Normalizing Speed-accuracy Biases in 2D Pointing Tasks with Better Calculation of Effective Target Widths

TL;DR

This study compares univariate and bivariate methods for calculating effective target widths in 2D pointing tasks, finding the univariate approach yields more consistent and reliable results across different speed-accuracy biases.

Contribution

The paper demonstrates that using univariate standard deviation for effective target width calculation improves model accuracy and stability in 2D Fitts' law experiments across multiple bias conditions.

Findings

Univariate W_e yields higher model correlations.

Univariate W_e produces more stable throughput.

Results are consistent with different participant subsets.

Abstract

For evaluations of 2D target selection using Fitts' law, ISO 9241-411 recommends using the effective target width (W_e) calculated using the univariate standard deviation of selection coordinates. Related research proposed using a bivariate standard deviation; however, the proposal was only tested using a single speed-accuracy bias condition, thus the assessment was limited. We compared the univariate and bivariate techniques in a 2D Fitts' law experiment using three speed-accuracy biases and 346 crowdworkers. Calculating W_e using the univariate standard deviation yielded higher model correlations across all bias conditions and produced more stable throughput among the biases. The findings were also consistent in cases using randomly sampled subsets of the participant data. We recommend that future research should calculate W_e using the univariate standard deviation for fair performance evaluations. Also, we found trivial effects when using nominal or effective amplitude and using different perspectives of the task axis.