  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.
