| Popis: |
Effective visualizations not only communicate information about individual datapoints, but also allow people to quickly extract summaries of the data. The visual perception literature has revealed 'ensemble' perception mechanisms that provide a way for people to extract statistical summaries, like the mean position of the datapoints, from visual displays like scatterplots. Prior work has focused on how people extract the mean of only a single, unified cluster of datapoints, and has not tested how accurately people extract summary statistics from more complex displays, and what strategies they use to do so. Scatterplots often contain clustered data, and identifying the global mean by aggregating across clusters can provide valuable insight for identifying trends, comparing groups, and guiding decision-making. The current studies systematically investigate how people make intuitive judgments about the global mean of two spatial clusters in scatterplots. We focus specifically on the case where people must aggregate over a larger main cluster and an 'outlier' cluster with relatively fewer points, and we vary both the number of items in each cluster and the dispersion of clusters. Our results suggest that people qualitatively differ in their perception of the mean, but all groups of participants reliably overweight the outlier group in their intuitive judgments of the mean of the datapoints. |
