Evaluating machine comprehension of sketch meaning at different levels of abstraction

This repository contains code to reproduce the results in our CogSci 2023 paper, Evaluating machine comprehension of sketch meaning at different levels of abstraction.

Directory Structure

├── paper
├── analysis
├── experiments
│   ├── category-selfpaced  
│   ├── recognition
├── results
│    ├── plots
├── data

paper contains the LaTeX source code along with figures for our paper.

analysis contains 3 main files -

1. analysis_nb.ipynb is a jupyter notebook that contains code to reproduce the results we present in our submission with headers corresponding to the different sections in the paper.

2. linear_models.Rmd is an R markdown notebook that contains additional analyses, specifically the results of mixed-effects models.

3. sketch_models.yml specifies a conda environment with the appropriate packages to reproduce our code. We recommend creating a new conda environment using the following command:

   conda env create -f sketch_models.yml
   

experiments contains 2 subfolders - category-selfpaced contains code and materials for the human sketch production experiment recognition contains code and maetrials for the human sketch recognition experiment

results contains a subdirectory called plots which is where the jupyter notebooks will save plots that are generated.

data will need to contain intermediate outputs, which serve as input for the R markdown and jupyter notebooks. Please place the contents found inside the recog_exp_data folder here inside the data directory running any notebook cells. Refer to the README.md in the data directory for more details.