Projektdetails
Abstract
Wider research context and theoretical frameworkPain is affecting 11–40% of the general population worldwide, therefore understanding pain in both ourselves(first-hand pain) and others (empathy for pain) is vital for the society. However, pain is highly subjective, andthe perception of pain depends heavily on our prior expectation. Bayesian integration has been proposed tounderstand how the perception of pain is modulated by prior pain expectation and noxious input. To date,however, only few studies have examined the neural implementation underlying such Bayesian integration.Furthermore, no studies have investigated whether the Bayesian integration process in first-hand pain alsounderpins empathy for pain.Hypotheses and objectivesTo close these gaps, our project will provide a systematic investigation into the neurocomputationalmechanisms of how prior expectations (i.e., how much pain is expected) and noxious information (i.e., howmuch pain is received) are integrated for both first-hand pain and empathy for pain. We hypothesize that thevariation in the degree of pain perception in oneself and others is associated with the differences in thedegree of Bayesian integration between prior expectations and noxious input, modulated by the source ofhow prior expectation is formed.MethodsWe propose two studies with functional magnetic resonance imaging (fMRI) to test our hypotheses, whenprior expectations are formed from direct experience (Study 1) and vicarious experience (Study 2). Thisexperimental paradigm will allow us to decouple the bidirectional influences of prior expectation and noxiousinput on each other. A series of data will be collected, including subjective pain ratings, physiologicalresponses, and fMRI signals, and they will be quantified with state-of-the-art hierarchical modeling togetherwith mass-univariate and multivariate fMRI analyses.Originality & innovationThis project employs rigorous approaches to address original research questions about the underlyingneurocomputational mechanisms of Bayesian integration in first-hand pain and empathy for pain, whichmoves beyond previous verbal models that lack formal quantification. This will thus help us gain a betterunderstanding of how we process pain in oneself, and how we share and understand pain in others, and thushow we manage better to avoid harm to ourselves as well as others.Primary researchers involvedWith this fellowship, I (applicant) will exploit my computational skills in the field of social, cognitive andaffective neuroscience, as well as advance my career development to become an independent researcher.This will be achieved by the two-way knowledge between the host institution and me, and complementaryexpert supervisions from Prof. Claus Lamm (co-applicant) and Prof. Christian Büchel (collaborator), both ofwhom are world-leading experts in the field and have a strong track record in promoting young researchers.
Akronym | BayesEmpathy |
---|---|
Status | Abgeschlossen |
Tatsächlicher Beginn/ -es Ende | 20/05/21 → 19/05/23 |
Schlagwörter
- Magnetic Resonance Imaging
- Social neuroscience
- Bayesian integration
- Pain
- Empathy for pain
- Computational modeling