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 /摘要导航社会世界要求人们预测别人的行为。这构成了一个重大挑战 - 因为人们无法直接观察一些最佳行动预测指标：其他人的内部精神状态。在- 可以通过代表精神国家 - 可以更好地预测这些行动，并更成功地在社会世界中浏览。人们如何（i）代表他人无形的精神状态的丰富性和复杂性，（ii）使用这些表示形式做社会预测？我们建议人们通过参加他们的精神状态位置在精神状态“地图”中的一些关键维度上。我们以前已经使用了rep- 对功能神经影像学（fMRI）数据的态度相似性分析（RSA），以表明人们确实使用简单的低维地图代表他人的心理状态。该地图的结构由三个维度 - 理性，社会影响和价。了解人们采用这张地图将如何提供有关人们如何预测别人行为的关键见解。目的1：该提案旨在开发一个通过首先表征精神地图的结构，是精神状态表示的Hansive框架国家。我们将通过评估其如何在新的社会环境和方式上适应结构来完善结构。我们使用fMRI数据的新型RSA方法，将测量地图的两个结构特征 - 尺寸和形状。 d 具有普遍社会功能的男性应在所有情况和方式上保持稳定的形状；具有特定或上下文函数的维度应在上下文或模态上变形。大小该空间应扩展以反映目标的社会相关性。了解人的结构 - TAL州地图为了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做出社会预测。我们建议精神状态图不仅编码他人当前精神状态的位置，而且编码在地图中的位置，它们可能会移至人们可以做出社会预测我们将使用fMRI，大规模的体验抽样研究和计算建模超越行为数据，以确定人们确实赞助建模他人的心理状态动态，并且此外，这些模型做出了准确的社会预测。在这两个目标中，我们都将测试结构和精神状态图的动态预测社会功能（或功能障碍）。使用个体差异下一个。通过将他人心理状态的动态建模为道路通过此地图。 proach，我们将将我们的新颖结构和动态措施与参与者在一组电池中的表现联系起来社会认知，社会行为和社会关系措施。总之，这个建议使用Innova- Tive技术（例如，新型的RSA方法，Markov建模和体验抽样），以开发一个综合 hasive的精神状态表征和社会预测理论。了解这些基本建筑社会认知障碍有保证未来评估和改善社会缺陷的方向。