Neural encoding of pain uncertainty is selectively amplified when inferring another’s pain
Abstract
Inferring another person’s pain is a challenging computational problem. Unlike self-related pain, individuals have no access to others’ nociceptive input and must instead rely only on indirect cues under substantial uncertainty. Using a predictive-processing framework, we modeled how expectation, uncertainty (variance-based risk), prediction error (PE) and surprise jointly shape pain-related decisions for oneself and for a stranger. During fMRI, participants received cues signaling the intensity and probability of possible painful events, then made decisions to mitigate the anticipated pain via a lottery and a wager. Behaviorally, participants were more risk-averse toward others’ pain than their own, prioritizing pain reduction over economic gain. Neurally, anticipatory risk signals in anterior insula (AI) and dorsal striatum were selectively amplified when pain concerned another person, as opposed to self-pain. By contrast, PE and surprise were reliably represented in AIns for both targets following pain delivery. However, representational similarity analysis suggest that error-signal in AIns is represented through reliable, but dissociated, patterns for self and others. Together, these findings show that expectation, uncertainty and error processing are differentially affected by the identity of the pain recipient, and underscore how models of prediction under uncertainty are powerful tools for explaining how we assess pain in other people.
