Deep social neuroscience: the promise and peril of using artificial neural networks to study the social brain

Author:

Sievers Beau12,Thornton Mark A3ORCID

Affiliation:

1. Department of Psychology, Stanford University , 420 Jane Stanford Way, Stanford, CA 94305, USA

2. Department of Psychology, Harvard University , 33 Kirkland St., Cambridge, MA 02138, USA

3. Department of Psychological and Brain Sciences, Dartmouth College , 6207 Moore Hall, Hanover, NH 03755, USA

Abstract

Abstract 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.

Publisher

Oxford University Press (OUP)

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