In this paper we report a new approach to generating predictions about skilled interactive cognition. The approach, which we call Cognitive Constraint Modeling, takes as input a description of the constraints on a task environment, on user strategies, and on the human cognitive architecture and generates as output a prediction of the time course of interaction. In the Cognitive Constraint Models that we have built this is achieved by encoding the assumptions inherent in CPM-GOMS as a set of constraints and reasoning about them using finite domain constraint satisfaction.

This paper presents a summary of the space of commonlyused HCI prototyping methods (low-fidelity to highfidelity) and asserts that with a better understanding of this space, HCI practitioners will be better equipped to direct scarce prototyping resources toward an effort likely to yield specific results. It presents a set of five dimensions along which prototypes can be planned and characterized. The paper then describes an analysis of this space performed by members of the NASA Ames Human-Computer Interaction Group when considering prototyping approaches for a new set of tools for Mars mission planning and scheduling tools. A description is presented of a prototype that demonstrates design solutions that would have been particularly difficult to test given conventional low-or mid-fidelity prototyping methods. The prototype created was "mixed-fidelity," that is, high-fidelity on some dimensions and low-fidelity on others. The prototype is compared to a preexisting tool being redesigned and to a tool that has been developed using the prototype. Experimental data are presented that show the prototype to be a good predictor of eventual user performance with the final application. Given the relative cost of developing prototypes, it is critical to better characterize the space of fidelity in order to more precisely allocate design and development resources.

This paper presents X-PRT, a new cognitive modeling tool supporting activities ranging from interface design to basic cognitive research. X-PRT provides a graphical model development environment for the CORE constraint-based cognitive modeling engine [7,13,21]. X-PRT comprises a novel feature set: (a) it supports the automatic generation of predictive models at multiple skill levels from a single taskspecification, (b) it supports a comprehensive set of modeling activities, and (c) it supports compositional reuse of existing cognitive/perceptual/motor skills by transforming high-level, hierarchical task descriptions into detailed performance predictions. Task hierarchies play a central role in X-PRT, serving as the organizing construct for task knowledge, the locus for compositionality, and the cognitive structures over which the learning theory is predicated. Empirical evidence supports the role of task hierarchies in routine skill acquisition.

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