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People routinely rely on data to make decisions, but the process can be riddled with biases. We show that patterns in data might be noticed first or more strongly, depending on how the data is visually represented or what the viewer finds salient. We also demonstrate that viewer interpretation of data is similar to that of ‘ambiguous figures’ such that two people looking at the same data can come to different decisions. In our studies, participants read visualizations depicting competitions between two entities, where one has a historical lead (A) but the other has been gaining momentum (B) and predicted a winner, across two chart types and three annotation approaches. They either saw the historical lead as salient and predicted that A would win, or saw the increasing momentum as salient and predicted B to win. These results suggest that decisions can be influenced by both how data are presented and what patterns people find visually salien 

Language can affect cognition, but through what mechanism? Substantial past research has focused on how labeling can elicit categorical representation during online processing. We focus here on a particularly powerful type of language—relational language—and show that relational language can enhance relational representation in children through an embodied attention mechanism. Four-year-old children were given a color-location conjunction task, in which they were asked to encode a two-color square, split either vertically or horizontally (e.g., red on the left, blue on the right), and later recall the same configuration from its mirror reflection. During the encoding phase, children in the experimental condition heard relational language (e.g., “Red is on the left of blue”), while those in the control condition heard generic non-relational language (e.g., “Look at this one, look at it closely”). At recall, children in the experimental condition were more successful at choosing the correct relational representation between the two colors compared to the control group. Moreover, they exhibited different attention patterns as predicted by the attention shift account of relational representation (Franconeri et al., 2012). To test the sustained effect of language and the role of attention, during the second half of the study, the experimental condition was given generic non-relational language. There was a sustained advantage in the experimental condition for both behavioral accuracies and signature attention patterns. Overall, our findings suggest that relational language enhances relational representation by guiding learners’ attention, and this facilitative effect persists over time even in the absence of language. Implications for the mechanism of how relational language can enhance the learning of relational systems (e.g., mathematics, spatial cognition) by guiding attention will be discussed. 

Reading a visualization is like reading a paragraph. Each sentence is a comparison: the mean of these is higher than those; this difference is smaller than that. What determines which comparisons are made first? The viewer's goals and expertise matter, but the way that values are visually grouped together within the chart also impacts those comparisons. Research from psychology suggests that comparisons involve multiple steps. First, the viewer divides the visualization into a set of units. This might include a single bar or a grouped set of bars. Then the viewer selects and compares two of these units, perhaps noting that one pair of bars is longer than another. Viewers might take an additional third step and perform a second-order comparison, perhaps determining that the difference between one pair of bars is greater than the difference between another pair. We create a visual comparison taxonomy that allows us to develop and test a sequence of hypotheses about which comparisons people are more likely to make when reading a visualization. We find that people tend to compare two groups before comparing two individual bars and that second-order comparisons are rare. Visual cues like spatial proximity and color can influence which elements are grouped together and selected for comparison, with spatial proximity being a stronger grouping cue. Interestingly, once the viewer grouped together and compared a set of bars, regardless of whether the group is formed by spatial proximity or color similarity, they no longer consider other possible groupings in their comparisons. 

Data visualizations present a massive number of potential messages to an observer. One might notice that one group's average is larger than another's, or that a difference in values is smaller than a difference between two others, or any of a combinatorial explosion of other possibilities. The message that a viewer tends to notice - the message that a visualization ‘affords’ - is strongly affected by how values are arranged in a chart, e.g., how the values are colored or positioned. Although understanding the mapping between a chart's arrangement and what viewers tend to notice is critical for creating guidelines and recommendation systems, current empirical work is insufficient to lay out clear rules. We present a set of empirical evaluations of how different messages-including ranking, grouping, and part-to-whole relationships-are afforded by variations in ordering, partitioning, spacing, and coloring of values, within the ubiquitous case study of bar graphs. In doing so, we introduce a quantitative method that is easily scalable, reviewable, and replicable, laying groundwork for further investigation of the effects of arrangement on message affordances across other visualizations and tasks. Pre-registration and all supplemental materials are available at https://osf.io/np3q7 and https://osf.io/bvy95 , respectively.