Department of Psychology, Stanford University, 420 Jane Stanford Way, Stanford, CA 94305, USA.
Department of Psychology, Harvard University, 33 Kirkland St., Cambridge, MA 02138, USA.
Soc Cogn Affect Neurosci. 2024 Feb 21;19(1). doi: 10.1093/scan/nsae014.
This review offers an accessible primer to social neuroscientists interested in neural networks. It begins by providing an overview of key concepts in deep learning. It then discusses three ways neural networks can be useful to social neuroscientists: (i) building statistical models to predict behavior from brain activity; (ii) quantifying naturalistic stimuli and social interactions; and (iii) generating cognitive models of social brain function. These applications have the potential to enhance the clinical value of neuroimaging and improve the generalizability of social neuroscience research. We also discuss the significant practical challenges, theoretical limitations and ethical issues faced by deep learning. If the field can successfully navigate these hazards, we believe that artificial neural networks may prove indispensable for the next stage of the field's development: deep social neuroscience.
这篇综述为对神经网络感兴趣的社会神经科学家提供了一个通俗易懂的入门介绍。它首先概述了深度学习中的关键概念。然后讨论了神经网络对社会神经科学家有三种用途：（i）构建统计模型，从大脑活动预测行为；（ii）量化自然刺激和社会互动；（iii）生成社会大脑功能的认知模型。这些应用有可能提高神经影像学的临床价值，并提高社会神经科学研究的普遍性。我们还讨论了深度学习面临的重大实际挑战、理论限制和伦理问题。如果该领域能够成功应对这些挑战，我们相信人工神经网络可能对该领域下一阶段的发展——即深度社会神经科学——不可或缺。
