  It has long been hypothesized that perceptual ambiguities play an important
role in aesthetic experience: a work with some ambiguity engages a viewer more
than one that does not. However, current frameworks for testing this theory are
limited by the availability of stimuli and data collection methods. This paper
presents an approach to measuring the perceptual ambiguity of a collection of
images. Crowdworkers are asked to describe image content, after different
viewing durations. Experiments are performed using images created with
Generative Adversarial Networks, using the Artbreeder website. We show that
text processing of viewer responses can provide a fine-grained way to measure
and describe image ambiguities.
