Neurocomputational basis of learning when choices simultaneously affect both oneself and others

• DOI: 10.31234/osf.io/rf4x9
• Publication Date: 2023-02-25T05:02:12Z
• Authors: Shawn A Rhoads; Lin Gan; Kathryn Berluti; Katherine O'Connell; Jo Cutler; Patricia Lockwood; Abigail Marsh
• Keywords: Affect; Prosocial Behavior; Value (mathematics)

Most prosocial and antisocial behaviors affect ourselves others simultaneously. To know whether to repeat that help or harm, we must learn from their outcomes. But the neurocomputational processes supporting such simultaneous learning remain poorly understood. In this pre-registered study, two independent samples learned make choices simultaneously affected themselves another person. Detailed model comparison showed people integrate self- other-relevant information into a single cached value per choice, but update asymmetrically based on different types of prediction errors related target (e.g., self, other) valence positive, negative). People who acquire more patterns are sensitive about how others. However, those with higher levels subclinical psychopathic traits relatively insensitive unexpected outcomes for Model-based neuroimaging revealed distinct brain regions tracking guided by asymmetric update. These results demonstrate way distinctly encode resulting behavior guides desirable same will be in future, regardless it is mutually beneficial costly, instrumentally harmful, altruistic.