Outsiders See More than Insiders: Interpersonal Neural and Computational Mechanisms of Friend Advice Interactions Optimizing Mate-Choice Decisions

Individuals often rely on friends’ advice when facing ambiguous cues in mate-choice decisions, yet how such advice optimizes outcomes remains unclear. Using fNIRS hyperscanning, we simultaneously recorded neural and behavioral activity from 66 adult friend dyads (33 female dyads) during interactive advice phases. Computational modeling quantified the decision optimization parameter ( β ). It showed that only the bidirectional Social Information Processing (SIP) model—best fit behavioral data, indicating that optimization arises from reciprocal adaptation rather than one-way influence. Behaviorally, positive advice from in the relationship advisors increased mate-choice intention, whereas negative advice from single advisors more effectively promoted optimization. Both conditions elicited enhanced interpersonal neural synchrony (INS) in the dorsolateral prefrontal cortex (DLPFC), linked to risk evaluation, and in the left superior and middle temporal gyri (l-STG, l-MTG), associated with semantic processing and value transformation. Machine learning analyses showed that trial-level INS predicted momentary optimization, while task-level INS robustly forecasted overall decision improvement. Together, these findings demonstrate that decision optimization emerges from dynamic brain-to-brain alignment in control and semantic networks—reflecting emotional sharing during positive advice and risk re-evaluation during negative advice. This study extending SIP theory to interactive decision-making and revealing how friend advice enhances decision quality and social well-being.