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,

This article is concerned with the issue of metaknowledge, that is, how people determine what they know about a question before they actually answer it. In particular, is it necessary to search memory in order to ascertain whether an answer is likely to be known? Although it might seem reasonable that people first search their memory for an answer before trying some other question-answering strategy, such as figuring out the answer, there is evidence that this is not the case (e.g., Portions of this work were initially presented at the Psychonomics Conference, Chicago, November 1988

People chose different strategies for performing tasks, and that choice often plays a key role in performance. We investigate the useand evolution of strategic behavior in the Kanfer-Ackerman Air Traffic Controller c fl task, a fast-paced, dynamic task. We present strategies in two dimensions for one aspect of the task, examine how people use them and switch between them, and how their use relates to final performance. We also discuss the implications that the observed variety of strategic behavior has for cognitive modeling. Introduction Problem-solving and learning in the real world often occur in dynamic situations. The teacher speaks at her own pace while the student understands her words, decides what's important, and takes notes. The apprentice watches the fire chief assess the course of a burning factory and direct teams of fire-fighters. The nine-year old learns to play a seemingly manic new videogame in an afternoon. Such domains have been studied in applied areas under the ...

Cooper and Shallice (1995) raise many issues regarding the unified theories of cognition research program in general, and Soar in particular. In this paper, we examine one specific criticism of Newell's (1990) treatment of immediate behavior and use it to explain the notion of the modeling idiom within a cognitive architecture. We compare a dual-task model using Newell's architecture and idiom to two other models that use different architectures and idioms (EPIC and an experimental version of Soar). We also look at the models' dependency on their respective cognitive architectures, and the theory/implementation gap also identified by Cooper and Shallice (1995). Introduction Unified theories of cognition seek to provide a unique, consistent framework for modeling all types of cognitive, perceptual, and motor activities. Several unified theories, or cognitive architectures, have been proposed: among them are Soar (Newell, 1990) , ACT-R (Anderson, 1993) and EPIC (Meyer & Kieras, 1995)....

this article we argue for a conclusion that has a similar spirit--- which is that the established results on sentence memory also follow from the ACT-R cognitive architecture (Anderson & Lebiere, 1998). ACTR bears similarity to SAM but is a more complete theory of cognition because it contains a model of cognitive control. As such we can directly embed in it a theory of sentence comprehension. Because of some of the architectural commitments of ACT-R, the theory of sentence comprehension is somewhat different than Kintsch's and closer to what is characterized as the minimalist hypothesis of sentence processing (McKoon & Ratcliff, 1992, 1995)

Category learning is often modeled as either an exemplar-based or a rule-based process. This paper shows that both strategies can be combined in a cognitive architecture that was developed to model other task domains. Variations on the EBRW exemplar model of Nosofsky and Palmeri (1997) and the RULEX rule-based model of Nososfky, Palmeri, and McKinley (1994) were implemented in the ACT-R cognitive architecture. The architecture allows the two strategies to be mixed to produce classification behavior. The combined system reproduces latency, learning, and generalization data from three category learning experiments—Nosofsky and Palmeri (1997), Nosofsky, Palmeri, and McKinley (1994), and Erickson and Krushke (1998). It is concluded that EBRW and ACT-R have different but equivalent means of incorporating similarity and practice. In addition, ACT-R brings a theory of strategy selection that enabled the exemplar and rule-based strategies to be mixed.

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