Browsing by Subject "Data Visualization"

The pressing challenge of climate change and its uncertainties require effective communication to engage mitigation efforts. Data visualizations enable presenting complex data to layperson and professionals. People’s perception, however, seems to be affected by their motivations, and uncertainty in data. This study investigates the influence of prior beliefs and uncertainty representation on climate-aware people’s interpretation of climate data visualizations. In an online experiment, participants estimated the correlation of variables displayed in scatterplots. The plots were labelled either with meaningful or abstracted variables, and either included uncertainty representation or not. Participants also indicated how much they believed the meaningful variables to be correlated. When a correlation triggered their beliefs, participants estimated higher correlations than when they did not have beliefs about the displayed data. The representation of uncertainty alone did not influence the estimation performance. When participants had beliefs about a correlation and uncertainty was represented in the plot, participants’ estimation was higher than in the other conditions and the least accurate. The findings suggest that people’s interpretation was biased by their prior beliefs, especially in combination with uncertainty representation. This might be explained by prior beliefs guiding participant’s attention to features of the visualization supporting their views. This biased perception seems to affect their interpretation. Uncertainty representation might increase the bias by expanding the range of possible interpretations, potentially prompting people to rely on their prior beliefs more strongly.