  To create effective data visualizations, it helps to represent data using
visual features in intuitive ways. When visualization designs match observer
expectations, visualizations are easier to interpret. Prior work suggests that
several factors influence such expectations. For example, the dark-is-more bias
leads observers to infer that darker colors map to larger quantities, and the
opaque-is-more bias leads them to infer that regions appearing more opaque
(given the background color) map to larger quantities. Previous work suggested
that the background color only plays a role if visualizations appear to vary in
opacity. The present study challenges this claim. We hypothesized that the
background color modulate inferred mappings for colormaps that should not
appear to vary in opacity (by previous measures) if the visualization appeared
to have a "hole" that revealed the background behind the map (hole hypothesis).
We found that spatial aspects of the map contributed to inferred mappings,
though the effects were inconsistent with the hole hypothesis. Our work raises
new questions about how spatial distributions of data influence color semantics
in colormap data visualizations.
