We investigate bivariate pointing in light of the recent progress in the modeling of univariate pointing. Unlike previous studies, we focus on the effect of target shape (width and height ratio) on pointing performance, particularly when such a ratio is between 1 and 2. Results showed unequal impact of amplitude and directional constraints, with the former dominating the latter. Investigating models based on the notion of weighted Lp norm, we found that our empirical findings were best captured by an Euclidean model with one free weight. This model significantly outperforms the best model to date.

Today's graphical interactive systems largely depend upon pointing actions, i.e. entering an object and selecting it. In this paper we explore whether an alternate paradigm --- crossing boundaries --- may substitute or complement pointing as another fundamental interaction method. We describe an experiment in which we systematically evaluate two target-pointing tasks and four goal-crossing tasks, which differ by the direction of the movement variability constraint (collinear vs. orthogonal) and by the nature of the action (pointing vs. crossing, discrete vs. continuous). We found that participants' temporal performance in each of the six tasks was dependent on the index of difficulty formulated in the same way as in Fitts' law, but that the parameters differ by task. We also found that goal crossing completion time was shorter or no longer than pointing performance under the same index of difficulty. These regularities, as well as qualitative characterizations of crossing actions and their application in HCI, lay the foundation for designing crossing-based user interfaces

In a ten-session experiment, six participants practiced typing with an expanding rehearsal method on an optimized virtual keyboard. Based on a large amount of in-situ performance data, this paper reports the following findings. First, the Fitts-digraph movement efficiency model of virtual keyboards is revised. The format and parameters of Fitts' law used previously in virtual keyboards research were incorrect. Second, performance limit predictions of various layouts are calculated with the new model. Third, learning with expanding rehearsal intervals for maximum memory benefits is effective, but many improvements of the training algorithm used can be made in the future. Finally, increased visual load when typing previously practiced text did not significantly change users' performance at this stage of learning, but typing unpracticed text did have a performance effect, suggesting a certain degree of text specific learning when typing on virtual keyboards

Interaction tasks on a computer screen can technically be scaled to a much larger or much smaller sized input control area by adjusting the input device's control gain or the control-display (C-D) ratio. However, human performance as a function of movement scale is not a well concluded topic. This study introduces a new task paradigm to study the scale effect in the framework of the steering law. The results confirmed a U-shaped performance-scale function and rejected straight-line or no-effect hypotheses in the literature. We found a significant scale effect in path steering performance, although its impact was less than that of the steering law's index of difficulty. We analyzed the scale effects in two plausible causes: movement joints shift and motor precision limitation. The theoretical implications of the scale effects to the validity of the steering law, and the practical implications of input device size and zooming functions are discussed in the paper.

Choosing input devices for interactive systems that best suit users needs remains a challenge, especially consid- ering the increasing number of devices available. The choice often has to be made through empirical evalua- tions. The most frequently used evaluation task hitherto is target acquisition, a task that can be accurately modeled by Fitts law. However, todays use of computer input devices has gone beyond target acquisition alone. In particular, we often need to perform trajectory-based tasks, such as drawing, writing, and navigation. This paper illustrates how a recently discovered model, the steering law, can be applied as an evaluation paradigm complementary to Fitts law. We tested five commonly used computer input devices in two steering tasks, one linear and one circular. Results showed that subjects performance with the five devices could be generally classified into three groups in the following order: 1. the tablet and the mouse, 2. the trackpoint, 3. the touch- pad and the trackball. The steering law proved to hold for all five devices with greater than 0.98 correlation. The ability to generalize the experimental results and the limitations of the steering law are also discussed.

Article Free Access Share on Beyond Fitts' law: models for trajectory-based HCI tasks Authors: Johnny Accot Centre d'Études de la, Navigation Aérienne, 7 avenue Edouard Belin, 31055 Toulouse cedex, France and Input Research Group, CSRI, University of Toronto, Toronto, ON M5S 1A4, Canada Centre d'Études de la, Navigation Aérienne, 7 avenue Edouard Belin, 31055 Toulouse cedex, France and Input Research Group, CSRI, University of Toronto, Toronto, ON M5S 1A4, CanadaView Profile , Shumin Zhai Input Research Group, CSRI, University of Toronto, Toronto, ON M5S 1A4, Canada and IBM Almaden, Research Center, 650 Harry Road, San Jose, CA Input Research Group, CSRI, University of Toronto, Toronto, ON M5S 1A4, Canada and IBM Almaden, Research Center, 650 Harry Road, San Jose, CAView Profile Authors Info & Claims CHI '97: Proceedings of the ACM SIGCHI Conference on Human factors in computing systemsMarch 1997 Pages 295–302https://doi.org/10.1145/258549.258760Published:27 March 1997Publication History 328citation6,971DownloadsMetricsTotal Citations328Total Downloads6,971Last 12 Months602Last 6 weeks96 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF