Visual Features as Carriers of Information
The perception of Pearson correlation in scatterplots can be described via simple laws (Rensink & Baldridge, 2010): just noticeable difference (jnd) follows a Weber-like (linear) law, and subjective estimate follows a Fechner-like (logarithmic) law. Behavior is largely invariant to the size, shape or color of the dots (Rensink, 2014), suggesting that correlation is (or is related to) a perceptually simple property carried via spatial position. To determine if features other than spatial position can carry information in the same way, a set of "augmented stripplots" was developed for visualizing two-dimensional data. As for the case of scatterplots, the first dimension was carried by spatial position along the horizontal axis. But the second dimension was carried by a visual feature such as orientation, size, or color. Precision was measured via the jnd in correlation for two above-and-below stripplots, each 2° high x 5° wide. Accuracy was determined via reference plots with fixed upper and lower values, with a test plot adjusted to have its apparent correlation be midway between them. Twenty observers were tested in each condition. Results showed the same pattern in all conditions. For all features, precision followed a Weber-like law, with jnds increasing linearly with distance from r=1. And for all features, accuracy followed a Fechner-like law, with subjective estimates a logarithmic function of the distance from r=1. The constants of these functions were similar as well; indeed, performance for color-augmented stripplots (both red-green and blue-yellow) was somewhat more accurate than that for scatterplots. These commonalities provide further evidence that correlation perception is a general process that is both sophisticated and rapid, relying on more than superficial appearance alone (e.g., the shape of the dot cloud). They also suggest that many if not all basic visual features can effectively carry information. Meeting abstract presented at VSS 2015.