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Visual modes of communication are ubiquitous in modern life --- from maps todata plots to political cartoons. Here we investigate drawing, the most basicform of visual communication. Participants were paired in an online environmentto play a drawing-based reference game. On each trial, both participants wereshown the same four objects, but in different locations. The sketcher's goalwas to draw one of these objects so that the viewer could select it from thearray. On `close' trials, objects belonged to the same basic-level category,whereas on `far' trials objects belonged to different categories. We found thatpeople exploited shared information to efficiently communicate about the targetobject: on far trials, sketchers achieved high recognition accuracy whileapplying fewer strokes, using less ink, and spending less time on theirdrawings than on close trials. We hypothesized that humans succeed in this taskby recruiting two core faculties: visual abstraction, the ability to perceivethe correspondence between an object and a drawing of it; and pragmaticinference, the ability to judge what information would help a viewerdistinguish the target from distractors. To evaluate this hypothesis, wedeveloped a computational model of the sketcher that embodied both faculties,instantiated as a deep convolutional neural network nested within aprobabilistic program. We found that this model fit human data well andoutperformed lesioned variants. Together, this work provides the firstalgorithmically explicit theory of how visual perception and social cognitionjointly support contextual flexibility in visual communication.

Pragmatic inference and visual abstraction enable contextual flexibility during visual communication