Picturing Bivariate Separable-Features for Univariate Vector Magnitudes in Large-Magnitude-Range Quantum Physics Data
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2019-05-06
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Henan Zhao and Jian Chen; Picturing Bivariate Separable-Features for Univariate Vector Magnitudes in Large-Magnitude-Range Quantum Physics Data 2019; Cite as:arXiv:1905.02586v1 [cs.GR]
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Abstract
We present study results from two experiments to empirically validate that separable bivariate pairs for univariate
representations of large-magnitude-range vectors are more efficient than integral pairs. The first experiment with 20 participants
compared: one integral pair, three separable pairs, and one redundant pair, which is a mix of the integral and separable features.
Participants performed three local tasks requiring reading numerical values, estimating ratio, and comparing two points. The second 18-participant study compared three separable pairs using three global tasks when participants must look at the entire field to get an answer: find a specific target in 20 seconds, find the maximum magnitude in 20 seconds, and estimate the total number of vector exponents within 2 seconds. Our results also reveal the following: separable pairs led to the most accurate answers and the shortest task execution time, while integral dimensions were among the least accurate; it achieved high performance only when a pop-out separable feature (here color) was added. To reconcile this finding with the existing literature, our second experiment suggests that the higher the separability, the higher the accuracy; the reason is probably that the emergent global scene created by the separable pairs reduces the subsequent search space.