Discreteness and systematicity in a continuous signal-meaning space

Setup

First, make sure you have the necessary packages.

For python:

conda env create -f environment.yml
conda activate color-sounds
pip install -e .

For R, open the project in RStudio and run renv::restore().

Analysis pipeline

To reproduce all calculations:

make clean
make

NOTE: this takes multiple hours. The scripts that take a long time are 02_pairwise_dists.py, which computes the pairwise distances between all combinations of the 1989 signals across all participants, mds_dims.py which calculates embeddings for 1 to 7 dimensions.

Finally, run stats_and_plots.Rmd to generate the plots and results of the analyses reported in the paper.

Other files

Tools

Contains helper functions for analyses. Not all of these are used.

TODO: clean up

Data

Raw data is in raw_data. See codebook.

Processed data is in outputs. See codebook.

Figures

The figure outputs of stats_and_plots.Rmd are saved in figs. For the Illustrator-edited figures in the paper, see 0_figs_combined.pdf.

Other

• extra/plot_embeddings.R: Plots 2D MDS embeddings for each game. Creates one plot (faceted by participant) per game. The colors of the points correspond to the referent, and the shapes correspond to cluster membership. Communication score, Hopkins statistic, systematicity, and partner alignment are displayed on each facet.
  • This file also contains code to filter and search example yellow signals that have migrated, for visualizing in the paper.
• plot_signals.py: Generates .svg plots for learning and example communication signals.

R session info

sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 14.4.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base

other attached packages:
 [1] jsonlite_1.8.0  car_3.1-0       carData_3.0-5   broom_1.0.5     tidyboot_0.1.1  forcats_0.5.1   stringr_1.4.0   dplyr_1.0.9     purrr_0.3.4
[10] readr_2.1.2     tidyr_1.2.0     tibble_3.1.7    ggplot2_3.4.4   tidyverse_1.3.1 here_1.0.1      emmeans_1.8.4-1 lmerTest_3.1-3  lme4_1.1-29
[19] Matrix_1.4-1

loaded via a namespace (and not attached):
 [1] httr_1.4.3          viridisLite_0.4.0   bit64_4.0.5         vroom_1.5.7         splines_4.2.0       modelr_0.1.8        assertthat_0.2.1
 [8] renv_1.0.7          cellranger_1.1.0    yaml_2.3.5          numDeriv_2016.8-1.1 pillar_1.7.0        backports_1.4.1     lattice_0.20-45
[15] glue_1.6.2          rvest_1.0.2         minqa_1.2.4         colorspace_2.0-3    pkgconfig_2.0.3     haven_2.5.0         mvtnorm_1.1-3
[22] scales_1.2.0        tzdb_0.3.0          mgcv_1.8-40         generics_0.1.2      farver_2.1.0        ellipsis_0.3.2      withr_2.5.0
[29] pbkrtest_0.5.1      cli_3.6.0           magrittr_2.0.3      crayon_1.5.1        readxl_1.4.0        estimability_1.4.1  fs_1.5.2
[36] fansi_1.0.3         nlme_3.1-157        MASS_7.3-56         xml2_1.3.3          tools_4.2.0         hms_1.1.1           lifecycle_1.0.3
[43] munsell_0.5.0       reprex_2.0.1        compiler_4.2.0      rlang_1.1.2         grid_4.2.0          nloptr_2.0.2        rstudioapi_0.13
[50] labeling_0.4.2      boot_1.3-28         gtable_0.3.0        abind_1.4-5         DBI_1.1.2           R6_2.5.1            lubridate_1.8.0
[57] knitr_1.45          bit_4.0.4           utf8_1.2.2          rprojroot_2.0.3     stringi_1.7.6       parallel_4.2.0      Rcpp_1.0.8.3
[64] vctrs_0.5.2         dbplyr_2.1.1        tidyselect_1.1.2    xfun_0.42