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

Abstract

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 (SDx) or bivariate (SDx,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 (SDx) is used in both, but that for 2-D pointing data, bivariate endpoint deviation (SDx,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 Fitts Study to facilitate comparisons.