Towards a universal cognitive tool

Data visualization leverages human visual system to enhance cognition, it helps a person quickly and accurately see the trends, outliers, and patterns in data. Yet using visualization requires a viewer to read abstract imagery, estimate statistics, and retain information. These processes typically function differently for those with Intellectual and Developmental Disabilities (IDD) and have created an inaccessible barrier for them to access data. Preliminary findings from our graphical perception experiment suggest that people with IDD use different strategies to reason with data and are more sensitive to the design of data visualization compared with non-IDD populations. This article discusses several implications from that study and lays out actionable steps towards turning data visualization into a universal cognitive tool for people with varying cognitive abilities.

Evaluating 'Graphical Perception' with CNNs

Convolutional neural networks can successfully perform many computer vision tasks on images. For visualization, how do CNNs perform when applied to graphical perception tasks? We investigate this question by reproducing Cleveland and McGill's seminal 1984 experiments, which measured human perception efficiency of different visual encodings and defined elementary perceptual tasks for visualization. We measure the graphical perceptual capabilities of four network architectures on five different visualization tasks and compare to existing and new human performance baselines. While under limited circumstances CNNs are able to meet or outperform human task performance, we find that CNNs are not currently a good model for human graphical perception. We present the results of these experiments to foster the understanding of how CNNs succeed and fail when applied to data visualizations.

Elementary Graphical Perception for Older Adults: A Comparison with the General Population

We identified the graphical perceptual information needs of older adults (≥ 60 years of age) through a set of psychophysical experiments on bar, stacked, and pie charts. The results are compared with those of a general population (< 60 years of age). We conducted the experiments as online remote studies with 202 total participants across two experimental types: (1) comparison judgments of graphs (50 older adults, 50 general population) and (2) proportion judgments of graphs (52 older adults, 50 general population). Older adults took longer than the general population to complete tasks across both comparison (4.09 s) and proportion judgments (3.66 s). However, this translated to an approximately equal level of perceptual accuracy. Bar charts were the most effective graphical display when considering both speed and accuracy. Older adults were more accurate using pie charts compared with the general population in the comparison task.