Adaptive empathy: A model for learning empathic responses based on feedback

Empathy is usually deployed in social interactions. Nevertheless, common measures and examinations of empathy study this construct in isolation from the person in distress. In this paper we seek to extend the field of examination to include both empathizer and target in order to determine whether and how empathic responses are affected by feedback and learned through interaction. Building on computational approaches in feedback-based adaptations (e.g., no feedback, model-free and model-based learning), we propose a framework for understanding how empathic responses are learned based on feedback. In this framework, adaptive empathy, defined as the ability to adapt one’s empathic responses, is a central aspect of empathic skills, and can provide a new dimension to the evaluation and investigation of empathy. By extending existing neural models of empathy, we suggest that adaptive empathy may be mediated by interactions between the neural circuits associated with valuation, shared distress, observation-execution and mentalizing. Finally, we propose that adaptive empathy should be considered as a prominent facet of empathic capabilities with the potential to explain empathic behavior in health and in psychopathology.