For scatterplots with gaussian distributions of dots, the perception of Pearson correlation r can be
described by two simple laws: a linear one for discrimination, and a logarithmic one for
perceived magnitude (Rensink & Baldridge, 2010). The underlying perceptual mechanisms,
however, remain poorly understood. To cast light on these, four different distributions of
datapoints were examined. The first had 100 points with equal variance in both dimensions.
Consistent with earlier results, just noticeable difference (JND) was a linear function of the distance away from r = 1, and the magnitude of perceived correlation a logarithmic function of this quantity. In addition, these laws were linked, with the intercept of the JND line being the inverse of the bias in perceived magnitude. Three other conditions were also examined: a dot cloud with 25 points, a horizontal compression of the cloud, and a cloud with a uniform distribution of dots. Performance was found to be similar in all conditions. The generality and form of these laws suggest that what underlies correlation perception is not a geometric structure such as the shape of the dot cloud, but the shape of the probability distribution of the dots, likely inferred via a form of ensemble coding. It is suggested that this reflects the ability of observers to perceive the information entropy in an image, with this quantity used as a proxy for Pearson