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Computational and brain predictors of emotion cue integration

情绪线索整合的计算和大脑预测因子

基本信息

批准号：
9923725
负责人：
Jamil Zaki
金额：
$ 45.18万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-05-19 至 2022-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9923725
关键词：
Affect Agreement Base of the Brain Basic Science Bayesian Modeling Bipolar Disorder Brain Brain region Classification Cognitive Complex Computer Models Cues Data Dependence Emotional Emotional disorder Emotions Event Exhibits Face Face Processing Facial Expression Functional Magnetic Resonance Imaging Future Image Individual Language Lateral Learning Life Linguistics Machine Learning Major Depressive Disorder Measures Mental disorders Methods Modality Modeling Mood Disorders Moods Movement National Institute of Mental Health Neurodevelopmental Disorder Neurosciences Observer Variation Participant Patients Pattern Perception Process Psychological reinforcement Psychologist Research Research Domain Criteria Research Personnel Running Sampling Scanning Scientist Sensory Social Functioning Social Interaction Social Processes Social Psychology Specific qualifier value Speech Structure System Testing Time Training Treatment Efficacy Visual Weight Work affective computing base brain abnormalities cognitive neuroscience computer science computerized tools executive function insight language comprehension multimodality neuroimaging novel recruit relating to nervous system response scaffold social social deficits tool

项目摘要

The purpose of this project is to develop computational and brain-based models of emotion cue integration: people’s inferences about others’ emotions based on dynamic, multimodal cues. Observers often decide how targets feel based on cues such as facial expressions, prosody, and language. Such inferences scaffold healthy social interaction, and abnormal inference both marks and exacerbates social deficits in numerous psychiatric disorders. Psychologists and neuroscientists have studied emotion inference for decades, but the vast majority of this work employs simplified social cues, such as vignettes or static images of faces. By contrast, “real world” emotion cues are complex, dynamic, and multimodal. Cue integration—inference based on naturalistic emotion information—likely differs from simpler inference at cognitive and neural levels, but this phenomenon remains poorly understood. This means that scientists lack a clear model of how observers adaptively process complex emotion cues, and how that processing goes awry in mental illness. Especially lacking are mechanistic models that can describe the computations and brain processes involved in cue integration with sufficient precision to predict inference in new cases, observers, and samples. This project will merge tools from social psychology, computer science, and neuroscience to generate a novel and rigorous model of emotion cue integration. We have demonstrated that in the face of complex emotion cues, observers dynamically “weight” cues from each modality (e.g., visual, linguistic) over time, a process that (i) tracks shifts in brain activity and connectivity; and (ii) can be captured using Bayesian models. Here, we will expand this work in several ways. First, we will develop precise computational tools to isolate features of emotion cues—such as facial movements, prosody, and linguistic sentiment—that track observers’ use of each cue modality during integration. Second, we will develop multi-region “signatures” of brain activity and connectivity that track emotion inference in each modality. We will use these signatures in conjunction with machine learning to predict unimodal emotion inference and cue integration in new observers and samples, based on brain data alone. Third, we will explore the context-dependence of naturalistic emotion inference by testing whether reinforcement learning can bias observers’ cue integration and accompanying brain signatures. Finally, we will model computational and neural abnormalities associated with cue integration in patients with Major Depressive Disorder and Bipolar Disorder. At the level of basic science, these data will generate a fundamentally new—and more naturalistic—approach to the neuroscience of emotion inference. The computational and brain metrics we produce will also be made publically available to facilitate the open and cumulative study of emotion inference across labs. At a translational level, we will provide a mechanistic, rich account of abnormal emotion inference in mood disorders, paving the way for computational and brain markers that can be used to assess social dysfunction and treatment efficacy in these and other mental illnesses.

这个项目的目的是开发情感线索整合的计算和基于大脑的模型：