CRCNS US-German Research Proposal: Efficient representations of social knowledge structures for learning from a computational, neural and psychiatric perspective (RepSocKnow)

CRCNS 美德研究提案：从计算、神经和精神病学角度学习的社会知识结构的有效表示 (RepSocKnow)

• 批准号: 10688109
• 负责人: Gabriela Rosenblau
• 金额: $ 16.23万
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-22 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10688109
• 关键词: Academic Medical Centers; Address; Behavior; Behavioral; Benchmarking; Brain; Calibration; Categories; Cells; Clinical; Code; Cognitive; Collaborations; Computer Models; Decision Making; Dimensions; Disease; Doctor of Philosophy; Dorsal; Evaluation; Exhibits; Functional Magnetic Resonance Imaging; German population; Germany; Goals; Grain; Human; Impairment; Individual; Individual Adjustment; Knowledge; Learning; Medial; Modeling; National Institute of Mental Health; Neurons; Neurosciences; New York; Pattern; Personal Satisfaction; Personality; Personality Disorders; Population; Prefrontal Cortex; Probability; Psychiatry; Psychology; Research; Research Domain Criteria; Research Proposals; Shapes; Side; Signal Transduction; Site; Social Adjustment; Social Environment; Social Functioning; Social Interaction; Specific qualifier value; Stereotyping; Structure; Symptoms; System; Testing; Uncertainty; Universities; Washington; autism spectrum disorder; clinical practice; clinically relevant; cognitive rigidity; computational neuroscience; computer framework; expectation; experimental study; flexibility; improved; individuals with autism spectrum disorder; interest; learning strategy; medical schools; mental state; mentalization; mid-career faculty; neural; neuropsychiatric disorder; novel; preference; professor; programs; recruit; skills; social; social deficits; social learning; social neuroscience; social space; symptomatology

PROJECT DESCRIPTION US-German Research Proposal for Collaboration in Computational Neuroscience: Efficient representations of social knowledge structures for learning from a computational, neural and psychiatric perspective (RepSocKnow) US-side PI: Prof. Gabriela Rosenblau, Ph.D., Assistant Professor, Department of Psychology, The George Washington University, 2115 G Street NW, Washington, DC 20052 German-side PI: Prof. Christoph W. Korn, Ph.D., Assistant Professor & PI Emmy-Noether Group, Section Social Neuroscience, Department of General Psychiatry, University of Heidelberg, Vossstraße 4, 69115 Heidelberg, Germany Consultant 1: Prof. Daniela Schiller, Ph.D., Associate Professor, Department of Psychiatry, Department of Neuroscience, and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York 10029, NY Consultant 2: Jan Gläscher, Ph.D., PI Bernstein Research Group, Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany 1. Aims and hypotheses This is a resubmission of our last year's CRCNS proposal, which received good and very good scores from reviewers. Reviewers were excited about the general neuro-computational approach that builds on the complementary skills of the PIs. In this revision, we address the reviewers' requests for clearer descriptions of the planed experiments and analyses. Importantly, our new proposal has direct relevance for clinical practice. By leveraging ideas from computational psychiatry [1–4] and from the Research Domain Criteria (RDoC; e.g., [5, 6]), we aim to apply our neuro-computational approach to improve the understanding of core social deficits shared by many pervasive neuro-psychiatric disorders. The general goal of this proposal is to establish comprehensive—and clinically relevant—neuro-computational models of aberrant learning in social contexts via behavioral and functional magnetic resonance imaging (fMRI) experiments. Learning about others is crucial for successful social interactions [7]. Social interactions strongly predict wellbeing [8]. Different types of impairments in social functioning accompany a variety of clinical conditions and constitute core symptoms of Autism Spectrum Disorders (ASD) [9–11] and Personality Disorders with a Borderline pattern qualifier (BPD) [12–16]. We harness our neuro-computational approach to investigate how social knowledge structures shape—and in turn are shaped by—learning about others. The mechanisms underlying knowledge representations and learning that we propose in our computational modeling approach are not “social” per se and we deem it a strength that they can be applied to learning across various (non-)social domains. Here, we focus on social learning to specify commonalities and differences between healthy individuals and individuals with marked social deficits associated with ASD or BPD. While these two clinical groups probably both employ overly rigid social knowledge structures, they exhibit different types of malfunctioning: ASD are characterized by under-mentalizing, i.e., insufficient inferences about mental states of others—possibly due to poor or unspecific social knowledge[17–19]. In contrast, BPD show over- mentalizing and overly negative interpretations of others' personality or intentions[12, 13]. Our novel neuro-computational framework can improve the understanding social malfunctioning along dimensional and categorical psychiatric criteria of ASD and BPD. We aim to test and refine computational models that formalize adequate strategies for acquiring and employing social knowledge structures during learning. Our models offer normative perspectives on social learning by specifying how ideal agents could learn in our controlled but ecologically valid tasks. Thereby, we can quantify how much humans—particularly clinical populations characterized by pervasive social deficits—deviate from computationally specified “optimal” benchmark strategies. In conjunction, this project aims to reveal the neural computations underlying the representation of social knowledge structures and their flexible deployment during learning. The project addresses three specific aims:

