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

项目说明 美德关于计算神经科学合作的研究建议： 用于从计算、神经网络学习的社会知识结构的有效表示 和精神病学观点(RepSocKnow) 美方派：加布里埃拉·罗森布劳教授，博士，乔治大学心理系助理教授 华盛顿大学，华盛顿特区西北G街2115号，邮编：20052 德国方面：Christoph W.Korn教授，博士，助理教授兼Pi Emmy-Noether Group，分部 社会神经科学，普通精神病学系，海德堡大学，Vossstra？e 4,69115 海德堡，德国 顾问1：Daniela Schiller教授，博士，精神病学系副教授 神经科学和弗里德曼脑研究所，西奈山伊坎医学院，1470麦迪逊 纽约大道，邮编：10029，NY 顾问2：Jan Gläscher博士，皮伯恩斯坦研究小组，系统神经科学研究所， 汉堡-埃彭多夫大学医学中心，马提尼斯特拉斯52,20246汉堡，德国 1.目标和假设 这是我们去年CRCNS提案的重新提交，该提案从以下方面获得了良好和非常好的分数 评论者。评论家们对建立在 PIs的互补技能。在这一版本中，我们满足了审查者对更清晰描述的要求 计划中的实验和分析。重要的是，我们的新建议与临床有直接关系 练习一下。通过利用计算精神病学[1-4]和研究领域标准中的思想 (RDoC；例如，[5，6])，我们的目标是应用我们的神经计算方法来提高对 许多普遍存在的神经精神障碍所共有的核心社会缺陷。这项提案的总体目标是 是建立全面的和临床相关的异常学习的神经计算模型 在社交环境中，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 预测幸福[8]。不同类型的社会功能障碍伴随着不同的临床 自闭症谱系障碍(ASD)的条件和构成[9-11]与人格 边界模式限定词(BPD)的障碍[12-16]。我们利用我们的神经计算方法 调查社会知识结构是如何形成的--反过来又是如何形成的--学习他人。 我们在计算中提出的知识表示和学习的基本机制 建模方法本身并不具有社会性，我们认为它是一种可以应用于学习的优势 跨越各种(非)社交领域。 在这里，我们将重点放在社交学习上，以明确健康与健康之间的共性和差异 与自闭症或BPD相关的明显社会缺陷的个人和个人。而这两个人 临床小组可能都采用了过于僵化的社会知识结构，他们表现出不同的类型 功能障碍：自闭症的特点是精神化不足，即对精神障碍的推断不足 其他国家--可能是由于缺乏或不具体的社会知识[17-19]。相比之下，BPD显示- 对他人的个性或意图进行意念化和过度负面的解释[12，13]。我们的小说 神经计算框架可以沿维度提高对社会功能障碍的理解 ASD和BPD的分类精神病学标准。 我们的目标是测试和改进计算模型，使适当的收购策略正式化 在学习中运用社会知识结构。我们的模型提供了关于以下方面的规范观点 通过指定理想的代理人如何在我们受控但在生态上有效的任务中学习来进行社会学习。 因此，我们可以量化有多少人类-特别是临床人口的特征是无处不在 社会赤字--偏离通过计算确定的“最佳”基准策略。同时， 这个项目旨在揭示社会知识表征背后的神经计算。 结构及其在学习过程中的灵活部署。该项目涉及三个具体目标：

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: