Area:
Serotonin, social neuroscience, neuroeconomics
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High-probability grants
According to our matching algorithm, Molly J. Crockett is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2020 |
Crockett, Molly J |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Computational Assays of Moral Inference
Project Summary/Abstract We choose who to trust or avoid based on inferences about whether they are likely to help or harm us. Successfully inferring the moral character of others is crucial for initiating and maintaining healthy social relationships. When moral inference breaks down, we might place our trust in the wrong people, or prematurely end relationships because we incorrectly infer our partner means us harm. Past work suggests disruptions to moral inference may underlie interpersonal problems observed in psychiatric disorders, but the neurocognitive mechanisms that underlie this relationship are poorly understood. Progress has been limited because we lack precise, quantitative measures of moral inference that can bridge levels of analysis between self-report, behavior and brain function. To address these limitations, the proposed research will develop parametrically detailed computational assays of moral inference in humans. We build on work in my lab that has begun to characterize the computational basis of moral inference in healthy people using a formal Bayesian framework for modeling inference under uncertainty, the Hierarchical Gaussian Filter (HGF). Our HGF model of moral inference describes how people infer another person?s tendency to be harmful or helpful based on observing their behavior, and has several parameters that are potentially relevant to psychopathology. Here, we focus on two: the uncertainty of beliefs about the harmfulness of others, and prior beliefs about the harmfulness of others. Our recent work in patients with Borderline Personality Disorder suggests that people who are unable to form appropriately certain beliefs about social partners may have problems forming healthy attachment relationships. We further hypothesize that people with pessimistic prior beliefs about the harmfulness of others may be less motivated to affiliate with and trust others. Crucially, our task and model can quantify individual variance in belief uncertainty and prior beliefs simultaneously in behavior and self-reports, thus bridging these levels of analysis. In the proposed study, over a six-month period we will collect three within-subject samples of moral inference task performance alongside a battery of clinical and transdiagnostic questionnaires measuring social, affective and cognitive dysregulation. In Aim 1, we will validate our model and task?s psychometric properties (such as test-retest reliability) and characterize normative variance in model parameters that future studies can compare against patient behavior. In Aim 2, we will characterize the longitudinal relationship between model parameters and questionnaire measures, investigating the extent to which our model parameters describe stable individual traits and track with clinical symptoms over time. Addressing these questions will pave the way for future work developing more detailed computational assays of moral inference linking behavior, self-report and brain function in patients. Our findings may ultimately be useful in identifying transdiagnostic biomarkers of interpersonal dysfunction that can be targeted for intervention.
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