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Pointing is a fundamental task within many interactions in current computer applications. It is incorporated into everything from selecting buttons to dragging files or positioning objects in a virtual environment. Thus, understanding, modeling and predicting pointing performance is crucial to the design and evaluation of many computer interfaces. Fitts’s Law (1954) is the basis for modeling human pointing performance in the international standard on pointing device evaluation (ISO 9241-400:2007). However, while it is extremely robust for many standard desktop applications, previous work by Shoemaker et al. (2012) has suggested that Fitts’s Law may not be robust enough to accurately model pointing at more extreme levels of gain and has proposed alternatives to Fitts’s Law based on earlier work by Welford (1968). This thesis extends preliminary research by Rajendran (2012) that further examined these alternatives to Fitts’s Law for distal pointing. Distal pointing is common in virtual and augmented reality interfaces. We first reänalyze results reported by Rajendran using a variety of Welford-style models to explore the relationship between target depth and a parameter k that was first suggested by Kopper et al. (2010) but is inherent in Welford’s model. We then present a new experiment that removes the confound of system latency from Rajendran’s approach. Our analyses provide evidence that k varies monotonically (possibly linearly) with target depth, which further supports the claim by Shoemaker et al. that Welford-style two-part models are preferable to Fitts-style one-part models in some situations. Our analyses also challenge Kopper et al.’s suggestion that angular measures of task difficulty are superior to linear measures for pointing models. We close with a discussion of how our findings about the variation of k with target depth might be used in calibration procedures for virtual environments.

Modeling distal pointing on large screens : the influence of target depth