With the rise of Large Language Models (LLMs), the novel metric "Brainscore" emerged as a means to evaluate the functional similarity between LLMs and human brain/neural systems. Our efforts were dedicated to mining the meaning of the novel score by constructing topological features derived from both human fMRI data involving 190 subjects, and 39 LLMs plus their untrained counterparts. Subsequently, we trained 36 Linear Regression Models and conducted thorough statistical analyses to discern reliable and valid features from our constructed ones. Our findings reveal distinctive feature combinations conducive to interpreting existing brainscores across various brain regions of interest (ROIs) and hemispheres, thereby significantly contributing to advancing interpretable machine learning (iML) studies. The study is enriched by our further discussions and analyses concerning existing brainscores. To our knowledge, this study represents the first attempt to comprehend the novel metric brainscore within this interdisciplinary domain.

翻译：随着大型语言模型（LLMs）的兴起，新型指标“脑评分”应运而生，用于评估LLMs与人脑/神经系统的功能相似性。本研究通过构建基于人类fMRI数据（涉及190名受试者）及39个LLMs（含其未训练版本）的拓扑特征，深入挖掘这一新型评分的语义内涵。随后，我们训练了36个线性回归模型并开展系统性统计分析，以甄别所构建特征中可靠且有效的部分。研究发现，在多个感兴趣脑区（ROIs）及大脑半球中，存在解释现有脑评分的独特特征组合，这为推进可解释机器学习（iML）研究提供了重要支撑。通过对现有脑评分的进一步讨论与分析，本研究的学术价值得以深化。据我们所知，这是在该交叉学科领域首次尝试理解新型指标“脑评分”的探索性研究。