As the COVID time reminded us, our life is a constant trade-off between the benefits and costs for self and those of others. In order to decide what action to take, we need to attribute values to the options at stake.
Reinforcement learning theory formalizes how people learn action-outcome values. Our feelings while we observe the consequences our actions cause, will determine the value for that action. If we feel the same when causing the same outcome again, we reinforce the initial value. If instead the action results in different outcomes, we will update the initial value to match how we now feel.
Attributing a value to an action when its outcome affects someone else is less direct. Years of research on how we perceive other people’s states suggest we have two complementary ways to guess how others feel. The cognitive route collects the information we have about the other to reason about the other. The affective route suggests that the emotional state of the other, conveyed by their facial expression, posture, etc., resonates with the state we would feel when in a similar way.
People tend to avoid situations in which they directly face people in distress more often than situation in which they are only informed about the state of the other. What is then special about witnessing what happens to others? How does seeing what happens to others affect our evaluation process?
In order to operationalize these questions, we developed a reinforcement learning paradigm in which participants need to learn which of the two options gives a higher monetary reward for the participant and higher physical pain for the other, and which gives a lower monetary reward for self and lower pain for the other. By changing the way the outcome is displayed (video of the person in pain or text), the gender of the pain-receiver, and action-outcome probabilities we will characterize the learning processes of such complex decisions.
We aimed at identifying the circuits involved using fMRI. Ideally, we would then aim to perturb these regions during the task to test the hypothesis that affective empathy is necessary to feel with the other and to update the value we associate to our own actions. Unfortunately, many of the areas involved in affective empathy are difficult to non-invasively perturb, making testing causality almost impossible, leaving most of what we know about empathy lying on correlations, or tested in animal models.
Part of this grant therefore focuses on the development of an ultrasound neuro-stimulation system in combination with ultrasound imaging which relies on changes in blood flow, more directly comparable with what normally people measure in human participants with fMRI. This system will hopefully give us a tool to establish the causal link between empathy-related phenomena and our altruistic or antisocial behavior in rodents and later in humans, in a translational way.