An error model for pointing based on Fitts’ law

For decades, Fitts’ law (1954) has been used to model pointing time in user interfaces. But as with any rapid motor act, faster movement times come at the cost of increased errors. 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 where 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 R2 = .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.