Revisiting Performance Models of Distal Pointing Tasks in Virtual Reality

TL;DR

This study evaluates and improves performance models for distal pointing in virtual reality, proposing a new methodology and identifying a simple Fitts-Law-style model as most effective.

Contribution

It introduces a novel data collection method and compares multiple models, finding that a simple Fitts-Law-style index best predicts VR distal pointing performance.

Findings

Simple Fitts-Law-style model outperforms others

Traditional models may be insufficient for VR distal pointing

New data collection methodology enhances model accuracy

Abstract

Performance models of interaction, such as Fitts Law, are important tools for predicting and explaining human motor performance and for designing high-performance user interfaces. Extensive prior work has proposed such models for the 3D interaction task of distal pointing, in which the user points their hand or a device at a distant target in order to select it. However, there is no consensus on how to compute the index of difficulty for distal pointing tasks. We present a preliminary study suggesting that existing models may not be sufficient to model distal pointing performance with current virtual reality technologies. Based on these results, we hypothesized that both the form of the model and the standard method for collecting empirical data for pointing tasks might need to change in order to achieve a more accurate and valid distal pointing model. In our main study, we used a new methodology to collect distal pointing data and evaluated traditional models, purely ballistic models, and two-part models. Ultimately, we found that the best model used a simple Fitts-Law-style index of difficulty with angular measures of amplitude and width.