[Under Submission] Internal World Models as Imagination Networks in Cognitive Agents
Note: The releavant files are provided with notes with them below after "<-".
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├── data <- NOT ON GITHUB. GET THIS FOLDER FROM OSF AND UNZIP.
├── docs
│ ├── docs
│ ├── mkdocs.yml
│ └── README.md
├── env_conda_imaginenet.yml <- Python conda environment file for the project.
├── imagine <- Local project library for utility.
│ ├── __init__.py
│ ├── __pycache__
│ ├── config.py <- provides access project related directory paths.
│ ├── funpair_corr_analysis.py <- functions for centrality correlational analysis.
│ └── sim_data.py <- functions to tabulate AI data and perform popuation diversity sampling for simulations.
├── Makefile
├── models <- Network analysis files.
│ ├── 1_datasets.R <- Sets up variables and loads data in R workspace and random seed. Always run this first to run other network analysis. Change the Working directory to use.
│ ├── 2_networkimg_spearman_cstab.R <- Estimates the networks, stores the centrality and CS coefficeint values.
│ ├── 3_clusters_net.R <- Estimates network clusters and stores them.
│ ├── clustering_img <- RProject folder for clustering analysis.
│ ├── networkimg_ebg <- RProject folder for network estimates analysis.
│ └── r_project_network_and_cluster.zip <- Zip file of the clustering_img and networkimg_ebg and Available on OSF.
├── notebooks
│ ├── network_analysis_centrality_corr.ipynb <- Perform centrality correlations and plot heatmaps with multiple comparisons.
│ ├── network_analysis_cscoeff_and_clustering.ipynb <- View CS coefficent from centrality estimate, view clusters data and calculate and plot clustering alignment using ARI (adjusted rand score).
│ ├── population_diversity_sampling_merge_human.ipynb <- Create population divesity sampling dataset for AI simulations and combine data for network analysis.
│ ├── total_vivdness_score_analysis.ipynb <- analyze total vividness score using KS test between groups.
│ ├── view_centrality_corr_data.ipynb <- view correlations csv with formatting.
│ └── wrangle_data_ai.ipynb <- Clean the AI data for use.
├── pyproject.toml
├── README.md
├── reports
│ ├── figures <- contains figures in svg format.
│ └── tables.pptx