It has been well established in Cognitive Psychology that humans are able to strategically adapt performance, even highly skilled performance, to meet explicit task goals such as being accurate (rather than fast). This paper describes a new capability for generating multiple human performance predictions from a single task specification as a function of different performance objective functions. As a demonstration of this capability, the Cognitive Constraint Modeling approach was used to develop models for several tasks across two interfaces from the aviation domain. Performance objectives are explicitly declared as part of the model, and the CORE (Constraint-based Optimal Reasoning Engine) architecture itself formally derives the detailed strategies that are maximally adapted to these objectives. The models are analyzed for emergent strategic variation, comparing those optimized for task time with those optimized for working memory load. The approach has potential application in user interface and procedure design.

This paper presents a summary of the space of commonly-used HCI prototyping methods (low-fidelity to high-fidelity) 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 task-specification, (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.

A priori prediction of skilled human performance has the potential to be of great practical value but is difficult to carry out. This article reports on an approach that facilitates modeling of human behavior at the level of cognitive, perceptual, and motor operations, following the CPM-GOMS method (John, 1990). CPM-GOMS is a powerful modeling method that has remained underused because of the expertise and labor required. We describe a process for automatically generating CPM-GOMS models from a hierarchical task decomposition expressed in a computational modeling tool, taking advantage of reusable behavior templates and their efficacy for generating zero-parameter a priori predictions of complex human behavior. To demonstrate the process, we present a model of automated teller machine interaction. The model shows that it is possible to string together existing behavioral templates that compose basic HCI tasks, (e.g., mousing to a button and clicking on it) to generate powerful human performance predictions. Because interleaving of templates is now automated, it becomes possible to construct arbitrarily long sequences of behavior. In addition, the manipulation and adaptation of complete models has the potential of becoming dramatically easier. Thus, the tool described here provides an engine for CPM-GOMS that may facilitate computational modeling of human performance at the millisecond level.

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 describes the design and deployment of a collaborative software tool, designed for and presently in use on the Mars Exploration Rovers (MER) 2003 mission. Two central questions are addressed. Does collaborative content like that created on easels and whiteboards have persistent value? Can groups of people jointly manage collaboratively created content? Based on substantial quantitative and qualitative data collected during mission operations, it remains difficult to conclusively answer the first question while there is some positive support for the second question. The MER mission provides a uniquely rich data set on the use of collaborative tools.

CPM-GOMS is a modeling method that combines the task decomposition of a GOMS analysis with a model of human resource usage at the level of cognitive, perceptual, and motor operations. CPM-GOMS models have made accurate predictions about skilled user behavior in routine tasks, but developing such models is tedious and error-prone. We describe a process for automatically generating CPM-GOMS models from a hierarchical task decomposition expressed in a cognitive modeling tool called Apex. Resource scheduling in Apex automates the difficult task of interleaving the cognitive, perceptual, and motor resources underlying common task operators (e.g. mouse move-and-click). Apex’s UI automatically generates PERT charts, which allow modelers to visualize a model’s complex parallel behavior. Because interleaving and visualization is now automated, it is feasible to construct arbitrarily long sequences of behavior. To demonstrate the process, we present a model of automated teller interactions in Apex and discuss implications for user modeling.

This paper describes de results of a study evaluating the effects of computer mediation on collaboratively solving architectural design problems.Pairs of graduate design students were asked to work on a landscape architecture design problem via computer terminals.In one condition they were allowed to communicate with an electronic whiteboard and a chat-line while in the other, the chat-line was substituted with videoconferencing (real-time video and audio).The protocols were evaluated according to two models.First, they were coded according to the pattern of collaboration, distinguishing meta-planning, negotiation; evaluation, and individual work No differences were found between the two groups when coded this way.The protocols were also coded in terms of the problem-solving content, distinguishing task-related exchanges, interface related exchanges, low-level design exchanges, and highlevel design exchanges.The results showed that in the bandwidth-limited chat-line condition, participants cut down task and interface-related as well as low-level design exchanges but attempted to maintain the same amount of high-level design exchanges.When de final designs wene evaluated by professional architects, no differences were found between two conditions indicating that chat-line participants implicitly compensate for the narrower bandwidth interface.

We present an analysis of an expert performing a highly interactive computer task. The analysis uses GOMS models, specifying the Goals, Operators, Methods, and Selection rules used by the expert. Two models are presented, one with function-level operators which perform high-level functions in the domain, and one with keystroke-level operators which describe hand movements. For a segment of behaviour in which the expert accomplished about 30 functions in about 30 s, the function-level model predicted the observed behaviour well, while the keystroke-level model predicted only about half of the observed hand movements. These results, including the discrepancy between the models, are discussed.

A GOMS analysis was used to predict the behavior of an expert in a graphic, machine-paced, highly interactive task. The analysis was implemented in a computational model using the Soar cognitive architecture. Using only the information available in an instruction booklet and some simple heuristics for selecting between operators, the functional-level behavior of the expert proved to be virtually dictated by the objects visible on the display. At the keystroke-level, the analysis predicted about 60% of the behavior, in keeping with similar results in previous GOMS research. We conclude that GOMS is capable of predicting expert behavior in a broader range of tasks than previously demonstrated.