Spanning seven orders of magnitude: a challenge for

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Goal-directed cognition is often discussed in terms of specialized memory structures like the "goal stack." The goal-activation model presented here analyzes goal-directed cognition in terms of the general memory constructs of activation and associative priming. The model embodies three predictive constraints: (1) the interference level, which arises from residual memory for old goals; (1) the strengthening constraint, which makes predictions about time to encode a new goal; and (3) the priming constraint, which makes predictions about the role of cues in retrieving pending goals. These constraints are formulated algebraically and tested through simulation of latency and error data from the Tower of Hanoi, a means-ends puzzle that depends heavily on suspension and resumption of goals. Implications of the model for understanding intention superiority, postcompletion error, and effects of task interruption are discussed.

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We identify a problem with the process of research in the HCI community -- an overemphasis on "radical invention" at the price of achieving a common research focus. Without such a focus, it is difficult to build on previous work, to compare different interaction techniques objectively, and to make progress in developing theory. These problems at the research level have implications for practice, too; as

We present a case study that tracks usability problems predicted with six usability evaluation methods (Claims Analysis, Cognitive Walkthrough, GOMS, Heuristic Evaluation, User Action Notation, and simply reading the specification) through a development process. We assess the methods' predictive power by comparing the predictions to the results of user tests. We assess the methods' persuasive power by seeing how many problems led to changes in the implemented code. We assess design-change effectiveness by user testing the resulting new versions of the system. We conclude that predictive methods are not as effective as the HCI field would like and discuss directions for future research.

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Critical parameters are quantitative measures of performance that may be used to determine the overall ability of a design to serve its purpose. Although critical parameters figure in almost every field of design where there is a demand for progressive improvement, they do not appear to figure significantly in the design of interactive systems. As a result, systems are designed that are recognizably different from other systems but not necessarily better at doing the job intended. This paper discusses the role of critical parameters in design, and illustrates their lack of use in interactive system design by presenting a number of of examples drawn from the HCI literature. It identifies a consequent need for research to establish critical parameters for applications and to build models of the performance of designs against these parameters. Some ideas are presented on how critical parameters might be established for specific applications, and the paper concludes by summarising some of the benefits that might be gained from moving in this direction.

Constraints and dependencies among the elements of embodied cognition form patterns or microstrategies of interactive behavior. Hard constraints determine which microstrategies are possible. Soft constraints determine which of the possible microstrategies are most likely to be selected. When selection is non-deliberate or automatic the least effort microstrategy is chosen. In calculating the effort required to execute a microstrategy each of the three types of operations, memory retrieval, perception, and action, are given equal weight; that is, perceptual-motor activity does not have a privileged status with respect to memory. Soft constraints can work contrary to the designer's intentions by making the access of perfect knowledge in-the-world more effortful than the access of imperfect knowledge in-the-head. These implications of soft constraints are tested in two experiments. In experiment 1 we varied the perceptual-motor effort of accessing knowledge in-the-world as well as the effort of retrieving items from memory. In experiment 2 we replicated one of the experiment 1 conditions to collect eye movement data. The results suggest that milliseconds matter. Soft constraints lead to a reliance on knowledge in-the-head even when the absolute difference in perceptual-motor versus memory retrieval effort is small, and even when relying on memory leads to a higher error rate and lower performance. We discuss the implications of # Supplementary data associated with this article can be found, in the online version, at doi:10.1016/ j.cogsci.2003.12.001 ## An earlier, much simpler version of this report was presented as an eight-page conference paper at CHI2001. That paper is archived as Gray and Fu (2001).

Many theories of skill acquisition have had considerable success in addressing the fine details of learning in relatively simple tasks, but can they scale up to complex tasks that are more typical of human learning in the real world? Some theories argue for scalability by making the implicit assumption that complex tasks consist of many smaller parts, which are learned according to basic learning principles. Surprisingly, there has been rather sparse empirical testing of this crucial assumption. In this article, we examine this assumption directly by decomposing the learning in the Kanfer–Ackerman Air-Traffic Controller Task (Ackerman, 1988) from the learning at the global level all the way down to the learning at the keystroke level. First, we reanalyze the data from Ackerman (1988) and show that the learning in this complex task does indeed reflect the learning of smaller parts at the keystroke level. Second, in a follow-up eye-tracking experiment, we show that a large portion of the learning at the keystroke level reflects the learning even at a lower, i.e., attentional level. © 2001 Academic Press Over the past 2 decades there have appeared a number of theories of skill