🫂 Updating our beliefs about what our interaction partners are feeling towards us and choosing partners who accept us can help us interact with partners who are likely to reciprocate care and avoid ones that are unlikely to, guiding adaptive social choice

💡These findings offer an alternative account to interpreting activity in the social rejection network, beyond social rejection being painful or surprising. We show that experiences of rejection can also serve as learning opportunities, shaping future social decisions

🔍 Next, we asked whether the social rejection network (dACC, vACC, AI) encoded an internal model of relational value. We found that partners who elicited more similar voxel patterns were rated more similarly by the participants ("how much do you think this person likes you?")

🧠 We also found that these processes are distinctly represented in the brain
-Updating beliefs about how others value us, through good or bad ranks, tracked activity in the “social rejection network”(dACC, vACC, AI)
-Learning from rewarding outcomes tracked activity in VS

🧩 Using a Bayesian cognitive model, we found that people tracked relational value and rewarding outcomes when choosing partners

🤝 Participants tried to match with others for a trust game and got feedback on:
1. Relational value (partner's ranking of them)
2. Reward value (successful match or not)

This allowed us to separate learning how others feel toward us from tracking positive social outcomes

🤔 Social rejection hurts, but it can also be informative. We modeled how people choose partners after experiences of social rejection and acceptance & uncovered two distinct learning mechanisms shaping social affiliation using computational neuroimaging