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,

To account for the large demands of working memory during text comprehension and expert problem solving it is proposed that the traditional models of working memory involving temporary storage have to be extended to include a long-term working-memory portion. According to the proposed theoretical framework cognitive processes are viewed as a sequence of stable states representing end products of processing. In skilled activities these end products are stored in long-term memory and kept directly accessible by retrieval cues in short-term memory as proposed by skilled memory theory. These

Although virtually any human activity can be viewed as the solving of a problem, throughout the hstory of the study of problem solving, most research has concerned tasks that take minutes or hours to per-form. Typically subjects make many observable actions during this period, and these actions are interpreted as the externally visible part of the solution process. Even if subjects are required to solve problems in their heads (for example, to mentally multiply 135 x 76), they are usually asked to talk aloud as they work, and the resulting verbal protocol is interpreted as a sequence of actions (see chapter 1). Thus the tasks studied are not only long tasks but also multistep tasks. The earliest experimental work on human problem solving was done

The relationship between theoretically-grounded hints, strategy selection, and solution performance in the Toads and Frogs puzzle, a well-structured problem in which weak methods are difficult to apply, is investigated through an experiment and an ACT-R simulation. The main results show that providing a state specific hint is useful in speeding up the adoption of a hybrid solution strategy, comprising both the retrieval of previous moves and the proceduralization of a domain-specific heuristic that avoids any kind of forward search. The implications of the results for the problem solving theory are discussed.

Recent studies on a computationally hard visual optimization problem, the Traveling Salesperson Problem (TSP), indicate that humans are capable of finding close to optimal solutions in near-linear time. The current study is a preliminary step in investigating human performance on another hard problem, the Minimum Vertex Cover Problem, in which solvers attempt to find a smallest set of vertices that ensures that every edge in an undirected graph is incident with at least one of the selected vertices. We identify appropriate measures of performance, examine features of problem instances that impact performance, and describe strategies typically employed by participants to solve instances of the Vertex Cover problem.