The Challenges in Modeling Human Performance in 3D Space with Fitts' Law

TL;DR

This paper reviews the challenges and current state of modeling human performance in 3D space using Fitts' law, highlighting the lack of a standardized, comprehensive metric for 3D interactions in virtual reality.

Contribution

It provides a comparative analysis of existing Fitts' law extensions for 3D, identifying key variables and challenges in developing a standardized 3D human performance model.

Findings

Most extensions assume specific settings, limiting generalizability.

A standardized 3D performance metric is still missing.

Important variables for 3D modeling are often disregarded.

Abstract

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.