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Graphical conventions: learning from iterated visual communication

How do people agree on ways of communicating visual concepts?

Note: This repo reflects the state of the codebase as of CogSci 2019. To access the codebase used in the subsequent journal article, please refer to: https://github.com/hawkrobe/graphical_conventions.

workflow

  • Run human experiments

    • communication task (/experiments/refgame/draw_chairs/)
      • Input: Shapenet chair collection and experimental design
      • Output: Human sketches and viewer decisions over time, communication efficiency timecourse
    • recognition task (/experiments/recog/)
      • Input: Sketches from communication task and 3D objects
      • Output: Sketch recognizability in context (4 objects) w/o interaction history
  • Analyze human task performance data

    • /analysis/ipynb/golden/generate_refgame_dataframe.py
      • Input: raw mongo database records
      • Output: tidy formatted dataframes containing key behavioral variables (XX.csv, BIS.csv)
    • /analysis/rmd/analyze_refgame_dataframe.Rmd
      • Input: tidy dataframe generated by generate_refgame_dataframe.py
      • Output: timecourse visualizations of key behavioral variables; results of linear mixed effects modeling of timecourse
    • /analysis/ipynb/golden/generate_recog_dataframe.py
      • Input: raw mongo database records
      • Output: tidy formatted dataframes containing key behavioral variables (XX.csv, BIS.csv)
    • /analysis/ipynb/golden/analyze_recog_dataframe.py
      • Input: tidy dataframe generated by generate_recog_dataframe.py
      • Output: timecourse visualizations of key behavioral variables; results of linear mixed effects modeling of timecourse
  • Model-based analyses of sketch data

    • /analysis/golden/analyze_sketch_features.py
      • Input: sketch features generated by extract_sketch_features.py
      • Output: timecourse visualizations of key sketch feature variables
  • Write paper

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how do people agree on ways of communicating visual concepts?

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