Deep social neuroscience : the promise and peril of using artificial neural networks to study the social brain
© The Author(s) 2024. Published by Oxford University Press..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:19 |
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| Enthalten in: |
Social cognitive and affective neuroscience - 19(2024), 1 vom: 21. Feb. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sievers, Beau [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial neural network |
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| Anmerkungen: |
Date Completed 23.02.2024 Date Revised 23.02.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/scan/nsae014 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a 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 | ||
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