Overcoming Pavlovian bias in semantic space.

Action is invigorated in the presence of reward-predicting stimuli and inhibited in the presence of punishment-predicting stimuli. Although valuable as a heuristic, this Pavlovian bias can also lead to maladaptive behaviour and is implicated in addiction. Here we explore whether Pavlovian bias can be overcome through training. Across five experiments, we find that Pavlovian bias is resistant to unlearning under most task configurations. However, we demonstrate that when subjects engage in instrumental learning in a verbal semantic space, as opposed to a motoric space, not only do they exhibit the typical Pavlovian bias, but this Pavlovian bias diminishes with training. Our results suggest that learning within the semantic space is necessary, but not sufficient, for subjects to unlearn their Pavlovian bias, and that other task features, such as gamification and spaced stimulus presentation may also be necessary. In summary, we show that Pavlovian bias, whilst robust, is susceptible to change with experience, but only under specific environmental conditions.

Social training reconfigures prediction errors to shape Self-Other boundaries.

Selectively attributing beliefs to specific agents is core to reasoning about other people and imagining oneself in different states. Evidence suggests humans might achieve this by simulating each other's computations in agent-specific neural circuits, but it is not known how circuits become agent-specific. Here we investigate whether agent-specificity adapts to social context. We train subjects on social learning tasks, manipulating the frequency with which self and other see the same information. Training alters the agent-specificity of prediction error (PE) circuits for at least 24 h, modulating the extent to which another agent's PE is experienced as one's own and influencing perspective-taking in an independent task. Ventromedial prefrontal myelin density, indexed by magnetisation transfer, correlates with the strength of this adaptation. We describe a frontotemporal learning network, which exploits relationships between different agents' computations. Our findings suggest that Self-Other boundaries are learnable variables, shaped by the statistical structure of social experience.

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.