  There is an intricate relation between the properties of an image and how
humans behave while describing the image. This behavior shows ample variation,
as manifested in human signals such as eye movements and when humans start to
describe the image. Despite the value of such signals of visuo-linguistic
variation, they are virtually disregarded in the training of current pretrained
models, which motivates further investigation. Using a corpus of Dutch image
descriptions with concurrently collected eye-tracking data, we explore the
nature of the variation in visuo-linguistic signals, and find that they
correlate with each other. Given this result, we hypothesize that variation
stems partly from the properties of the images, and explore whether image
representations encoded by pretrained vision encoders can capture such
variation. Our results indicate that pretrained models do so to a
weak-to-moderate degree, suggesting that the models lack biases about what
makes a stimulus complex for humans and what leads to variations in human
outputs.
