Viewers tend to underestimate correlation in positively correlated scatterplots. However, systematically changing the size and opacity of scatterplot points can bias estimates upwards, correcting for this underestimation. Here, we examine whether the application of these visualisation techniques goes beyond a simple perceptual effect and could actually influence beliefs about information from trusted news sources. We present a fully-reproducible study in which we demonstrate that scatterplot manipulations that are able to correct for the correlation underestimation bias can also induce stronger levels of belief change compared to conventional scatterplots presenting identical data. Consequently, we show that novel visualisation techniques can be used to drive belief change, and suggest future directions for extending this work with regards to altering attitudes and behaviours.

When visualising data, chart designers have the freedom to choose the upper and lower limits of numerical axes. Axis limits can determine the physical characteristics of plotted values, such as the physical position of data points in dot plots. In two experiments (total N=300), we demonstrate that axis limits affect viewers' interpretations of the magnitudes of plotted values. Participants did not simply associate values presented at higher vertical positions with greater magnitudes. Instead, participants considered the relative positions of data points within the axis limits. Data points were considered to represent larger values when they were closer to the end of the axis associated with greater values, even when they were presented at the bottom of a chart. This provides further evidence of framing effects in the display of data, and offers insight into the cognitive mechanisms involved in assessing magnitude in data visualisations.

Systematically changing the size and opacity of points on scatterplots can be used to induce more accurate perceptions of correlation by viewers. Evidence points to the mechanisms behind these effects being similar, so one may expect their combination to be additive regarding their effects on correlation estimation. We present a fully-reproducible study in which we combine techniques for influencing correlation perception to show that in reality, effects of changing point size and opacity interact in a non-additive fashion. We show that there is a great deal of scope for using visual features to change viewers’ perceptions of data visualizations. Additionally, we use our results to further interrogate the perceptual mechanisms at play when changing point size and opacity in scatterplots.

Data visualisation software provides the ability to create highly customisable choropleth maps.This presents an abundance of design choices.The colour legend, one particular aspect of choropleth map design, has the potential to effectively convey data points' absolute magnitudes (how large or small they are).Colour legends present the mapping between a specific range of colours and a specific range of numerical values.In this experiment, we demonstrate that manipulating this range affects interpretations of the plotted values' absolute magnitudes.Participants (N = 100) judged the urgency of addressing pollution levels as greater when the colour legend's upper bound was equal to the maximum plotted value, compared to when it was significantly larger than the maximum plotted value.This provides insight into the cognitive processing of plotted data in choropleth maps that are designed to promote inferences about overall magnitude.

Scatterplots are common data visualizations that can be used to communicate a range of ideas, the most intensively studied being the correlation between two variables. Despite their ubiquity, people typically do not perceive correlations between variables accurately from scatterplots, tending to underestimate the strength of the relationship displayed. Here we describe a two-experiment study in which we adjust the visual contrast of scatterplot points, and demonstrate a systematic approach to altering the bias. We find evidence that lowering the total visual contrast in a plot leads to increased bias in correlation estimates and show that decreasing the salience of points as a function of their distance from the regression line, by lowering their contrast, can facilitate more accurate correlation perception. We discuss the implications of these findings for visualization design, and provide a framework for online, reproducible, and large-sample-size (N = 150 per experiment) testing of the design parameters of data visualizations.