This repository contains the code from [1]. Refer to the paper for a detailed explanation of the model. If you make use of this code, please cite as follows:
López, F.M., Raabe, M.C., Shi, B.E., and Triesch, J. (2024) Self-Calibrating Saccade-Vergence Interactions. In 2023 IEEE International Conference on Development and Learning (ICDL).
The model was built and tested on Python 3.10. We recommend using Miniconda to create an environment with the required libraries.
First, fork the repository into your own github account. Open a terminal in your computer, navigate to a work forlder, and clone the repository:
git clone "URL_OF_YOUR_FORKED_REPOSITORY
Navigate to the newly created folder and create a virtual environment with the required libraries:
conda create -n saccade-vergence-interactions --file requirements.txt python=3.10
Activate the virtual environment:
source activate saccade-vergence-interactions
Finally, install the MIMo platform [2]:
pip install -e MIMo
You should now be able to run the code. If you encounter any problems during installation, please open an issue.
Before starting an experiment, make sure the code is installed correctly by running the embodimient. You can do by executing the following:
python src/embodiment.py
You can also visualize the embodiment with the following:
python src/embodiment.py --animate
To run a full experiment, execute the following:
python src/main.py --folder_name=test --n_epochs=10000 --save_every=1000
[1] López, F.M., Raabe, M.C., Shi, B.E., and Triesch, J. (2024) Self-Calibrating Saccade-Vergence Interactions. In 2023 IEEE International Conference on Development and Learning (ICDL).
[2] Mattern, D., Schumacher, P., López, F. M., Raabe, M. C., Ernst, M. R., Aubret, A., & Triesch, J. (2024). MIMo: A Multi-Modal Infant Model for Studying Cognitive Development. IEEE Transactions on Cognitive and Developmental Systems.
This project is licensed under the MIT License – see the LICENSE file for details.