ANTASID: Cleaned Dataset of Uncontrolled Pointing Task Experiment

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. 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 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.

香农难度指数（ID）久负盛名，它将指向任务的感知难度量化为移动幅度（A）与目标宽度（W）之间的对数关系，用于在受控实验环境中对这类任务中观测到的相应移动时间（MT_O）进行建模。然而，现实场景中的指向任务在空间与时间上均不受控，受多种因素影响，例如人类因素、主观行为、交互情境以及固有的速度-精度权衡（speed-accuracy trade-off）——其中，强调精度会牺牲交互速度，反之亦然。有效目标宽度（W_e）被视为补偿精度的空间调整量。然而，现有文献中尚无针对不同交互情境下速度补偿的显著调整方法。因此，若缺乏时间调整，使用香农ID可能无法准确量化不受控指向任务的真实难度。为验证这一点，我们提出ANTASID（A Novel Temporal Adjustment to Shannon’s ID）公式，并开展详细的性能分析。我们假设时间调整因子（t）为MT_O的二进制对数，用于补偿因情境差异导致的速度变化，并最小化移动幅度与目标宽度之间的非线性关系。考虑对ID进行空间和/或时间调整，我们使用自主数据集与基准数据集（Benchmark datasets），在通用鼠标指向任务的受控与不受控场景中开展回归分析。ANTASID公式在所有场景中均表现出显著更优的拟合度与吞吐量，同时降低了标准误差。此外，与香农ID的经典公式相比，ANTASID对ID的量化结果存在显著差异，验证了本研究的目的。