Hosseini Eghbal, Casto Colton, Zaslavsky Noga, Conwell Colin, Richardson Mark, Fedorenko Evelina
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
bioRxiv. 2024 Dec 26:2024.12.26.629294. doi: 10.1101/2024.12.26.629294.
Many artificial neural networks (ANNs) trained with ecologically plausible objectives on naturalistic data align with behavior and neural representations in biological systems. Here, we show that this alignment is a consequence of convergence onto the same representations by high-performing ANNs and by brains. We developed a method to identify stimuli that systematically vary the degree of inter-model representation agreement. Across language and vision, we then showed that stimuli from high- and low-agreement sets predictably modulated model-to-brain alignment. We also examined which stimulus features distinguish high- from low-agreement sentences and images. Our results establish representation universality as a core component in the model-to-brain alignment and provide a new approach for using ANNs to uncover the structure of biological representations and computations.
许多在自然主义数据上以生态合理目标训练的人工神经网络（ANN）与生物系统中的行为和神经表征相一致。在这里，我们表明这种一致性是高性能ANN和大脑收敛到相同表征的结果。我们开发了一种方法来识别能够系统地改变模型间表征一致性程度的刺激。然后，在语言和视觉领域，我们表明来自高一致性和低一致性集合的刺激可预测地调节了模型与大脑的一致性。我们还研究了哪些刺激特征区分了高一致性和低一致性的句子及图像。我们的结果将表征普遍性确立为模型与大脑一致性的核心组成部分，并提供了一种利用ANN揭示生物表征和计算结构的新方法。
