HR appear to have local minima andcerHWH7R have noisy evaluation functions, so we chose toexplor the use of Genetic Algor17&H (Davis &Ritter 1987).Other appr1H9W should also betrqR& such asgrHqqqQ descent. Overview of Example Problem Topr9RqH a basisfor testing the effectiveness of a GA in optimizing the fitting of a model, an example prmple must be used. Her. we use a cognitive model simulating a child putting together a puzzle. Empir.1q data was takenfre both childr7 and adults whoconstrH1NH the puzzle. This data was used as acomparN7O for the model simulation. Puzzle Const'uN The puzzle is apyr9&O consisting of five levels and a top. It is known as theTower of Nottingham puzzleor simply, theTower Task (Wood & Middleton, 1975). The puzzle isconstr1NRW one level at a time and each level is made up offour pieces. The bottom level isconstr1NF7 firs followed by the nextlar1RHH Typically, two

untersuchen die mentale Repräsentation von Zahlen und arithmetischen Fakten sowie die kognitiven Prozesse die diese generieren, abrufen und manipulieren. Das Spannungsfeld zwischen der scheinbar einfachen formalen Struktur dieses Aufgabenbereichs und den Schwierigkeiten, die Kinder bei seiner Bewältigung haben, stellt einen einzigartigen Zugang zum Studium kognitiver Prozesse dar. Der vorliegende

In this study, we examine the hypothesis of Ericsson and Kintsch’s (1995) Long-Term Working Memory (LTWM) theory, according to which, with practice, people can utilize long-term memory so efficiently that they can overcome interruptions virtually without any costs. Six subjects were recruited to perform algebraic problem solving tasks for a total of nine hours in three consecutive days. Color patch n-back tasks interrupted performance, after which there was a recall task for the previous equation. Subjects ’ performance in the primary task increased across the three days, reflecting a general learning effect. More importantly, the negative effects of interruptions on memory recall decreased faster than the general learning effect predicted. However, this effect was small and limited to the two first days of the experiment. We discuss alternative explanations to this short-lived effect of practice on interruption tolerance.