Adaptive control of thought-rational (ACT-R; J. R. Anderson & C. Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explains how these modules are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT-R. These modules are associated with distinct cortical regions. These modules place chunks in buffers where they can be detected by a production system that responds to patterns of information in the buffers. At any point in time, a single production rule is selected to respond to the current pattern. Subsymbolic processes serve to guide the selection of rules to fire as well as the internal operations of some modules. Much of learning involves tuning of these subsymbolic processes. A number of simple and complex empirical examples are described to illustrate how these modules function singly and in concert.

This paper describes ACT-R/PM, an integrated theory of cognition, perception, and action which consists of the ACT-R production system and a set of perceptual-motor modules like those found in EPIC (Meyer & Kieras, 1997a, 1997b. Each module (including cognition) is essentially serial, but they run in parallel with one another. ACT-R/PM can model simple dual tasks such as the psychological refractory period (PRP), including subtle results previously explained with EPIC. The principal difference between the theories is that in EPIC, productions implementing central cognition can fire in parallel whereas in ACT-R/PM they fire serially. Three PRP-like experiments were run employing more demanding cognitive requirements, and indicated that cognitive processing for the two tasks did not overlap. ACT-R's activation-based retrieval processes are critical in accounting for the timing of these tasks and for explaining the dual-task performance decrement. integrated theories that seriously attempt to integrate cognition, perception, and action. This is surprising, since there are numerous domains to which such a theory might be applicable, such as mental workload, manual tracking, divided attention, time and motion analysis, paced tasks and time stress, resource-conflict matrices, some kinds of errors, the eye-hand span in typewriting, and many more. This is not to say there have been no theories concerned with such integration. The Model Human Processor (MHP) of Card, Moran, and Newell (1983) was originally presented as a summary of the state of the field's current knowledge about cognition and performance. The MHP, while never implemented as a computational model, specified the timing for cognition,

Systematic errors In performance are an important aspect of human behavior that have not received adequate explanation. One such systematic error is termed postcompletion error; a typical example is leaving one's card In the automatic teller after withdrawing cash. This type of error seems to occur when people have an extra step to perform in a procedure after the main goal has been satisfied. The fact that people frequently make this type of error, but do not make this error every time, may best be explained by considering the working memory load at the time the step is to be performed: The error is made when the load on working memory is high, but will not be made when the load is low. A model of performance In the task was constructed using Just and Carpenter's (1992) CAPS that predicted that high working memory load should be associated with postcompletion errors. Two experiments confirmed that such errors can be produced in a laboratory as well as a naturalistic setting, and that the conditions under which the CAPS model makes the error are consistent with the conditions under which the errors occur in the laboratory.

Understanding the interaction of a user with a designed device such as a GUI requires clear understanding of three components: the cognitive, perceptual and motor capabilities of the user, the task to be accomplished and the artefact used to accomplish the task. Computational modeling systems which enable serious consideration of all these constraints have only recently begun to emerge. One such system is ACT-R/PM, which is described in detail. ACT-R/PM is a production system architecture that has been augmented with a set of perceptual-motor modules designed to enable the detailed modeling of interactive tasks. Nilsen's (1991) random menu selection task serves two goals: to illustrate the promise of this system and to help further our understanding of the processes underlying menu selection and visual search. Nilsen's original study, two earlier models of the task, and recent eye-tracking data are all considered. Drawing from the best properties of the previous models considered and guided by information from the eye-tracking experiment, a series of new models of random menu selection were constructed using ACT-R/PM. The "nal model provides a zero-parameter "t to the data that does an excellent, though not perfect, job of capturing the data.2001 Academic Press ¹he Psychology of Human}Computer Interaction (Card, Moran & Newell, 1983) is often credited with the creation of the "eld of human}computer interaction and is, at the very least, one of its most central early in#uences. This book introduced the Model Human Processor (MHP) as an engineering model of human performance and goals, operators, methods and selection rules (GOMS) as a method of task analysis. The conceptual basis for GOMS and, at heart, the underlying belief about the best way to #esh out the MHP, is production rule systems. Since that time, the dominant production rule systems have been the ACT family of systems (Anderson, 1983(Anderson, , 1993) and the Soar architecture (Newell, 1990). EPIC (Kieras & Meyer, 1997) is a more recent, but promising and in#uential, entry into this arena. The applicability and success of GOMS and its MHP-inspired extension, CPM-GOMS (see John & Kieras, 1996, for a review), has clearly indicated that this is a fruitful approach for desktop-style user interfaces. However, the future of the human}computer interface is not on the desktop. Increasingly, computers with user interfaces are appearing in tasks where they previously have not been present, such as automobile navigation systems. These new applications provide signi"cant challenges, both practical and theoretical, to traditional analyses. These interfaces are increasingly

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