Over the past decade, studies of naturalistic language processing where participants are scanned while listening to continuous text have flourished. Using word embeddings at first, then large language models, researchers have created encoding models to analyze the brain signals. Presenting these models with the same text as the participants allows to identify brain areas where there is a significant correlation between the functional magnetic resonance imaging (fMRI) time series and the ones predicted by the models' artificial neurons. One intriguing finding from these studies is that they have revealed highly symmetric bilateral activation patterns, somewhat at odds with the well-known left lateralization of language processing. Here, we report analyses of an fMRI dataset where we manipulate the complexity of large language models, testing 28 pretrained models from 8 different families, ranging from 124M to 14.2B parameters. First, we observe that the performance of models in predicting brain responses follows a scaling law, where the fit with brain activity increases linearly with the logarithm of the number of parameters of the model (and its performance on natural language processing tasks). Second, although this effect is present in both hemispheres, it is stronger in the left than in the right hemisphere. Specifically, the left-right difference in brain correlation follows a scaling law with the number of parameters. This finding reconciles computational analyses of brain activity using large language models with the classic observation from aphasic patients showing left hemisphere dominance for language.

翻译：过去十年间，自然语言处理研究蓬勃发展，参与者在聆听连续文本的同时接受脑部扫描。研究者最初使用词嵌入技术，随后采用大型语言模型，构建编码模型来分析大脑信号。通过向这些模型呈现与参与者相同的文本，可以识别出功能磁共振成像（fMRI）时间序列与模型人工神经元预测序列之间存在显著相关性的大脑区域。这些研究揭示了一个引人注目的发现：它们呈现出高度对称的双侧激活模式，这与语言处理具有明显左半球偏侧化的经典认知存在一定差异。本研究通过调控大型语言模型的复杂度，对fMRI数据集进行分析，测试了来自8个不同系列、参数量从1.24亿到142亿不等的28个预训练模型。首先，我们发现模型预测大脑响应的性能遵循缩放定律，即模型与大脑活动的拟合度随模型参数量（及其在自然语言处理任务上的性能）的对数线性增长。其次，尽管这种效应在双侧半球均存在，但左半球的增强效应显著强于右半球。具体而言，大脑相关性的左右半球差异随参数量变化遵循缩放定律。这一发现将使用大型语言模型进行的脑活动计算分析，与失语症患者研究中显示左半球语言优势的经典观察结果统一起来。