Tuckute Greta, Sathe Aalok, Srikant Shashank, Taliaferro Maya, Wang Mingye, Schrimpf Martin, Kay Kendrick, Fedorenko Evelina
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
bioRxiv. 2023 Oct 30:2023.04.16.537080. doi: 10.1101/2023.04.16.537080.
Transformer models such as GPT generate human-like language and are highly predictive of human brain responses to language. Here, using fMRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict the magnitude of brain response associated with each sentence. Then, we use the model to identify new sentences that are predicted to drive or suppress responses in the human language network. We show that these model-selected novel sentences indeed strongly drive and suppress activity of human language areas in new individuals. A systematic analysis of the model-selected sentences reveals that surprisal and well-formedness of linguistic input are key determinants of response strength in the language network. These results establish the ability of neural network models to not only mimic human language but also noninvasively control neural activity in higher-level cortical areas, like the language network.
像GPT这样的Transformer模型能够生成类人语言，并且对人类大脑对语言的反应具有高度预测性。在此，我们利用功能磁共振成像（fMRI）测量的大脑对1000个不同句子的反应，首先表明基于GPT的编码模型能够预测与每个句子相关的大脑反应强度。然后，我们使用该模型识别预计会驱动或抑制人类语言网络反应的新句子。我们发现，这些模型选择的新句子确实能强烈驱动和抑制新个体中人类语言区域的活动。对模型选择的句子进行系统分析后发现，语言输入的意外性和语法正确性是语言网络中反应强度的关键决定因素。这些结果证明了神经网络模型不仅能够模仿人类语言，还能够非侵入性地控制像语言网络这样的高级皮层区域的神经活动。
