ANTASID: A NOVEL TEMPORAL ADJUSTMENT TO SHANNON’S INDEX OF DIFFICULTY FOR QUANTIFYING THE PERCEIVED DIFFICULTY OF UNCONTROLLED POINTING TASKS

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

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

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Shannon’s Index of Difficulty ( $ID$ ), reputable for quantifying the perceived difficulty of pointing tasks as a logarithmic relationship between movement-amplitude ( $A$ ) hbl5266ca and target-width ( $W$ ), is used for modeling 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 chiggate.com 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.

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