Citation

Quarmley M, Zelinsky G, Athar S, Yang Z, Drucker JH, Samaras D, Jarcho JM. Biol. Psychol. 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.biopsycho.2023.108670

PMID

37652178

Abstract

Aggression elicited by social rejection is costly, prevalent, and often lethal. Attempts to predict rejection-elicited aggression using trait-based data have had little success. This may be because in-the-moment aggression is a complex process influenced by current states of attention, arousal, and affect which are poorly predicted by trait-level characteristics. In a study of young adults (N=89; 18-25 years), machine learning tested the extent to which nonverbal behavioral indices of attention (eye gaze), arousal (pupillary reactivity), and affect (facial expressions) during a novel social interaction paradigm predicted subsequent aggression towards rejecting and accepting peers. Eye gaze and pupillary reactivity predicted aggressive behavior; predictions were more successful than measures of trait-based aggression and harsh parenting. These preliminary results suggest that nonverbal behavior may elucidate underlying mechanisms of in-the-moment aggression.

Language: en

Keywords

aggressive behavior; machine learning; eye tracking; social rejection