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; 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经验中抽象一般规则的能力，然后 将此规则应用于新的刺激中，以推断可能的结果。抽象和推理工作的过程 串联通知驱动行为的期望（例如，选择最佳选择）和影响（例如， 兴奋以获得奖励）。这些过程中的功能障碍会导致期望扭曲，然后 适应不良的行为和情绪是精神病的标志。推断的期望可能是 像在抑郁症，PTSD和广义上一样，正如药物使用和躁狂一样积极地膨胀 焦虑，导致回避行为和烦躁或焦虑的影响。抽象的关键作用和 对健康和病理行为的推论掩盖了我们对神经基础的有限理解。过去 工作表明神经表示随抽象学习的变化（例如，代表性增加 相似性），神经表示的特定格式与行为函数（即推论）之间的联系 保持未经测试。此外，大多数现有的任务都集中在奖励中的抽象学习上 关于厌恶结果的抽象问题，这是大多数精神疾病的基础。我们在这里 建议开发一个理论框架，以反映大脑如何以一种反映的格式代表过去的刺激 抽象知识和使用这种结构化表示的机制来推断新颖的特性 刺激。该框架将与人类中的行为任务相结合，以捕获基本要素 现实世界中的抽象，包括食欲和厌恶结果，以及对fMRI的分析方法 测试人脑中代表形式与推理行为的功能联系。填充 这些差距，人类的拟议工作利用了猴子的最新发现，表明 单神经元以抽象格式表示支持推理的刺激。翻译这项工作将 提前了解人类抽象的神经基础。我建议用两个 具体目标。首先，我将制定一项新颖的行为任务，其中人类受试者从中学到一个隐性规则 事先经验并使用此规则来推断同并发fMRI期间的奖励行动。使用小说 计算方法，我将测试是否有经验的刺激支持形式的形式 关于未经验的刺激的推断。我将通过测试大脑和行为之间的联系 神经格式随着学习而出现的预测，并解释了推理的个体变化。 其次，我将比较在抽象规则学习中的食欲和厌恶结果以及在 支持推理的神经表示形成。这项工作将为研究 人类抽象的神经基础，更普遍地将建立联系人口级别的路线图 fMRI到行为函数中的神经表示。未来的工作将扩展此框架以了解 情绪和焦虑症病理学的神经基础。