Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
Sorbonne Université, Paris, France.
Psychon Bull Rev. 2019 Apr;26(2):669-676. doi: 10.3758/s13423-018-1525-7.
Across science, education, and business, we process and communicate data visually. One bedrock finding in data visualization research is a hierarchy of precision for perceptual encodings of data (e.g., that encoding data with Cartesian positions allows more precise comparisons than encoding with sizes). But this hierarchy has only been tested for single-value comparisons, under the assumption that those lessons would extrapolate to multivalue comparisons. We show that when comparing averages across multiple data points, even for pairs of data points, these differences vanish. Viewers instead compare values using surprisingly primitive perceptual cues (e.g., the summed area of bars in a bar graph). These results highlight a critical need to study a broader constellation of visual cues that mediate the patterns that we can see in data, across visualization types and tasks.
在科学、教育和商业领域，我们都以可视化的方式处理和交流数据。数据可视化研究的一个基本发现是，数据的感知编码具有精度层次（例如，用笛卡尔坐标对数据进行编码比用大小进行编码允许更精确的比较）。但是，仅针对单值比较测试了这种层次结构，假设这些经验教训可以推断到多值比较。我们表明，即使在比较多个数据点的平均值时，即使对于两个数据点，这些差异也会消失。相反，观察者使用令人惊讶的原始感知线索来比较值（例如，条形图中条形的总和面积）。这些结果突出表明，我们需要研究更广泛的视觉线索组合，以研究跨可视化类型和任务的数据中我们可以看到的模式。
