Agent-specific learning signals for self–other distinction during mentalising

Humans have a remarkable ability to simulate the minds of others. How the brain distinguishes between mental states attributed to self and mental states attributed to someone else is unknown. Here, we investigated how fundamental neural learning signals are selectively attributed to different agents. Specifically, we asked whether learning signals are encoded in agent-specific neural patterns or whether a self–other distinction depends on encoding agent identity separately from this learning signal. To examine this, we tasked subjects to learn continuously 2 models of the same environment, such that one was selectively attributed to self and the other was selectively attributed to another agent. Combining computational modelling with magnetoencephalography (MEG) enabled us to track neural representations of prediction errors (PEs) and beliefs attributed to self, and of simulated PEs and beliefs attributed to another agent. We found that the representational pattern of a PE reliably predicts the identity of the agent to whom the signal is attributed, consistent with a neural self–other distinction implemented via agent-specific learning signals. Strikingly, subjects exhibiting a weaker neural self–other distinction also had a reduced behavioural capacity for self–other distinction and displayed more marked subclinical psychopathological traits. The neural self–other distinction was also modulated by social context, evidenced in a significantly reduced decoding of agent identity in a nonsocial control task. Thus, we show that self–other distinction is realised through an encoding of agent identity intrinsic to fundamental learning signals. The observation that the fidelity of this encoding predicts psychopathological traits is of interest as a potential neurocomputational psychiatric biomarker.Author summary: In order for people to have meaningful social interactions, they need to infer each other’s beliefs. Converging evidence from humans and nonhuman primates suggests that a person’s brain can represent a second person’s beliefs by simulating that second person’s brain activity. However, it is not known how the outputs of those simulations are identified as ‘yours and not mine’. This ability to distinguish self from other is required for social cognition, and it may be impaired in mental health disorders with social cognitive deficits. We investigated self–other distinction in healthy adults learning about an environment both from their own point of view and the point of view of another person. We used computationally identified learning variables and then detected how these variables are represented by measuring magnetic fields in the brain. We found that the human brain can distinguish self from other by expressing these signals in dissociable activity patterns. Subjects who showed the largest difference between self signals and other signals were better at distinguishing self from other in the task and also showed fewer traits of mental health disorders.