  Visual modes of communication are ubiquitous in modern life --- from maps to
data plots to political cartoons. Here we investigate drawing, the most basic
form of visual communication. Participants were paired in an online environment
to play a drawing-based reference game. On each trial, both participants were
shown the same four objects, but in different locations. The sketcher's goal
was to draw one of these objects so that the viewer could select it from the
array. On `close' trials, objects belonged to the same basic-level category,
whereas on `far' trials objects belonged to different categories. We found that
people exploited shared information to efficiently communicate about the target
object: on far trials, sketchers achieved high recognition accuracy while
applying fewer strokes, using less ink, and spending less time on their
drawings than on close trials. We hypothesized that humans succeed in this task
by recruiting two core faculties: visual abstraction, the ability to perceive
the correspondence between an object and a drawing of it; and pragmatic
inference, the ability to judge what information would help a viewer
distinguish the target from distractors. To evaluate this hypothesis, we
developed a computational model of the sketcher that embodied both faculties,
instantiated as a deep convolutional neural network nested within a
probabilistic program. We found that this model fit human data well and
outperformed lesioned variants. Together, this work provides the first
algorithmically explicit theory of how visual perception and social cognition
jointly support contextual flexibility in visual communication.
