Fitts' law (1954) characterizes pointing speed-accuracy performance as throughput, whose invariance to target distances (A) and sizes (W) is known. However, it is unknown whether throughput and Fitts' law models in general are invariant to task dimensionality (1-D vs. 2-D), whether univariate (SD x ) or bivariate (SD x,y ) endpoint deviation is used, whether throughput is calculated using the mean-of-means approach or the slope-inverse approach, or whether Guiard's (2009) Form × Scale experiment design is used instead of fully crossed A×W factors. We empirically investigate the confluence of these issues, finding that Fitts' law is largely invariant across 1-D and 2-D, provided that univariate endpoint deviation (SD x ) is used in both, but that for 2-D pointing data, bivariate endpoint deviation (SD x,y ) results in better Fitts' law models. Also, the mean-of-means throughput calculation exhibits lower variance across subjects and dimensionalities than the slope-inverse calculation. In light of these and other findings, we offer recommendations for pointing evaluations, especially in 2-D. We also offer an evaluation tool called FittsStudy to facilitate comparisons.
Recently, Wobbrock et al. (2008) derived a predictive model of pointing accuracy to complement Fitts' law's predictive model of pointing speed. However, their model was based on one-dimensional (1-D) horizontal movement, while applications of such a model require two dimensions (2-D). In this paper, the pointing error model is investigated for 2-D pointing in a study of 21 participants performing a time-matching task on the ISO 9241-9 ring-of-circles layout. Results show that the pointing error model holds well in 2-D. If univariate endpoint deviation (SD x ) is used, regressing on N=72 observed vs. predicted error rate points yields R 2 =.953. If bivariate endpoint deviation (SD x,y ) is used, regression yields R 2 =.936. For both univariate and bivariate models, the magnitudes of observed and predicted error rates are comparable.
The challenge of mobile text entry is exacerbated as mobile devices are used in a number of situations and with a number of hand postures. We introduce ContextType, an adaptive text entry system that leverages information about a user's hand posture (using two thumbs, the left thumb, the right thumb, or the index finger) to improve mobile touch screen text entry. ContextType switches between various keyboard models based on hand posture inference while typing. ContextType combines the user's posture-specific touch pattern information with a language model to classify the user's touch events as pressed keys. To create our models, we collected usage patterns from 16 participants in each of the four postures. In a subsequent study with the same 16 participants comparing ContextType to a control condition, ContextType reduced total text entry error rate by 20.6%.
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