A probabilistic approach to modeling two-dimensional pointing

We investigate and model two-dimensional pointing where the target distance and size vary as does the angle of movement. We first study the spread of hits in a rapid approximate pointing task at varied distances and movement angles. Consistent with the literature, our results show that the spread of hits along the movement direction deviate more than the spread of hits in the direction perpendicular to movement, and both spreads increase with distance. Based on the distribution of this spread of hits, we propose and validate a new probabilistic model that describes two-dimensional pointing. Unlike previous models, our model accounts for more variables of two-dimensional pointing and can be generalized to any target shape, size, orientation, location, and dimension. In contrast to previous work, which suggests that target height has minimal impact on performance when it is larger than the width, our results show that, even when height is greater than width, it can significantly impact movement time.