Perception and Cognitive Implications of Logarithmic Scales for Exponentially Increasing Data: Perceptual Sensitivity Tested with Statistical Lineups

This is the authors' abstract. We don't add key points for this paper.

Logarithmic transformations are a standard solution to displaying data that span several magnitudes within a single graph. This article investigates the impact of log scales on perceptual sensitivity through a visual inference experiment using statistical lineups. Our study evaluated participants’ ability to detect differences between exponentially increasing data, characterized by varying levels of curvature, using both linear and logarithmic scales. Participants were presented with a series of plots and asked to identify the panel that appeared most different from the others. Due to the choice of scale altering the contextual appearance of the data, the results revealed slight perceptual advantages for both scales depending on the curvatures of the compared data. This study serves as the initial part of a three-paper series dedicated to understanding the perceptual and cognitive implications of using logarithmic scales for visualizing exponentially increasing data. These studies serve as an example of multi-modal graphical testing, examining different levels of engagement and interaction with graphics to establish nuanced and specific guidelines for graphical design. Supplementary materials for this article are available online.