Autor(es):
IJzerman, H. ; Lindenberg, S. ; Dalğa, I. ; Weissgerber, S. C. ; Vergara, R. C. ; Cairo, A. H. ; Čolić, M. V. ; Dursun, P. ; Frankowska, N. ; Hadi, R. ; Hall, C. J. ; Hong, Y. ; Hu, Chuan-Peng ; Joy-Gaba, Jennifer ; Lazarević, D. ; Lazarević, L. B. ; Parzuchowski´, M. ; Ratner, K. G. ; Rothman, D. ; Sim, S. ; Simão, C. ; Song, M. ; Stojilović, D. ; Blomster, J. ; Brito, R. ; Hennecke, M. ; Jaume-Guazzini, F. ; Schubert, T. W. ; Schütz, A. ; Seibt, B. ; Zickfeld, Janis H.
Data: 2018
Identificador Persistente: http://hdl.handle.net/10071/19812
Origem: Repositório ISCTE
Assunto(s): Social integration; Social thermoregulation theory; Attachment theory; Embodiment; Machine learning
Descrição
Social thermoregulation theory posits that modern human relationships are pleisiomorphically organized around body temperature regulation. In two studies (N = 1755) designed to test the principles from this theory, we used supervised machine learning to identify social and non-social factors that relate to core body temperature. This data-driven analysis found that complex social integration (CSI), defined as the number of high-contact roles one engages in, is a critical predictor of core body temperature. We further used a cross-validation approach to show that colder climates relate to higher levels of CSI, which in turn relates to higher CBT (when climates get colder). These results suggest that despite modern affordances for regulating body temperature, people still rely on social warmth to buffer their bodies against the cold.
