  People's visual experiences of the world are easy to carve up and examine
along natural language boundaries, e.g., by category labels, attribute labels,
etc. However, it is more difficult to elicit detailed visuospatial information
about what a person attends to, e.g., the specific shape of a tree. Paying
attention to the shapes of things not only feeds into well defined tasks like
visual category learning, but it is also what enables us to differentiate
similarly named objects and to take on creative visual pursuits, like
poetically describing the shape of a thing, or finding shapes in the clouds or
stars. We use a new data collection method that elicits people's prioritized
attention to shapes during visual photo inspection by asking them to trace
important parts of the image under varying time constraints. Using data
collected via crowdsourcing over a set of 187 photographs, we examine changes
in patterns of visual attention across individuals, across image types, and
across time constraints.
