Ensemble of Task-Specific Language Models for Brain Encoding
This work incrementally improves brain encoding models for neuroscience applications.
The authors tackled the problem of predicting brain fMRI activations using language models by creating an ensemble of 10 task-specific models, resulting in a 10% average improvement over baselines across all regions of interest.
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.