Structural Similarities Between Language Models and Neural Response Measurements

This is the code to replicate the experiments described in the paper (to appear in NeurReps@NeurIPS, 2023):

Jiaang Li*, Antonia Karamolegkou*, Yova Kementchedjhieva, Mostafa Abdou, Sune Lehmann, and Anders Søgaard. Structural Similarities Between Language Models and Neural Response Measurements. In NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations 2023.

Installation

You can clone this repository issuing:
git clone [email protected]:coastalcph/brainlm.git

1. Create a fresh conda environment and install all dependencies.

conda create -n brain2lang python=3.11
conda activate brain2lang
pip3 install torch torchvision torchaudio
pip install -r requirements.txt

fMRI datasets

Dataset Name	Participants	Language	Format	Total n of words
Harry Potter	8	English	book chapter	1405
Natural Stories	19	English	Natural story stimuli	5228

How to run

See available model configurations in config.py under MODEL_CONFIGS and available saving paths of datasets, runtime parameters, and projection method in config.py under RunConfig.

Example to sequentially run BERT-Tiny and BERT-Mini models utilizing the Procrustes Analysis method on the Harry Potter dataset:

python main.py \
    --multirun \
    models=bert-tiny,bert-mini \
    datasets=hp_fmri \
    projection_method=procrustes

How to Cite

If you find our code or ideas useful in your research, please consider citing the paper:

@inproceedings{
li2023structural,
title={Structural Similarities Between Language Models and Neural Response Measurements},
author={Jiaang Li and Antonia Karamolegkou and Yova Kementchedjhieva and Mostafa Abdou and Sune Lehmann and Anders S{\o}gaard},
booktitle={NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations},
year={2023},
url={https://openreview.net/forum?id=ZobkKCTaiY}
}

Acknowledgement

Our codebase heavily relies on these excellent repositories:

• MUSE
• brain_language_nlp
• transformers