  With the rapid growth in virtual reality technologies, object interaction is
becoming increasingly more immersive, elucidating human perception and leading
to promising directions towards evaluating human performance under different
settings. This spike in technological growth exponentially increased the need
for a human performance metric in 3D space. Fitts' law is perhaps the most
widely used human prediction model in HCI history attempting to capture human
movement in lower dimensions. Despite the collective effort towards deriving an
advanced extension of a 3D human performance model based on Fitts' law, a
standardized metric is still missing. Moreover, most of the extensions to date
assume or limit their findings to certain settings, effectively disregarding
important variables that are fundamental to 3D object interaction. In this
review, we investigate and analyze the most prominent extensions of Fitts' law
and compare their characteristics pinpointing to potentially important aspects
for deriving a higher-dimensional performance model. Lastly, we mention the
complexities, frontiers as well as potential challenges that may lay ahead.
