An error model for pointing based on Fitts' law

Abstract

For decades, Fitts' law (1954) has been used to model pointing time in user interfaces. As with any rapid motor act, faster pointing movements result in increased errors. But although prior work has examined accuracy as the "spread of hits," no work has formulated a predictive model for error rates (0-100%) based on Fitts' law parameters. We show that Fitts' law mathematically implies a predictive error rate model, which we derive. We then describe an experiment in which target size, target distance, and movement time are manipulated. Our results show a strong model fit: a regression analysis of observed vs. predicted error rates yields a correlation of R 2 = .959 for N = 90 points. Furthermore, we show that the effect on error rate of target size (W) is greater than that of target distance (A), indicating a departure from Fitts' law, which maintains that W and A contribute proportionally to index of difficulty (ID). Our error model can be used with Fitts' law to estimate and predict error rates along with speeds, providing a framework for unifying this dichotomy.

The effects of task dimensionality, endpoint deviation, throughput calculation, and experiment design on pointing measures and models

“…c. d. Figure 6. The FittsStudy tool offers (a) zoomable path visualizations and path analysis measures [17], (b) graphical submovement profiles including velocity (shown), acceleration, and jerk over time, and (c) Fitts' law models (shown) and pointing error models [28] using both endpoint deviation schemes and throughput calculation approaches [26,30]. (d) An example project by students using FittsStudy shows an eye-tracking "heat map" overlaying FittsStudy's ring-of-circles task.…”

“…A spatial outlier was defined from prior work [19,28] as an error whose movement was less than half the nominal distance A, or whose endpoint landed more than twice the target width W from the target center. In all, 9/7560 and 2/7560 test trials were removed from the 1-D and 2-D tasks, respectively.…”

“…FittsStudy logs all pointing trials in a session in XML, from which it builds spreadsheets for statistical analysis. Although Fitts' law evaluation tools have been described in the literature [5,22,25], FittsStudy offers more features than previous tools, providing, among other things, trial browsing and zooming tools, submovement profile graphs, results from both endpoint deviation and throughput calculations, path analysis measures [17], a metronome for time-matching temporal-precision studies (e.g., [23]), and models of movement times and error rates [28,29]. Future work in FittsStudy includes offering not just the vertical ribbon and ring-of-circles tasks, but also random target fields (e.g., [6]), and crossing and steering tasks [1].…”

Intelligent Interfaces to Empower People with Disabilities

“…Empirical studies to evaluate quantitatively assistive software that supports or alters mouse-pointer movement on the screen typically measure the time it takes a user to issue a command ("target acquisition") and describe the communication bandwidth using Fitts' law [94,112,3]. Qualitative evaluation methods, including interviews with users and caregivers, are also beneficial [3].…”

Modeling and predicting pointing errors in two dimensions

“…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).…”