  Graphical perception studies typically measure visualization encoding
effectiveness using the error of an "average observer", leading to canonical
rankings of encodings for numerical attributes: e.g., position > area > angle >
volume. Yet different people may vary in their ability to read different
visualization types, leading to variance in this ranking across individuals not
captured by population-level metrics using "average observer" models. One way
we can bridge this gap is by recasting classic visual perception tasks as tools
for assessing individual performance, in addition to overall visualization
performance. In this paper we replicate and extend Cleveland and McGill's
graphical comparison experiment using Bayesian multilevel regression, using
these models to explore individual differences in visualization skill from
multiple perspectives. The results from experiments and modeling indicate that
some people show patterns of accuracy that credibly deviate from the canonical
rankings of visualization effectiveness. We discuss implications of these
findings, such as a need for new ways to communicate visualization
effectiveness to designers, how patterns in individuals' responses may show
systematic biases and strategies in visualization judgment, and how recasting
classic visual perception tasks as tools for assessing individual performance
may offer new ways to quantify aspects of visualization literacy. Experiment
data, source code, and analysis scripts are available at the following
repository: https://osf.io/8ub7t/?view\_only=9be4798797404a4397be3c6fc2a68cc0.
