five

Code and data for "An Active Inference Model of Trust Game Behavior"

收藏
DataCite Commons2026-04-14 更新2026-05-04 收录
下载链接:
https://open.uni-marburg.de/handle/openumr/9539
下载链接
链接失效反馈
官方服务:
资源简介:
This is a zipped version of the github reposiory and behavioral data for an Active Inference model of trust game behavior. It contains the software for simulation, model inversion, model comparison, classification and parameter/ prediction recovery. To use this code and reproduce any results or figures, follow the steps detailed in our readme file below. Please bear in mind that use is permitted under a CC BY-NC 4.0 license (see repo and below for details). # Code Repository for **Active Inference in Interpersonal Decision-Making** ## 📦 Installation This repository uses an **editable installation**. To reproduce all results and run the code, follow these steps: ### 1. Clone the repository ```bash git clone git@gitlab.uni-marburg.de:fb04/ag-endres/pymdp_depression.git ``` ### 2. Install in editable mode Navigate to the `src/` directory and run: ```bash pip install -e . ``` --- ## 🗂️ Repository Structure ``` pymdp_depression/ ├── pyproject.toml ├── README.txt ├── .gitignore ├── data/ ├── notebooks/ │ └── # Contains all plotting notebooks in order of appearance in the manuscript ├── src/ │ └── pymdp_depression/ │ ├── model/ │ ├── classification/ │ ├── model_comparison/ │ ├── simulations/ │ ├── optimization/ │ ├── recovery/ ├── archive/ ``` --- ## 🔈 Naming conventions The model variants have slightly different names in the paper vs. in the optimization algo for readability reasons: Paper name --> repo name --- 1) ABC --> fits_alpha16 2) ABCE --> with_E 3) ABCDE --> with_EandD 4) ABCDEα --> with_E_and_alpha 5) ABCDE_lrAlrB --> hyperparam_opt 6) ABCD_policy2 --> incr_policy_horizon --- ## 📊 Reproducibility All figures in the manuscript can be reproduced using the notebooks found in the `notebooks/` directory. * The notebooks are **numbered in the order they appear in the paper**. * Each notebook imports the necessary functions from the `model`, `simulations`, `optimization`, and `recovery` submodules. * All figures are generated directly from data stored under: ``` data/results/... ``` --- ## 📚 Citation Please cite our manuscript - once published, you will find the full citation on Google Scholar or here: https://eckertal.github.io/personal-website/publications/
提供机构:
Philipps-Universität Marburg
创建时间:
2026-04-14
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作