What Aimed Movement Models Fit Distal Pointing With Varying Depth?

With the rapid improvements in drone technology, there is an increasing interest in distal pointing to diffuse drones. This study investigated the effect of depth on distal pointing when the hand does not traverse the entire distance from start to target so that the most suitable mathematical model can be assessed.

Starting from the Fitts paradigm, researchers have proposed different models to predict movement time when the distance to the target is variable. They do consider distance, but they are based on statistical modeling rather than the underlying control mechanisms.

Twenty-four participants volunteered for an experiment in a full-factorial Fitts' paradigm task (3 levels of movement amplitude *7 levels of target width *3 levels of distance from participant to screen). Movement time and the number of errors were the dependent variables.

Depth has a significant effect when the target width is small, but depth has no effect when the target width is large. The angular version of the two-part model is superior to the one-part Fitts' model at larger distances. Besides, Index of difficulty for distal pointing, $$ with adjustable k achieves the best fit even though the model is very sensitive to the value of k and the complexity of the model could be resulting in an overfitting. The result implies that the effects of movement amplitude and target width are not comparable and grouping them to form a dependent index of difficulty can be misleading especially when distance is an added variable.

The angular version of the two-part model is a viable and meaningful description for distal pointing. Even though the $$ with adjustable k is the best predictor for movement time when depth is an added variable, there is no physical interpretation for it.

A reasonable predictive model for performance assessments and predictions in distal pointing.