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)

基本信息

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
项目类别：
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

项目摘要

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:

项目描述美德计算神经科学合作研究提案：社会知识结构的有效表示，用于从计算、神经网络中学习和精神病学观点 (RepSocKnow) 美方PI：Gabriela Rosenblau教授，博士，乔治大学心理学系助理教授华盛顿大学，2115 G Street NW，华盛顿，DC 20052 德方 PI：Christoph W. Korn 教授，博士，助理教授兼 PI Emmy-Noether Group，Section 社会神经科学，普通精神病学系，海德堡大学，Vossstraße 4, 69115 德国海德堡顾问1：Daniela Schiller教授，博士，精神病学系副教授，系神经科学系和弗里德曼大脑研究所，西奈山伊坎医学院，1470 Madison 纽约大道 10029, NY 顾问 2：Jan Gläscher，博士，PI Bernstein 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，Martinistrasse 52, 20246 汉堡，德国 1. 目的和假设这是我们去年的 CRCNS 提案的重新提交，该提案获得了良好且非常好的分数审稿人对建立在该模型之上的通用神经计算方法感到兴奋。 PI 的互补技能在本次修订中，我们满足了审稿人对更清晰描述的要求。重要的是，我们的新提案与临床直接相关。通过利用计算精神病学 [1-4] 和研究领域标准的想法。（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的神经精神疾病共有的核心社会缺陷该提案的总体目标。是建立全面的、临床相关的异常学习神经计算模型通过行为和功能磁共振成像（fMRI）实验在社会环境中进行研究。了解他人对于成功的社交互动至关重要[7]。预测健康状况[8]。不同类型的社会功能障碍伴随着各种临床症状。自闭症谱系障碍 (ASD) [9-11] 和人格的条件和构成核心症状具有边界模式限定符（BPD）的疾病[12-16]。研究社会知识结构如何塑造——以及反过来如何塑造——了解他人。我们在计算中提出的知识表示和学习的机制建模方法本身并不“社交”，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，我们重点关注社会学习，以明确健康人群之间的共性和差异。个人和具有与自闭症谱系障碍 (ASD) 或边缘性人格障碍 (BPD) 相关的明显社交缺陷的个人。临床群体可能都采用过于严格的社会知识结构，他们表现出不同的类型功能障碍：自闭症谱系障碍的特点是心智化不足，即对心理的推断不足他人的状态——可能是由于缺乏或不具体的社会知识[17-19]。对他人个性或意图的心理化和过度消极的解释[12, 13]。神经计算框架可以提高对维度上社会功能障碍的理解以及 ASD 和 BPD 的分类精神病学标准。我们的目标是测试和完善计算模型，以形式化获取足够的策略在学习过程中运用社会知识结构我们的模型提供了规范的观点。通过指定理想主体如何在我们受控但生态有效的任务中学习来进行社会学习。因此，我们可以量化人类，特别是以普遍存在的临床人群社会赤字——偏离计算指定的“最佳”基准策略。该项目旨在揭示社会知识表征背后的神经计算该项目解决了三个具体目标：