The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots
| Autor: | Danielle Albers Szafir, Jessica K. Witt, Matt-Heun Hong |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Lightness Computer science business.industry media_common.quotation_subject Computer Science - Human-Computer Interaction Illusion Centroid Computer Graphics and Computer-Aided Design Human-Computer Interaction (cs.HC) Data modeling Correlation Vision science Data visualization Dimension (vector space) Signal Processing Statistics Computer Vision and Pattern Recognition business Software media_common |
| Zdroj: | IEEE Transactions on Visualization and Computer Graphics. 28:987-997 |
| ISSN: | 2160-9306 1077-2626 |
| Popis: | Scatterplots can encode a third dimension by using additional channels like size or color (e.g. bubble charts). We explore a potential misinterpretation of trivariate scatterplots, which we call the weighted average illusion, where locations of larger and darker points are given more weight toward x- and y-mean estimates. This systematic bias is sensitive to a designer's choice of size or lightness ranges mapped onto the data. In this paper, we quantify this bias against varying size/lightness ranges and data correlations. We discuss possible explanations for its cause by measuring attention given to individual data points using a vision science technique called the centroid method. Our work illustrates how ensemble processing mechanisms and mental shortcuts can significantly distort visual summaries of data, and can lead to misconceptions like the demonstrated weighted average illusion. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|