Neural and behavioral mechanisms of abstraction in humans

人类抽象的神经和行为机制

• 批准号: 10237939
• 负责人: Daniel Landay Kimmel
• 金额: $ 19.76万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10237939
• 关键词: Address; Affect; Amygdaloid structure; Animal Experimentation; Animals; Anxiety Disorders; Back; Behavior; Behavioral; Behavioral Mechanisms; Brain; Brain region; Collaborations; Computing Methodologies; Coupled; Cues; Data; Decision Making; Desire for food; Disease; Elements; Emotional; Emotions; Foundations; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; Grant; Health; Hippocampus (Brain); Human; Image; Impairment; Individual Differences; Intelligence; Knowledge; Learning; Link; Manic; Measures; Mental Depression; Mental Processes; Mental disorders; Monkeys; Mood Disorders; Neurons; Outcome; Pathologic; Performance; Population; Post-Traumatic Stress Disorders; Process; Property; Research; Research Personnel; Rewards; Role; Stimulus; Structure; System; Testing; Training; Translating; Variant; Ventral Striatum; Work; anxious; avoidance behavior; base; behavior test; brain behavior; excessive behavior; expectation; experience; flexibility; generalized anxiety; human subject; individual variation; neural circuit; neuromechanism; neurophysiology; neurotransmission; novel; relating to nervous system; response; substance use; tool; trial comparing

Fundamental to human intelligence is the ability to abstract a general rule from prior experiences and then apply this rule to new stimuli so as to infer likely outcomes. The processes of abstraction and inference work in tandem to inform the expectations that drive both behavior (e.g., choosing the best option) and affect (e.g., excitement for a reward). Dysfunction in these processes leads to distorted expectations, and in turn, the maladaptive behavior and emotions that are a hallmark of psychiatric disease. Inferred expectations can be inflated positively, as in substance use and mania, or negatively, as in depression, PTSD and generalized anxiety, leading to avoidance behavior and dysphoric or anxious affect. The critical role of abstraction and inference in healthy and pathological behavior belies our limited understanding of their neural basis. While past work has shown neural representations change with abstract learning (e.g., increased representational similarity), the link between the specific format of neural representation and behavioral function (i.e., inference) remains untested. Moreover, most existing tasks focus on abstract learning from reward, leaving open questions about abstraction during aversive outcomes, which is fundamental to most mental illness. Here we propose to develop a theoretical framework for how the brain represents past stimuli in a format that reflects abstract knowledge and a mechanism for using this structured representation to infer the properties of novel stimuli. This framework will be coupled with a behavioral task in humans that captures the essential elements of real-world abstraction, including appetitive and aversive outcomes, and an analysis approach for fMRI that tests the functional link between the format of representation in the human brain and inference behavior. To fill these gaps, the proposed work in humans leverages recent findings in the monkey showing that populations of single neurons represent stimuli in an abstract format that supports inference. Translating this work will advance understanding of the neural basis of abstraction in humans. I propose to accomplish this with two specific aims. First, I will develop a novel behavioral task in which human subjects learn an implicit rule from prior experience and use this rule to infer rewarded actions during concurrent fMRI. Using a novel computational method, I will test whether the format of representations of experienced stimuli supports inference about unexperienced stimuli. I will further validate the link between brain and behavior by testing the predictions that the neural format emerges with learning and that it explains individual variation in inference. Second, I will compare the roles of appetitive and aversive outcomes on abstract rule learning and on the formation of neural representations that support inference. This work will lay the foundation for studying the neural basis of abstraction in humans and, more generally, will establish a roadmap for linking population-level neural representations in fMRI to behavioral function. Future work would extend this framework to understand the neural basis of pathological inference in mood and anxiety disorders.

人类智能的基础是从先前的经验中抽象出一般规则的能力，然后 将此规则应用于新的刺激，以推断可能的结果。抽象和推理的过程在 串联以告知驱动行为（例如，选择最佳选项）和影响（例如， 奖励的兴奋）。这些过程的功能障碍会导致预期扭曲，反过来， 适应不良的行为和情绪是精神疾病的标志。推断的期望可以是 正向膨胀，如药物滥用和躁狂症，或负向膨胀，如抑郁症、创伤后应激障碍和广义 焦虑，导致回避行为和烦躁或焦虑情绪。抽象和抽象的关键作用 对健康和病理行为的推断掩盖了我们对其神经基础的有限理解。虽然过去 研究表明，神经表征会随着抽象学习而改变（例如，表征性学习的增加） 相似性），神经表征的具体格式和行为功能（即推理）之间的联系 仍未经过测试。此外，大多数现有任务都侧重于从奖励中抽象学习，留下开放的空间 关于厌恶结果期间的抽象问题，这是大多数精神疾病的基础。在这里我们 提议开发一个理论框架来解释大脑如何以反映过去刺激的格式 抽象知识以及使用这种结构化表示来推断小说属性的机制 刺激。该框架将与人类的行为任务相结合，捕捉基本要素 现实世界的抽象，包括食欲和厌恶的结果，以及功能磁共振成像的分析方法 测试人脑表示格式与推理行为之间的功能联系。填补 这些差距，拟议的人类工作利用了猴子的最新发现，表明猴子的种群 单个神经元以支持推理的抽象格式表示刺激。翻译这部作品将 促进对人类抽象神经基础的理解。我建议用两个来完成这个任务 具体目标。首先，我将开发一项新颖的行为任务，其中人类受试者从中学习隐含规则 先前的经验并使用此规则来推断并发 fMRI 期间的奖励行为。使用小说 计算方法，我将测试经验刺激的表示格式是否支持 对未经历过的刺激的推断。我将通过测试来进一步验证大脑和行为之间的联系 预测神经格式会随着学习而出现，并解释推理中的个体差异。 其次，我将比较食欲和厌恶结果在抽象规则学习和 支持推理的神经表征的形成。这项工作将为研究奠定基础 人类抽象的神经基础，更一般地说，将建立一个链接人口水平的路线图 fMRI 中行为功能的神经表征。未来的工作将扩展该框架以理解 情绪和焦虑症病理推理的神经基础。