perceptual bias – vialab | Dr. Christopher Collins

Saliency Deficit and Motion Outlier Detection in Animated Scatterplots

Contributors:

We report the results of a crowdsourced experiment that measured the accuracy of motion outlier detection in multivariate, animated scatterplots. The targets were outliers either in speed or direction of motion and were presented with varying levels of saliency in dimensions that are irrelevant to the task of motion outlier detection (e.g., colour, size, position). We found that participants had trouble finding the outlier when it lacked irrelevant salient features and that visual channels contribute unevenly to the odds of an outlier being correctly detected. Direction of motion contributes the most to the accurate detection of speed outliers, and position contributes the most to accurate detection of direction outliers. We introduce the concept of saliency deficit in which item importance in the data space is not reflected in the visualization due to a lack of saliency. We conclude that motion outlier detection is not well supported in multivariate animated scatterplots.

This research was given an honourable mention at CHI 2019.

Materials used to conduct this research are available for download here.

Publications

Acknowledgements

Perceptual Biases in Font Size as a Data Encoding

Contributors:

Eric Alexander, Chih-Ching Chang, Mariana Shimabukuro, Steven Franconeri, Christopher Collins, and Michael Gleicher

Many visualizations, including word clouds, cartographic labels, and word trees, encode data within the sizes of fonts. While font size can be an intuitive dimension for the viewer, using it as an encoding can introduce factors that may bias the perception of the underlying values. Viewers might conflate the size of a word’s font with a word’s length, the number of letters it contains, or with the larger or smaller heights of particular characters (‘o’ vs. ‘p’ vs. ‘b’). We present a collection of empirical studies showing that such factors-which are irrelevant to the encoded values-can indeed influence comparative judgements of font size, though less than conventional wisdom might suggest. We highlight the largest potential biases and describe a strategy to mitigate them.