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.
| Original language | English |
|---|---|
| Article number | 108670 |
| Journal | Biological Psychology |
| Volume | 183 |
| DOIs | |
| State | Published - Oct 2023 |
Keywords
- Aggressive behavior
- Eye tracking
- Machine learning
- Social rejection
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