Periodic Reporting for period 4 - HelpUS (Pioneering focused Ultrasounds as a new non-invasive deep brain stimulation for a causal investigation of empathy related brain processes in moral learning and decision making)
Reporting period: 2023-01-01 to 2024-12-31
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.
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.
Over 500 participants performed our learning task either online or while in the fMRI. Results indicate that people vary in the way they learn. Some individuals are motivated by the money choosing more often the option that brings higher monetary reward to the self at the expensive of other's pain. Others value the pain of others more than the money for self. Reinforcement learning models indicate that individual preference for the money or the pain exerts it influences at the moment in which we are directly confronted with the outcome of our actions. While no gender differences were observed in the way participants learn, female were more prosocial in some experiments and participants were more considerate toward females in others, but effect sizes for gender were small. Whether the pain of the other is directly observable or only indicated by abstract symbols did not robustly alter decisions, although small effects can be observed in particular situations.
When participants were asked to report the probability of earning money and causing pain for each symbol, they were able to correctly do so for both the preferred and non-preferred outcome, but more accurately for the preferred one. This suggests that when we decide to gain money at the expense of someone’s pain, we do remain, at least partially, aware of causing pain. Asking participants to gain more or less money or to cause more or less pain to the other showed that pain-related instructions had stronger effects on choices than money-related instruction.
Looking at brain correlates of learning in morally conflictual situations revealed that the biased expectations of self-money and other-shocks is reflected in the ventromedial prefrontal cortex, while the pain-observation network represented pain prediction errors independently of individual preferences. The cingulate cortex correlated with value updates (prediction error multiplied by learning rate, reflecting how much the pain you just caused alters the pain you'll predict the same action to cause in future). This attributes a critical role to the cingulate cortex during the learning phase, which was also confirmed by a rodent study from our lab.
We then built a flexible set-up for combing ultrasound imaging, ultrasound stimulation and psycho-physiological measures (pupil dilation, facial expressions, body movements). This allows future causal investigation on brain-body interactions during social decision making in rodents, which cannot be easily tested in humans. To further improve the ultrasound imaging, in collaboration with D. Maresca's lab at TUDelft, we developed new high-definition imaging protocols (https://doi.org/10.1101/2024.07.31.605825(opens in new window)) and one that visualizes intracellular pH (https://doi.org/10.1101/2025.01.24.634762(opens in new window))
When participants were asked to report the probability of earning money and causing pain for each symbol, they were able to correctly do so for both the preferred and non-preferred outcome, but more accurately for the preferred one. This suggests that when we decide to gain money at the expense of someone’s pain, we do remain, at least partially, aware of causing pain. Asking participants to gain more or less money or to cause more or less pain to the other showed that pain-related instructions had stronger effects on choices than money-related instruction.
Looking at brain correlates of learning in morally conflictual situations revealed that the biased expectations of self-money and other-shocks is reflected in the ventromedial prefrontal cortex, while the pain-observation network represented pain prediction errors independently of individual preferences. The cingulate cortex correlated with value updates (prediction error multiplied by learning rate, reflecting how much the pain you just caused alters the pain you'll predict the same action to cause in future). This attributes a critical role to the cingulate cortex during the learning phase, which was also confirmed by a rodent study from our lab.
We then built a flexible set-up for combing ultrasound imaging, ultrasound stimulation and psycho-physiological measures (pupil dilation, facial expressions, body movements). This allows future causal investigation on brain-body interactions during social decision making in rodents, which cannot be easily tested in humans. To further improve the ultrasound imaging, in collaboration with D. Maresca's lab at TUDelft, we developed new high-definition imaging protocols (https://doi.org/10.1101/2024.07.31.605825(opens in new window)) and one that visualizes intracellular pH (https://doi.org/10.1101/2025.01.24.634762(opens in new window))
The focus of the project on conflictual choices between self and others expands the use of reinforcement learning models and unravel the brain circuits involved.
We developed a mathematical model that predicts the time course and individual variability of choices while participants witness the consequences of actions on others and themselves. We then showed that the network associated with empathy contain signals that conform to the predictions of our mathematical models (https://doi.org/10.1038/s41467-023-36807-3(opens in new window)).
To study causality between brain activity and behavior, researchers perturb the activity of a particular regions while behavioral changes are recorded, but this is often difficult in humans due to the limitation of currently available tools and primarily done in rodents. Brain activity is typically measured using different methods in humans and animals, making the translation of result across species is difficult.
We therefore built a flexible set-up for combing ultrasound imaging, ultrasound stimulation and psycho-physiological measures (pupil dilation, facial expressions, body movements), allowing future causal investigation on brain-body interactions during social decision making in rodents. The mid sagittal plan images generated from an observer mouse witnessing a conspecific receiving a noxious stimulation showed striking similarities with those observed in human fMRI, confirming the homologies in the neural mechanisms involved in perceiving the pain of others.
We developed a mathematical model that predicts the time course and individual variability of choices while participants witness the consequences of actions on others and themselves. We then showed that the network associated with empathy contain signals that conform to the predictions of our mathematical models (https://doi.org/10.1038/s41467-023-36807-3(opens in new window)).
To study causality between brain activity and behavior, researchers perturb the activity of a particular regions while behavioral changes are recorded, but this is often difficult in humans due to the limitation of currently available tools and primarily done in rodents. Brain activity is typically measured using different methods in humans and animals, making the translation of result across species is difficult.
We therefore built a flexible set-up for combing ultrasound imaging, ultrasound stimulation and psycho-physiological measures (pupil dilation, facial expressions, body movements), allowing future causal investigation on brain-body interactions during social decision making in rodents. The mid sagittal plan images generated from an observer mouse witnessing a conspecific receiving a noxious stimulation showed striking similarities with those observed in human fMRI, confirming the homologies in the neural mechanisms involved in perceiving the pain of others.