September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Modeling contextual flexibility in visual communication
Author Affiliations
  • Judith Fan
    Department of Psychology, Stanford University
  • Robert Hawkins
    Department of Psychology, Stanford University
  • Mike Wu
    Department of Computer Science, Stanford University
  • Noah Goodman
    Department of Psychology, Stanford UniversityDepartment of Computer Science, Stanford University
Journal of Vision September 2018, Vol.18, 1045. doi:10.1167/18.10.1045
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      Judith Fan, Robert Hawkins, Mike Wu, Noah Goodman; Modeling contextual flexibility in visual communication. Journal of Vision 2018;18(10):1045. doi: 10.1167/18.10.1045.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

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. Communicative drawing poses a core challenge for theories of how vision and social cognition interact, requiring a detailed understanding of how sensory information and social context jointly determine what information is relevant to communicate. Participants (N=192) 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 — the target — so that the viewer could pick it out from a set of distractor objects. There were two types of trials: close, where objects belonged to the same category, and far, where objects belonged to different categories. We found that people exploited information in common ground with their partner to efficiently communicate about the target: on far trials, sketchers achieved 99.7% recognition accuracy while applying fewer strokes, using less ink, and spending less time (ps< 0.001) on their drawings than on close trials. We hypothesized that humans excel at this task by recruiting two core competencies: (1) visual abstraction, the capacity to perceive the correspondence between an object and a drawing of it; and (2) social reasoning, the ability to infer what information would help a viewer distinguish the target from distractors. We instantiated these competencies in a computational model of communicative drawing that combines a multimodal convnet visual encoder with a Bayesian model of recursive social reasoning, and found that it fit the data well and outperformed lesioned variants of the model. Together, this work provides the first unified computational theory of how perception and social cognition support contextual flexibility in real-time visual communication.

Meeting abstract presented at VSS 2018

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