Forewarned Is Forearmed: The Single- and Dual-Brain Mechanisms in Detectors from Dyads of Varying Social Distance during Deceptive Outcome Evaluation
Abstract
Preventing deception requires understanding how lie detectors process social information across social distance. Although the outcomes of such information are crucial, how detectors evaluate gains or losses from close versus distant others remains unclear. Using a sender–receiver paradigm and functional near-infrared spectroscopy hyperscanning, we recruited 66 healthy adult dyads (32 male and 34 female dyads) to investigate how perceived social distance modulates the neural basis in receivers (the detector) during deceptive gain/loss evaluation. The results showed that detectors were more prone to deception in gain contexts, with these differences mediated by connectivity in risk evaluation (dorsolateral prefrontal cortex, DLPFC), reward-processing (orbitofrontal cortex, OFC), and intention-understanding regions (frontal pole area). Hyperscanning analyses revealed that friend dyads exhibited higher interpersonal neural synchrony (INS) in these regions than stranger dyads. In gain contexts, friend dyads showed enhanced INS in the OFC, whereas in loss contexts, enhanced INS was observed in the DLPFC. Trial-level analysis revealed that the INS during the current trial effectively predicted the successful deception of that trial. We constructed a series of regression models and found that INS provides superior predictive power over single-brain measures. The INS-based support vector regression model achieved an accuracy of 86.66% in predicting deception. This indicates that increased trust at closer social distances reduces vigilance and fosters relationship-oriented social information processing. As the first to identify INS as a neural marker for deception from the detector's perspective, this work advances interpersonal deception theory and offers a neuroscientific basis for credit risk management.
Article Details
Authors (10)
Rui Huang
School of Chemistry
Xiaowei Gao
Chenyu Zhang
Jingyue Liu
Department of Physics
Ye Zhang
Yifei Zhong
Yunen Chen
He Wang
Xing Wei
Yingjie Liu