The Influence of Mental Representations of Social Agents on Social Decision Preferences

该奖项是作为NSF社会、行为和经济学博士后研究奖学金(SPRF)计划的一部分提供的。SPRF计划的目标是为学术界、工业界或私营部门和政府的科学职业生涯培养有前途的、早期职业博士水平的科学家。SPRF奖项包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。国家科学基金会致力于促进科学界所有阶层的科学家参与其研究方案和活动，包括那些来自代表性不足的群体的科学家；博士后阶段被认为是实现这一目标的专业发展的一个重要水平。每个博士后研究员都必须解决推动各自学科领域向前发展的重要科学问题。在加州大学洛杉矶分校卡罗琳·帕金森博士的赞助下，这一博士后奖学金奖项支持一位早期职业科学家，他研究了社会代理人的心理表征如何影响涉及这些人的决策过程。社会决策被定义为具有直接或间接社会后果的决策，在过去的二十年里受到了大量的科学关注。然而，现有的社会决策研究是贫乏的，因为人们对社会决策所涉及的社会目标的特征如何影响决策过程知之甚少。人类代表了关于他人的大量信息，这些信息深刻地塑造了人们的思想和行为，但这些信息--他人的动态心理模型--如何影响决策偏好，目前尚不清楚。事实上，社会认知心理学和神经科学的经典研究表明，表征指导着语境中的行为，这表明心理表征可能在塑造社会决策偏好方面发挥着重要作用。本研究旨在使用计算方法分析功能磁共振图像和文本数据，以(I)探索常见社会决策目标(父母、朋友)的心理表征，以及(Ii)将这些表征的结构特征与社会决策行为联系起来。该项目将为社会决策研究作出重要的理论贡献，并有助于不断努力建立统一和可推广的社会决策模式。值得注意的是，由于社会决策行为具有广泛的影响--从个人幸福感到聚合的社会现象--这个项目可以为未来促进个人和社会适应性社会决策行为的努力提供信息。该项目将使用著名的计算方法来测试现实生活中社会伴侣的心理表征如何根据这些代理人所实现的动机目标和需求来塑造社会决策偏好。通过将功能磁共振成像(FMRI)数据的多变量模式分析与自然语言处理(NLP)和行为计算模型相结合，该项目将全面测量日常社交伙伴的心理表征，并确定它们与社会决策偏好的关系。具体地说，这将涉及(I)使用功能磁共振来探索不同类型社会主体的神经表征，(Ii)确定神经表征重叠如何塑造社会决策偏好，以及(Iii)使用关于社会主体书面内容的NLP工具来帮助识别导致社会伙伴之间社会决策偏好差异的表征内容。这项研究有望为逐步建立和完善统一的社会决策量化模型奠定基础，为未来的基础和应用科学工作提供帮助。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Carolyn Parkinson at the University of California, Los Angeles, this postdoctoral fellowship award supports an early career scientist investigating how mental representation of social agents affect decision processes involving these others. Defined as decisions that have direct or indirect social consequences, social decision-making has received much scientific attention over the past two decades. However, existing social decision-making research is impoverished insofar that very little is known about how the features of social targets implicated in social decision-making influence decision processes. Humans represent a wealth of information about others that profoundly shapes thought and behavior, yet it is unknown how these representations—dynamic mental models of others—impact decision preferences. Indeed, classic work from social cognitive psychology and neuroscience indicates that representations guide behavior in context, suggesting that mental representations likely play an important role in shaping social decision preferences. The current study aims to use computational methods to analyze functional magnetic resonance images and text data to (i) probe mental representations of common social decision-making targets (parents, friends) and (ii) relate structural features of these representations to social decision behavior. This project stands to make important theoretical contributions towards social decision-making research and contribute to ongoing efforts to build unifying and generalizable models of social decision-making. Notably, because social decision-making behavior has widespread impacts—ranging from individual well-being to aggregate societal phenomena—this project could inform future efforts to promote individually and societally adaptive social decision behavior.This project will employ eminent computational methodologies to test how mental representations of real-life social partners shape social decision preferences as a function of the motivational goals and needs fulfilled by said agents. By combining multivariate pattern analysis of functional magnetic resonance imaging (fMRI) data with natural language processing (NLP) and computational models of behavior, this project will comprehensively measure mental representations of everyday social partners and identify their relationship to social decision-making preferences. Concretely, this will involving (i) probing the neural representations of different kinds of social agents using fMRI, (ii) determining how neural representational overlap shapes social decision preferences, and (iii) use NLP tools on written content of social agents to help identify the representational content that drives differences in social decision preferences across social partners. This research will hopefully help lay the groundwork for the gradual establishment and refinement of unifying quantitative models of social decision-making, aiding both basic and applied scientific endeavors in the future.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.