ANTASID: A Novel Temporal Adjustment to Shannon's Index of Difficulty for Quantifying the Perceived Difficulty of Uncontrolled Pointing Tasks

TL;DR

This paper introduces ANTASID, a new temporal adjustment to Shannon's Index of Difficulty, improving the accuracy of difficulty measurement in uncontrolled pointing tasks by accounting for speed variations.

Contribution

The paper proposes ANTASID, a novel temporal adjustment to Shannon's ID, validated through regression analysis on multiple datasets, enhancing difficulty quantification in real-world tasks.

Findings

ANTASID outperforms classical Shannon's ID in fit and throughput.

ANTASID reduces standard error in difficulty measurement.

Quantification of ID with ANTASID differs significantly from traditional methods.

Abstract

Shannon's Index of Difficulty ($ID$), reputable for quantifying the perceived difficulty of pointing tasks as a logarithmic relationship between movement-amplitude ($A$) and target-width ($W$), is used for modelling the corresponding observed movement-times ($MT_O$) in such tasks in controlled experimental setup. However, real-life pointing tasks are both spatially and temporally uncontrolled, being influenced by factors such as - human aspects, subjective behavior, the context of interaction, the inherent speed-accuracy trade-off where, emphasizing accuracy compromises speed of interaction and vice versa, and so on. Effective target-width ($W_e$) is considered as spatial adjustment for compensating accuracy. However, no significant adjustment exists in the literature for compensating speed in different contexts of interaction in these tasks. As a result, without any temporal adjustment, the true difficulty of an uncontrolled pointing task may be inaccurately quantified using Shannon's ID. To verify this, we propose the ANTASID (A Novel Temporal Adjustment to Shannon's ID) formulation with detailed performance analysis. We hypothesized a temporal adjustment factor ($t$) as a binary logarithm of $MT_O$, compensating for speed due to contextual differences and minimizing the non-linearity between movement-amplitude and target-width. Considering spatial and/or temporal adjustments to ID, we conducted regression analysis using our own and Benchmark datasets in both controlled and uncontrolled scenarios of pointing tasks with a generic mouse.ANTASID formulation showed significantly superior fitness values and throughput in all the scenarios while reducing the standard error. Furthermore, the quantification of ID with ANTASID varied significantly compared to the classical formulations of Shannon's ID, validating the purpose of this study.