Evaluating the Viability of Additive Models to Predict Task Completion Time for 3D Interactions in Augmented Reality

TL;DR

This paper investigates the use of additive models, like the Keystroke-Level Model, to predict task completion times in 3D augmented reality interactions, demonstrating their potential accuracy across different input modalities.

Contribution

It introduces a KLM-style additive model tailored for 3D interactions and evaluates its predictive accuracy through two empirical studies.

Findings

Models predicted task times with less than 20% error

Additive models can accurately predict relative performance

Feasibility shown for multiple input modalities

Abstract

Additive models of interaction performance, such as the Keystroke-Level Model (KLM), are tools that allow designers to compare and optimize the performance of user interfaces by summing the predicted times for the atomic components of a specific interaction to predict the total time it would take to complete that interaction. There has been extensive work in creating such additive models for 2D interfaces, but this approach has rarely been explored for 3D user interfaces. We propose a KLM-style additive model, based on existing atomic task models in the literature, to predict task completion time for 3D interaction tasks. We performed two studies to evaluate the feasibility of this approach across multiple input modalities, with one study using a simple menu selection task and the other a more complex manipulation task. We found that several of the models from the literature predicted actual task performance with less than 20% error in both the menu selection and manipulation study. Overall, we found that additive models can predict both absolute and relative performance of input modalities with reasonable accuracy.