Zhao Fang, Gaschler Robert
Center of Advanced Technology for Assisted Learning and Predictive Analytics, FernUniversität in Hagen, Hagen, Germany.
Department of Psychology, FernUniversität in Hagen, Hagen, Germany.
Front Psychol. 2022 Dec 19;13:991420. doi: 10.3389/fpsyg.2022.991420. eCollection 2022.
Different graph types may differ in their suitability to support group comparisons, due to the underlying graph schemas. This study examined whether graph schemas are based on perceptual features (i.e., each graph type, e.g., bar or line graph, has its own graph schema) or common invariant structures (i.e., graph types share common schemas). Furthermore, it was of interest which graph type (bar, line, or pie) is optimal for comparing discrete groups. A switching paradigm was used in three experiments. Two graph types were examined at a time (Experiment 1: bar vs. line, Experiment 2: bar vs. pie, Experiment 3: line vs. pie). On each trial, participants received a data graph presenting the data from three groups and were to determine the numerical difference of group A and group B displayed in the graph. We scrutinized whether switching the type of graph from one trial to the next prolonged RTs. The slowing of RTs in switch trials in comparison to trials with only one graph type can indicate to what extent the graph schemas differ. As switch costs were observed in all pairings of graph types, none of the different pairs of graph types tested seems to fully share a common schema. Interestingly, there was tentative evidence for differences in switch costs among different pairings of graph types. Smaller switch costs in Experiment 1 suggested that the graph schemas of bar and line graphs overlap more strongly than those of bar graphs and pie graphs or line graphs and pie graphs. This implies that results were not in line with completely distinct schemas for different graph types either. Taken together, the pattern of results is consistent with a hierarchical view according to which a graph schema consists of parts shared for different graphs and parts that are specific for each graph type. Apart from investigating graph schemas, the study provided evidence for performance differences among graph types. We found that bar graphs yielded the fastest group comparisons compared to line graphs and pie graphs, suggesting that they are the most suitable when used to compare discrete groups.
由于底层的图形模式，不同的图形类型在支持组间比较的适用性上可能存在差异。本研究考察了图形模式是基于感知特征（即每种图形类型，如柱状图或折线图，都有其自己的图形模式）还是基于共同的不变结构（即图形类型共享共同的模式）。此外，哪种图形类型（柱状图、折线图或饼图）最适合比较离散组也很值得研究。在三个实验中使用了切换范式。每次考察两种图形类型（实验1：柱状图与折线图，实验2：柱状图与饼图，实验3：折线图与饼图）。在每次试验中，参与者会收到一个呈现三组数据的数据图，并要确定图中显示的A组和B组的数值差异。我们仔细研究了从一次试验到下一次试验切换图形类型是否会延长反应时间。与仅有一种图形类型的试验相比，切换试验中反应时间的延长可以表明图形模式的差异程度。由于在所有图形类型的配对中都观察到了切换成本，所测试的不同图形类型对似乎都没有完全共享一个共同的模式。有趣的是，有初步证据表明不同图形类型对之间的切换成本存在差异。实验1中较小的切换成本表明，柱状图和折线图的图形模式比柱状图和饼图或折线图和饼图的图形模式重叠得更强。这意味着结果也不符合不同图形类型具有完全不同模式的情况。综上所述，结果模式与一种层次观点一致，即图形模式由不同图形共享的部分和每种图形类型特有的部分组成。除了研究图形模式外，该研究还提供了不同图形类型之间性能差异的证据。我们发现，与折线图和饼图相比，柱状图进行组间比较的速度最快，这表明在用于比较离散组时，柱状图是最合适的。
