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The structure and dynamics of mental state representations

心理状态表征的结构和动态

基本信息

批准号：
10176594
负责人：
Diana Tamir
金额：
$ 36.45万
依托单位：
PRINCETON 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：
2017-09-18 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10176594
关键词：
3-Dimensional Address Behavior Behavioral Biological Assay Cognitive Computer Models Contracts Data Dimensions Emotional Fatigue Foundations Functional Magnetic Resonance Imaging Functional disorder Future Impairment Individual Individual Differences Link Location Maps Markov Chains Measures Methods Mind Modality Modeling Moods Participant Performance Persons Probability Sampling Sampling Studies Shapes Social Behavior Social Environment Social Functioning Social Impacts Social Interaction Stimulus Structure Study models Techniques Testing Touch sensation Visual Work autism spectrum disorder experience experimental study gratitude innovation insight lexical markov model mental representation mental state neural patterning neuroimaging novel psychologic relating to nervous system social social cognition social deficits social relationships success theories trait two-dimensional

项目摘要

PROJECT SUMMARY / ABSTRACT Navigating the social world requires people to predict others' actions. This poses a significant challenge be- cause people cannot directly observe some of the best predictors of action: others' internal mental states. In- dividuals who can leverage information about these hidden causes of actions—by representing mental states—can better predict those actions and more successfully navigate the social world. How do people (i) represent the richness and complexity of others' invisible mental states, and (ii) use those representations to make social predictions? We propose that people reduce the complexity of others' minds by attending to the location of their mental states on a few key dimensions in a mental state “map.” We have previously used rep- resentational similarity analyses (RSA) on functional neuroimaging (fMRI) data to show that people indeed represent others' mental states using a simple, low-dimensional map. The structure of this map is defined by three dimensions—rationality, social impact, and valence. Understanding how people employ this map will provide key insights into how people predict others' actions. Aim 1: This proposal seeks to develop a compre- hensive framework of mental state representations by first characterizing the structure of the map of mental states. We will refine the structure by assessing how it adapts across new social contexts and modalities. We will measure two structural features of the map—size and shape—using novel RSA methods on fMRI data. Di- mensions that have universal social functions should hold a stable shape across all contexts and modalities; dimensions that have specific, or contextualized functions should deform across context or modality. The size of the space should expand to reflect the social relevance of the target. Understanding the structure of the men- tal state map lays the foundation for understanding how people represent others' mental states. Aim 2: We next explore how people leverage this map to make social predictions. We propose that the mental state map encodes not only the location of others' current mental state, but also where in the map they will likely move to Thus, people could make social predictions We will use fMRI, large-scale experience sampling studies, and computational modeling over behavioral data to establish that people indeed spontaneously model others' mental state dynamics, and moreover, that these models make accurate social predictions. In both aims, we will test how the structure and dynamic of mental state maps predict social functioning (or dysfunction). Using an individual differences ap- next. by modeling the dynamics of others' mental states as paths through this map. proach, we will link our novel measures of structure and dynamics to participants' performance on a battery of social cognition, social behavior, and social relationship measures. Taken together, this proposal uses innova- tive techniques (e.g., novel RSA methods, Markov modeling, and experience sampling) to develop a compre- hensive theory of mental state representations and social predictions. Understanding these basic building blocks of social cognition carries promising future directions for assessing and ameliorating social deficits.

项目摘要/摘要在社交世界中导航需要人们预测他人的行为。这是一个重大的挑战，因为- 因为人们不能直接观察到行动的一些最好的预测因素：他人的内在心理状态。在- 可以利用有关这些隐藏的行为原因的信息的个人-通过代表心理国家-可以更好地预测这些行动，并更成功地在社交世界中导航。人们是如何做到的(一) 表示他人看不见的心理状态的丰富性和复杂性，以及(Ii)使用这些表示做社交预测吗？我们建议人们通过关注其他人的思想来减少思想的复杂性他们的精神状态在精神状态“地图”中的几个关键维度上的位置。我们以前用过rep- 对功能神经成像(FMRI)数据进行再定向相似性分析(RSA)，以表明人们确实用一张简单的低维地图来表现他人的精神状态。此地图的结构由以下内容定义三个维度--合理性、社会影响力和价态。了解人们如何使用这张地图将会提供关于人们如何预测他人行为的关键见解。目标1：这项提议寻求开发一种压缩- 先表征心理地图结构的心理状态表征框架各州。我们会通过评估它如何适应新的社会背景和模式来优化结构。我们将使用新的RSA方法对fMRI数据进行测量，以测量地图的两个结构特征--大小和形状。迪- 具有普遍社会功能的提法应在所有语境和情态中保持稳定的形状；具有特定或情境化功能的维度应该在不同的上下文或通道中变形。大小应扩大空间的范围，以反映目标的社会相关性。了解这些人的结构- TAL状态图为理解人们如何代表他人的心理状态奠定了基础。目标2：我们接下来，探索人们如何利用这张地图进行社交预测。我们认为精神状态图不仅编码其他人当前精神状态的位置，还编码他们可能在地图上移动到的位置因此，人们可以做出社会预测。我们将使用功能磁共振成像、大规模经验抽样研究和计算建模行为数据，以确定人们确实自发地模拟他人的精神状态动态，以及此外，这些模型做出了准确的社会预测。在这两个目标中，我们将测试结构和精神状态图的动态变化可以预测社会功能(或功能障碍)。使用个人差异AP- 下一首。通过将他人心理状态的动态建模为路径通过这张地图。我们将把我们的新的结构和动力学测量与参与者在一组社会认知、社会行为和社会关系测量。总而言之，这项提议使用Innova- 技术(例如，新的RSA方法、马尔可夫建模和经验抽样)来开发一种压缩精神状态表征和社会预测的隐含理论。了解这些基础建设社会认知块为评估和改善社会赤字提供了前景看好的未来方向。