Neuro-computational mechanisms of social learning and variation along psychiatric symptom dimensions and in autism

项目摘要 社会学习是获得关于世界的知识和决定如何在社会情况下采取行动的关键。 通过允许个人学习环境中行为的后果，而不必直接 经验，社会学习能力是进化的优势。然而，它们在个体之间差异很大， 在一些与社会功能受损有关的疾病中， 紊乱在美国，自闭症影响了近2%的新生儿，并与其他疾病表现出高度的共病性，如 社交焦虑常见的症状包括社交退缩和社交困难， 并且可能在临床评估和治疗决策期间引起挑战。为了应对这些挑战， 我们需要更好地描述造成社会功能缺陷的机制。这个项目 旨在通过开发一种新的任务电池来评估社会学习的多个方面， 神经计算框架解剖的基本机制，无论是在一般人群中， 与精神病理学相关的症状维度（如社交焦虑），以及自闭症患者。 在该奖项的K99阶段，在加州理工学院John O'Doherty博士的指导下，候选人将 接受高级计算方法（与Yisong Yue博士一起）以及生物和临床 精神病学（与拉尔夫·阿道夫斯博士和杰米·弗斯纳博士）。在目标1中，候选人将建立一个 计算建模框架，以表征三项任务中的社会学习-观察 学习、社会整合和动态信任学习。在目标2中，跨诊断方法将用于 大规模在线研究，将社会学习计算中的个体差异与症状维度联系起来 与精神病理学有关，特别是自闭症特征和社交焦虑。在授予的R00阶段， 候选人将联合收割机结合她的计算和精神病学培训，以调查个人的社会学习 自闭症患者和对照组参与者将完成社会学习任务电池， (Aim 3）和同时进行fMRI（Aim 4）。这将决定任务组合的重测信度， 识别自闭症中改变的社会学习计算以及那些随着相关症状而变化的计算 目标2中确定的尺寸。fMRI的发现将阐明神经计算和功能 与社会学习相关的连接模式及其在自闭症中的改变。使用尖端 计算模型和神经成像方法，这个项目将完善我们对社会的理解。 功能障碍，并将有助于方法和概念创新，以推动新兴领域的 计算精神病学。从长远来看，这种对精神缺陷的计算表征， 为政策和临床干预提供信息以改善社会功能的潜力。通往独立的道路 该奖项将允许候选人达到她的培训和研究目标，形成新的合作，并全面 为成功过渡到独立铺平了道路。

PROJECT SUMMARY Social learning is key for acquiring knowledge about the world and for deciding how to act in social situations. By allowing individuals to learn the consequences of actions in the environment without having to directly experience them, social learning abilities are evolutionary advantageous. Yet they vary widely across individuals, and deficits are common in a number of disorders associated with impaired social function, such autism spectrum disorders. Autism affect nearly 2% of births in the US, and exhibits high comorbidity with other disorders, such as social anxiety. Symptoms in common include social withdrawal and difficulties navigating social interactions, and can cause challenges during clinical assessments and treatment decisions. To address these challenges, we need to provide a better characterization of the mechanisms underlying deficits in social function. This project aims to do so by developing a novel task battery assessing multiple aspects of social learning and an integrative neuro-computational framework dissecting the underlying mechanisms, both in the general population, in relation to symptom dimensions relevant to psychopathology (such as social anxiety), and in individuals with autism. During the K99 phase of the award, under the mentorship of Dr. John O’Doherty at Caltech, the candidate will receive training in advanced computational methods (with Dr. Yisong Yue), and in biological and clinical psychiatry (with Dr. Ralph Adolphs and Dr. Jamie Feusner). In Aim 1, the candidate will establish a hierarchical computational modelling framework to characterize social learning across a battery of three tasks - observational learning, social integration and dynamic trust learning. In Aim 2, a trans-diagnostic approach will be used in a large-scale online study to link individual differences in social learning computations with symptoms dimensions relevant to psychopathology, particularly autistic traits and social anxiety. During the R00 phase of the award, the candidate will combine her computational and psychiatry training to investigate social learning in individuals with autism and matched controls. Participants will complete the social learning task battery both behaviorally (Aim 3) and while undergoing fMRI (Aim 4). This will determine the task battery’s test-retest reliability, and identify social learning computations that are altered in autism and those that vary along relevant symptom dimensions identified in Aim 2. The fMRI findings will illuminate the neural computations and functional connectivity patterns associated with social learning and their alterations in autism. Using cutting-edge computational modelling and neuroimaging methods, this project will refine our understanding of social dysfunction and will contribute methodological and conceptual innovations to advance the burgeoning field of computational psychiatry. In the long term, such computational characterization of psychiatric deficits has the potential to inform policies and clinical interventions to improve social function. This Pathway to Independence award will allow the candidate to reach her training and research goals, form new collaborations, and overall paves the way for a successful transition to independence.