An important visual cue for the distance to a target is its binocular depth, the disparity between the left and right eyes. We examined mid-air pointing on a large screen, varying the physical distances (depths) to targets. Welford's two-part formulation provided a better model than the one-part Fitts's Law formulation to predict movement time from movement amplitude and target width. Angular measures suggested by Kopper et al. did not improve the model. Consistent variations of Shoemaker et al.'s k-factor suggest target depth plays a role similar to gain for mid-air pointing. We compared both physical and virtual targets to determine if artificial binocular depth cues induce the same performance as purely physical binocular depth cues. Variation of the k-factor was different when virtual depth and physical depth were not identical. This has implications for calibrating 3-D virtual environments and for the design of interactive 3-D pointing techniques for those environments.

Rating interfaces are widely used on the Internet to elicit people's opinions. Little is known, however, about the effectiveness of these interfaces and their design space is relatively unexplored. We provide a taxonomy for the design space by identifying two axes: Measurement Scale for absolute rating vs. relative ranking, and Recall Support for the amount of information provided about previously recorded opinions. We present an exploration of the design space through iterative prototyping of three alternative interfaces and their evaluation. Among many findings, the study showed that users do take advantage of recall support in interfaces, preferring those that provide it. Moreover, we found that designing ranking systems is challenging; there may be a mismatch between a ranking interface that forces people to specify a total ordering for a set of items, and their mental model that some items are not directly comparable to each other.