Using conversational artificial intelligence to dissect the psychoneuroimmunology of social safety and threat
Abstract
Human health is profoundly shaped by the quality of our social environments, yet the mechanisms through which social experiences become biologically embedded remain poorly understood. Social Safety Theory (SST) proposes that perceived social safety and threat influence health through neuroendocrine and immune pathways, but it remains unclear which features of social interaction engage these pathways. Here, we argue that conversational artificial intelligence (AI) provides a powerful experimental tool to address this mechanistic gap. Synthetic agents allow researchers to systematically vary core social signals, including responsiveness, reciprocity, empathic cues, agency, and multimodal information, while holding other features of the interaction relatively constant. This controllability creates a unique opportunity to identify which social signals are necessary or sufficient to generate perceived safety or threat and to engage downstream biological responses. Building on SST, we propose a psychoneuroimmunological framework linking experimentally controlled features of synthetic social interaction to neuroendocrine and immune processes and derive testable hypotheses concerning perceived humanness, social-signal strength, and multimodal embodiment. By enabling the causal decomposition of social interaction, conversational AI could help reveal what makes an interaction biologically social and clarify the mechanisms linking social disconnection to health.
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BibTeX
@misc{packheiser2026using,
title = {{Using conversational artificial intelligence to dissect the psychoneuroimmunology of social safety and threat}},
author = {Julian Packheiser and Sigrid Elsenbruch and Harald Engler and Dirk Scheele},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/dasvu_v1},
url = {https://osf.io/preprints/psyarxiv/dasvu_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Using conversational artificial intelligence to dissect the psychoneuroimmunology of social safety and threat
AU - Julian Packheiser
AU - Sigrid Elsenbruch
AU - Harald Engler
AU - Dirk Scheele
PY - 2026
DA - 2026/10/10
PB - PsyArXiv
DO - 10.31234/osf.io/dasvu_v1
UR - https://osf.io/preprints/psyarxiv/dasvu_v1/
AB - Human health is profoundly shaped by the quality of our social environments, yet the mechanisms through which social experiences become biologically embedded remain poorly understood. Social Safety Theory (SST) proposes that perceived social safety and threat influence health through neuroendocrine and immune pathways, but it remains unclear which features of social interaction engage these pathways. Here, we argue that conversational artificial intelligence (AI) provides a powerful experimental tool to address this mechanistic gap. Synthetic agents allow researchers to systematically vary core social signals, including responsiveness, reciprocity, empathic cues, agency, and multimodal information, while holding other features of the interaction relatively constant. This controllability creates a unique opportunity to identify which social signals are necessary or sufficient to generate perceived safety or threat and to engage downstream biological responses. Building on SST, we propose a psychoneuroimmunological framework linking experimentally controlled features of synthetic social interaction to neuroendocrine and immune processes and derive testable hypotheses concerning perceived humanness, social-signal strength, and multimodal embodiment. By enabling the causal decomposition of social interaction, conversational AI could help reveal what makes an interaction biologically social and clarify the mechanisms linking social disconnection to health.
ER -