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.

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(G) Expression pattern of genes associated with group I and II loci (from Fig. 2E) within differentially expressed gene sets. Significance calculated by prop.test R, (***) P<0.001. (H) Venn diagram shows overlap between SOX3 binding in NPCs and GPCs. Bar graph shows expression pattern of genes continuously bound by SOX3 NPCs and GPCs. (I) Venn diagram shows overlap between SOX3 and SOX9 binding in GPCs. Bar graph shows expression pattern of genes co-bound by SOX3 and SOX9 in GPCs. (J) ChIP-seq peak graphics around the astrocyte gene Fgfbp3. ChIP-seq peaks are derived from three different experiments; SOX3 ChIPs in NPCs, SOX3 ChIPs in GPCs, SOX9 ChIPs in GPCs. Both ChIP-seq reads and called peak regions (underlying black lines) are shown for all data sets. Bar graphs shows the distribution of differentially expressed genes that are bound by all three factors. P-values (phyper, R) were calculated from the total number of protein coding genes in mm10 assembly (23´389). List of tagged entities: multiple components, Fgfbp3 (ncbigene:72514), Sox3 (uniprot:P53784), Sox9 (uniprot:Q04887), , ChIP assay (obi:OBI_0001954),ChIP-seq assay (obi:OBI_0000716),gene expression assay (bao:BAO_0002785)

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