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.

翻译：语言模型已被证明足够丰富，能够编码大脑中特定兴趣区域的fMRI激活信号。以往研究探索了从为常见自然语言处理任务学习到的表征中迁移学习，以预测大脑反应。在本工作中，我们通过从10个主流语言模型（2个句法模型和8个语义模型）创建集成模型，提升了此类编码器的性能。通过集成方法，我们在所有兴趣区域上平均比现有基线提升了10%的性能。