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This paper presents Care, a data-driven affective architecture and methodology for learning models of empathy by observing human human social interactions.

A key challenge posed by affective reasoning in synthetic agents is devising empirically informed models of empathy that accurately respond in social situations.

Two complementary studies investigating the predictive accuracy and perceived accuracy of Care-induced models of empathy suggest that the Care paradigm can provide the basis for effective empathetic behavior control in embodied companion agents.

Current neurobehavioral models of empathy (e.g., [12], [13], [45]) emphasize the contribution of both automatic and controlled processes to the conscious experience of empathy.

Recent models of empathy, however, also emphasize the role of top-down processes such as perspective taking and self/other awareness [12], [13].

These data are consistent with perception-action models of empathy [10] in which observing and imagining another person in a particular state is thought to activate a similar state in the observer.

Care tracks situational data including locational, intentional, and temporal information to induce a model of empathy.

At runtime, Care uses the model of empathy to drive situation-appropriate empathetic behaviors.

The present study was aimed to clarify this issue by controlling for some affective and social variables (depression, anxiety, and social desirability) that are presumed to influence emotional and empathic measures, using a staged multicomponent model of empathy.

Prior neuroimaging studies of empathy have shown that by observing another's emotional state, part of the neural circuitry underlying the same state becomes active in oneself, whether it is disgust, pain or social emotions (see [6] [9]. Such findings are consistent with the perception-action model of empathy [10].

Examining the links between empathy and agreeableness should therefore, be set within a wider conceptual model of empathy.