项目描述 美国 - 德国计算神经科学合作的研究建议： 社会知识结构的有效表示，用于从计算，神经学习 和精神科视角（Repsocknow） 美国PI：George心理学系助理教授Gabriela Rosenblau教授 华盛顿大学，2115 G街西北，华盛顿特区，20052年 德国人PI：Christoph W. Korn教授，博士，助理教授和PI Emmy-Noether Group，部分 海德堡大学普通精神病学系社会神经科学，沃斯特拉斯大学4，69115 德国海德堡 顾问1：Daniela Schiller教授，博士，精神病学系副教授，系 神经科学和弗里德曼脑研究所，西奈山伊坎医学院，1470年麦迪逊 大街，纽约10029，纽约 顾问2：JanGläscher博士，PI Bernstein研究小组，系统神经科学研究所 大学医学中心汉堡 - 埃潘多夫，德国汉堡52号Martinistrasse 52 1。目标和假设 这是我们去年CRCNS提案的重新提交，该提案获得了良好的得分 评论者。审稿人对基于该方法的一般神经计算方法感到兴奋 PIS的完全技能。在此修订中，我们解决了审阅者的要求更清晰的描述请求 重要的是，我们的新建议与临床有直接相关 实践。通过利用计算精神病学[1-4]和研究领域标准的思想 （RDOC;例如[5，6]），我们旨在应用我们的神经计算方法来提高对 许多普遍的神经精神疾病共享的核心社会缺陷。该提议的一般目标 是为了建立综合且在临床上相关的NEURO计算模型异常学习模型 在社会环境中，通过行为和功能磁共振成像（fMRI）实验。 了解他人对于成功的社会互动至关重要[7]。社交互动强烈 预测健康[8]。社会功能住宿中不同类型的损害各种临床 疾病和构成自闭症谱系障碍的核心症状（ASD）[9-11]和个性 具有边界模式预选赛（BPD）的疾病[12-16]。我们利用我们的神经计算方法 调查社会知识结构如何形成 - 而又是由他人塑造的。 我们在计算中提出的知识表征和学习的基础机制 建模方法本身不是“社会”，我们认为它们可以应用于学习的优势 在各种（非）社会领域。 在这里，我们专注于社会学习，以指定健康之间的共同点和差异 与ASD或BPD相关的具有明显社会缺陷的个人和个人。而这两个 临床组可能都采用过度严格的社会知识结构，他们暴露了不同的类型 出现故障：ASD的特征是不足，即对心态的推断不足 他人的状态 - 可能是由于贫穷或特定的社会知识[17-19]。相比之下，BPD显示过多 对他人的性格或意图的心理和过于负面解释[12，13]。我们的小说 神经计算框架可以改善沿维度的社会故障的理解 和ASD和BPD的分类精神病标准。 我们旨在测试和完善计算模型，以正式化适当的策略 并在学习过程中采用社会知识结构。我们的模型提供了正常的观点 通过指定理想代理如何在我们受控但生态有效的任务中学习的社会学习。 因此，我们可以量化多少人 - 尤其是以普遍性为特征的临床人群 社交定义 - 从计算指定的“最佳”基准策略中删除。结合 该项目旨在揭示社会知识表示的神经计算 在学习过程中结构及其灵活的部署。该项目针对三个具体目标：