Language models have been shown to be rich enough to encode fMRI activations of certain Regions of Interest in our Brains. Previous works have explored transfer learning from representations learned for popular natural language processing tasks for predicting brain responses. In our work, we improve the performance of such encoders by creating an ensemble model out of 10 popular Language Models (2 syntactic and 8 semantic). We beat the current baselines by 10% on average across all ROIs through our ensembling methods.
@article{arxiv.2310.15720,
title = {Ensemble of Task-Specific Language Models for Brain Encoding},
author = {Arvindh Arun and Jerrin John and Sanjai Kumaran},
journal= {arXiv preprint arXiv:2310.15720},
year = {2023}
}