Bayesian integration in pain and empathy for pain

Pain is affecting 1140% of the general population worldwide, therefore understanding pain in both ourselves (first-hand pain) and others (empathy for pain) is vital for society. However, pain is highly subjective, and the perception of pain depends heavily on our prior expectation s. Past research has embraced the Bayesian integration account to formally quantify the perception in first-hand pain, namely, the posterior pain perception is modulated by prior pain expectation (e.g., how painful the event can be) and noxious input (e.g., the actual pain intensity). However, whether empathy for pain can also be quantified by the Bayesian integration account is poorly understood. And if so, what is the neurocomputational mechanisms that underly the process of empathy for pain? Moreover, when the source of prior expectation differs (e.g., knowing something is painful through ones own experience vs. observing others reaction), can the Bayesian integration account still hold? We will tackle this research question by two closely related experiments with human participants. In both studies, participants will first establish the outcome contingencies (e.g., which stimulus will be more likely followed by pain) using a classic conditioning paradigm, being the learning phase, and then they will be predicting the painfulness of each stimulus either for themselves or for another person, being the testing phase. Importantly, in Experiment 1, the conditioning will be implemented through direct learning, whereas in Experiment 2, the conditioning will be implemented through observational learning. Across both studies, we will employ state-of-the-art computational modeling and model-based functional neuroimaging to establish a comprehensive neurocomputational account of first -hand pain and empathy for pain. Results from this project will help us gain a better understanding of how we process pain in oneself, and how we share and understand pain in others, and thus how we manage better to avoid harm to ourselves as well as others. This project is not only relevant to social neuroscience and cognitive computational neuroscien ce, but seeks to break the boundaries of research disciplines and promote cross-section new research avenues.