Revealing Perceptual Proxies with Adversarial Examples

Brian Ondov, Fumeng Yang, Matthew Kay, Niklas Elmqvist, Steven Franconeri

View presentation: 2020-10-27T19:15:00Z GMT-0600 Change your timezone on the schedule page
2020-10-27T19:15:00Z
Exemplar figure
We propose two approaches for uncovering how the visual system extracts statistics from a visualization, by pitting correct answers against adversarial models of candidate perceptual proxies. (A) In the “theory-driven” approach, we optimize charts to manipulate conjectured perceptual proxies, and test how powerfully they alter judgments. (B) In the “data-driven” approach, we seek to discover deceptive charts de novo, using human judgments as an objective function. The examples above present four real trials from the combined experiment. All annotations on bar charts are for illustrating purposes only.
Keywords

perceptual proxies, vision science, crowdsourced evaluation

Abstract

Data visualizations convert numbers into visual marks so that our visual system can extract data from an image instead of raw numbers. Clearly, the visual system does not compute these values as a computer would, e.g., by calculating an arithmetic mean or a correlation. Instead, it extracts these patterns using perceptual proxies; heuristic shortcuts of the visual marks, such as a center of mass or a shape envelope. Understanding which proxies people actually use would lead to more effective visualizations. We present the results of a series of crowdsourced experiments that measure how powerfully a set of candidate proxies can explain human performance when comparing the mean and range of pairs of data series presented as bar charts. We generated datasets where the correct answer—the series with the higher arithmetic mean or range—was pitted against an “adversarial” series that should be seen as higher if the viewer uses a particular candidate proxy. Using a staircase design, we sought metrics of how strongly each adversarial proxy could drive viewers to answer incorrectly, yielding evidence for whether that proxy is consistent with the viewer's actual practice. We then use hierarchical modeling to investigate whether different individuals may choose different proxies. Finally, we attempt to construct adversarial datasets from scratch, using an iterative crowdsourcing procedure to perform black-box optimization.