社会神经科学
概化理论
认知科学
领域(数学)
人工神经网络
神经影像学
神经科学
心理学
认知神经科学
人工智能
计算机科学
认知
社会认知
发展心理学
数学
纯数学
作者
Beau Sievers,Mark Allen Thornton
标识
DOI:10.31234/osf.io/fr4cb
摘要
This review offers an accessible primer to social neuroscientists interested in neural networks. It begins by providing a conceptual 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.
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