ReviewSocial cognitive and affective neuroscience2024
Deep social neuroscience: the promise and peril of using artificial neural networks to study the social brain.
Review in Social cognitive and affective neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Structurally restricted message-passing within shallow architectures for explainable network-level brain decoding on small cohorts.Frontiers in behavioral neuroscience · 2026Article
- Immersive virtual reality as a novel approach to improve social cognition in multiple sclerosis: an EEG-based pilot study.Frontiers in neuroscience · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.