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，能够编码我们大脑中某些感兴趣区域的功能性磁共振成像激活模式。先前的研究探索了从为常见自然语言处理任务学到的表示中进行迁移学习，以预测大脑反应。在我们的工作中，我们通过基于10种流行语言模型（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。通过我们的集成方法，我们在所有感兴趣区域上的平均性能比当前基线提升了10%。