A Cognitive Simulator for Tutoring Causal Relation between Mental Operations and Behavior

Problem solving is understood as a process through which states of problem solving are transferred from the initial state to the goal state by applying adequate operators. Within this framework, knowledge and strategies are given as operators for the search. One of the most important points of researchers’ interest in the domain of problem solving is to explain the performance of problem solving behavior based on the knowledge and strategies that the problem solver has. We call the interplay between problem solvers’ knowledge/strategies and their behavior the causal relation between mental operations and behavior. It is crucially important, we believe, for novice learners in this domain to understand the causal relation between mental operations and behavior. Based on this insight, we have constructed a learning system in which learners can control mental operations of a computational agent that solves a task, such as knowledge, heuristics, and cognitive capacity, and can observe its behavior. We also introduce this system to a university class, and discuss which findings were discovered by the participants. Introduction In the traditional theory of human problem solving, human behavior in solving a problem has been regarded as a search for a problem space. In this perspective, problem solving is understood as a process through which states of problem solving are transferred from the initial state to the goal state by applying adequate operators. The utility of this theoretical framework, especially in the early stages of problem solving studies, was supported by many empirical studies in which simple puzzle-like experimental tasks were used. Following after these studies, this view was expanded into a more general framework, and has taken a central role in providing fundamental principles for explaining complex human cognition. For example, there are some representative studies such as: a series of Klahr’s studies where scientific discovery is regarded as a search for dual problem spaces: rule and data spaces (Klahr & Dunbar, 1988, 2000, Kulkarni & Simon, 1988); studies on insight problem solving where insight is brought about by switching a problem space searched (Kaplan & Simon, 1990); studies on diagrammatic problem solving where the superiority of using diagrams has been very often analyzed from the viewpoint of the efficiency of the problem space search (Larkin & Simon, 1987); and studies on distributed cognition, where the efficiency of distributed cognition is explained based on the merging of multiple problem spaces represented in the internal and external world (Zhang & Norman, 1994). Within this framework, knowledge and strategies are given as operators for the search. One of the most important points of researchers’ interest in the domain of problem solving is to explain the performance of problem solving behavior based on the knowledge and strategies that the problem solver has. In the production system, the most popular computer architecture used in studies on human problem solving, operators are represented as production rules (e.g., Klahr, Langley, & Neches, 1987). There have been many trials in which the difference of rules implemented in a cognitive model tries to explain a variety of human problem solving behavior. In one landmark book, Human Problem Solving, published in 1972, Newell and Simon indicated that complex human behavior can be generated by repeated applications of a relatively small amount of simple operators, and a wide variety of behavior can be successfully explained based on the addition and elimination of a very small number of specific operators (Newell & Simon, 1972). These findings strongly influenced following studies on human problem solving. In this paper, we call the interplay between problem solvers’ knowledge/strategies and their behavior the causal relation between mental operations and behavior. Many introductory textbooks on cognitive science and cognitive psychology introduce the theoretical framework of problem solving mentioned above. It is crucially important, we believe, for novice learners in this domain to understand the causal relation between mental operations and behavior. Based on this insight, we have constructed a learning system in which learners can control mental operations of a computational agent that solves a task, such as knowledge, heuristics, and cognitive capacity, and can observe its behavior. The users are expected to notice many aspects of the causal relation between mental operations and behavior while simulating the human problem solving process using our system. We also