Dissecting the features and neural mechanisms supporting naturalistic social inference
剖析支持自然社会推理的特征和神经机制
基本信息
- 批准号:10631938
- 负责人:
- 金额:$ 7.18万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-06-01 至 2025-05-31
- 项目状态:未结题
- 来源:
- 关键词:AdultAuditoryBehaviorBehavioralBehavioral ModelBrainBrain regionCognitiveComputer ModelsComputer Vision SystemsConsensusDimensionsFaceFace ProcessingFacial ExpressionFellowshipFriendsFunctional Magnetic Resonance ImagingFutureGenetic TranscriptionGesturesGoalsHumanIndividualIndividual DifferencesJudgmentLearningLifeLinear RegressionsLinkMental DepressionMental disordersMethodsMindModelingModernizationNatural Language ProcessingParticipantPatternPersonsPopulationPostdoctoral FellowPovertyProcessResearchScanningSchizophreniaSemanticsSocial InteractionSourceSpeechStereotypingStimulusSystemTextTimeTrainingTranslatingUpdateValidationVideo RecordingVisualWeightWorkautism spectrum disordercomputerized toolsdeep neural networkdyadic interactionfeature extractionhuman dataimpressionindividuals with autism spectrum disordermachine learning methodmental statementalizationmodel buildingneuralneural patterningneuroimagingneuromechanismpredictive modelingpsychologicrecruitresponseskillssocialsocial cognitiontooltraittrustworthinessvideo chat
项目摘要
Project Summary
This first-time fellowship application proposes an integrated training and research plan focused on human
social interactions. The applicant has a strong and broad background in computational modeling of behavioral
human data, and will now leverage this in her postdoc at Caltech to learn how to model and analyze human
interactions from text-based chats and video interactions in behavior and in the brain (using fMRI). Aim 1 will
utilize state-of-the-art computer vision and natural language processing methods to annotate the visual, auditory
and text features of social interactions. Specific features, such as facial expressions and the semantic content
of text, will be extracted from 300 recorded social interactions between healthy adults. These features, in turn,
will be used to fit models that predict the social judgments that participants make about one another: how
trustworthy, friendly or arrogant do they judge their partner to be? A main goal of this first Aim is to extend human
social judgments beyond the typically narrow context used in past studies (e.g., static photos of faces) into the
naturalistic, interactive context we actually encounter in the real world. This emphasis will also be important for
future applications to psychiatric populations with critical deficits in social cognition (such as autism, outside the
scope of the present fellowship). Aim 2 translates the interactions of Aim 1 into neuroimaging, and BOLD-fMRI
will be acquired while participants watch previously recorded social interactions. This will reveal the brain regions
and networks that track the dynamic features of the stimuli. Neural representations of social inferences will be
extracted from multivariate activation patterns using representational similarity analysis, and similarity in neural
processing will be compared to similarity in behavioral social inference using inter-subject correlation analysis.
All analyses will both apply a whole-brain approach and will query specific social cognition networks. A main
goal of Aim 2 is to characterize the neural systems that subserve the dynamic construction of social attributions,
information that in future studies can be linked to individual differences and psychiatric illness.
项目摘要
这是第一次申请奖学金,提出了一个综合的培训和研究计划,重点是人类
社交互动申请人在行为的计算建模方面具有强大而广泛的背景。
人类数据,现在将利用她在加州理工学院的博士后,学习如何建模和分析人类
基于文本的聊天和视频互动在行为和大脑中的相互作用(使用功能磁共振成像)。目标1将
利用最先进的计算机视觉和自然语言处理方法,
以及社会交往的文本特征。具体特征,如面部表情和语义内容
的文本,将从300个健康成年人之间的社会互动记录中提取。反过来,这些特征,
将被用来拟合模型,预测参与者对彼此的社会判断:
他们认为自己的伴侣是值得信赖的、友好的还是傲慢的?第一个目标的一个主要目标是扩大人类
社会判断超出了过去研究中使用的典型狭窄背景(例如,静态照片的脸)到
我们在真实的世界中遇到的自然的、互动的环境。这一重点也将是重要的,
未来应用于社会认知严重缺陷的精神病人群(如自闭症,
本研究金的范围)。Aim 2将Aim 1的相互作用转化为神经成像,
将在参与者观看先前记录的社交互动时获得。这将揭示大脑区域
以及跟踪刺激的动态特征的网络。社会推理的神经表征
使用代表性相似性分析从多变量激活模式中提取,
将使用受试者间相关性分析将处理与行为社会推理中的相似性进行比较。
所有分析都将采用全脑方法,并将查询特定的社会认知网络。主
目标2的目标是表征有助于社会归因的动态构建的神经系统,
在未来的研究中,这些信息可以与个体差异和精神疾病联系起来。
项目成果
期刊论文数量(0)
专著数量(0)
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