The geometry of neural representations reflecting abstraction in humans

反映人类抽象的神经表征的几何形状

• 批准号: 10682315
• 负责人: C. DANIEL SALZMAN
• 金额: $ 65.46万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10682315
• 关键词: Address; Affective; Amygdaloid structure; Area; Behavior; Behavioral; Behavioral Mechanisms; Brain; Brain region; Categories; Cognitive; Complex; Corpus striatum structure; Cues; Data; Decision Making; Desire for food; Dimensions; Disease; Dorsal; Emotional; Environment; Evolution; Foundations; Functional Magnetic Resonance Imaging; Geometry; Grouping; Health; Hippocampus; Hour; Human; Impairment; Individual Differences; Knowledge; Learning; Link; Maps; Measures; Medial; Mental disorders; Methodology; Motivation; Motor Cortex; Outcome; Output; Performance; Population; Prefrontal Cortex; Process; Psychopathology; Response to stimulus physiology; Reversal Learning; Rewards; Role; Stimulus; Structure; System; Temporal Lobe; Testing; Training; Visual Cortex; Work; behavior prediction; cognitive function; cognitive neuroscience; emotion regulation; emotional functioning; entorhinal cortex; experience; experimental study; flexibility; human subject; individual variation; insight; memory consolidation; neural; neural patterning; neuromechanism; nonhuman primate; novel; performance tests; response; statistics; transfer learning

ABSTRACT The process of abstraction involves identifying the features shared across past experiences so as to represent a complex environment using only a small number of variables. Abstraction obviates the need to represent all combinations of values for all features and enables generalization to novel environments. Such generalization is fundamental to rapid, flexible adjustments in behavioral, cognitive, and emotional responses. However, it re- mains unknown how the human brain learns to represent past experiences to reflect their shared features and enable generalization, nor how this process is modulated by different timescales of learning, memory consolida- tion, multiple levels of abstraction, and motivational states. To answer these questions, we adapt for human fMRI a theoretical framework and analytic methodology from recent work in non-human primates. Healthy human subjects learn a complex reversal-learning task with multiple stimuli linked by a hidden structure that can be represented by a small number of variables. In pilot data, subjects learn this structure, which they demonstrate via inference: a change in one stimulus is sufficient to infer the new values for the remaining stimuli. We analyze multivoxel fMRI activity to probe for the relationships, or geometry, between neural representations, which we test for an ‘abstract format’, i.e., a format that enables generalization, as well as quantify its dimensionality, or capacity to represent a large number of (non-abstract) variables. In Aim 1, we probe the evolution of neural representations during learning at multiple timescales, from hours to a week, to provide mechanistic insight into the formation and consolidation of abstract representations. We predict that (1) the abstract format will emerge first for ‘explicit’ variables (e.g., response and outcome) in regions associated with sensorimotor processing. (2) After multi-day training and consolidation, we predict that ‘hidden’ variables defined by the task’s temporal statistics will be represented in an abstract format, first by regions that encode relational knowledge (e.g., medial temporal lobe), which then relay this information to prefrontal regions that encode abstract rules and task states. Aim 2 compares different levels of abstraction, from identifying shared features across specific instances to a system of general states that can be transferred to novel problems. We compare the neural geometry and brain regions (e.g., hippocampus vs. entorhinal cortex) that support these distinct levels. Aim 3 investigates the role of appetitive vs. aversive outcomes, which profoundly influence decision-making and learning, but their distinct roles in abstraction are unknown. This gap is striking given that many psychiatric disorders involve impaired abstraction and generalization tied to aversive experiences. To address this gap, subjects perform alternating versions of the task under gain or loss domains. We will test how motivational valence impacts abstract learning and neural geometry. In all Aims, we relate individual differences in behavior and affective processing to differences in neural geometry. Going forward, the framework provides a foundation for linking neural geometry to cognitive and emotional function with broad applicability to cognitive neuroscience and psychopathology.

抽象的 抽象的过程涉及识别过去经验中共有的特征，以表示 仅使用少量变量的复杂环境无需表示所有变量。 所有特征的值的组合并能够泛化到新的环境。 然而，它是快速、灵活地调整行为、认知和情绪反应的基础。 主要未知人类大脑如何学习代表过去的经历以反映他们共同的特征和 实现泛化，也不知道这个过程是如何通过不同的学习时间尺度、记忆巩固来调节的 为了回答这些问题，我们采用了人类功能磁共振成像。 来自非人类灵长类动物最近研究的理论框架和分析方法。 受试者学习复杂的逆向学习任务，其中包含通过隐藏结构连接的多个刺激，该隐藏结构可以 在试点数据中，受试者学习并展示了这种结构。 通过推理：一个刺激的变化足以推断出其余刺激的新值。 多体素功能磁共振成像活动，用于探测神经表征之间的关系或几何形状，我们 测试“抽象格式”，即能够泛化并量化其维度的格式，或 表示大量（非抽象）变量的能力在目标 1 中，我们探讨了神经网络的进化。 在学习过程中以多​​个时间尺度（从几小时到一周）进行表征，以提供机械洞察力 我们预测，（1）抽象形式将会出现。 首先是与感觉运动处理相关的区域中的“显性”变量（例如反应和结果）（2）。 经过多天的训练和巩固后，我们预测由任务的时间定义的“隐藏”变量 统计数据将以抽象格式表示，首先由编码关系知识的区域（例如，医学 颞叶），然后将这些信息传递到编码抽象规则和任务状态的前额叶区域。 目标 2 比较不同的抽象级别，从识别特定实例之间的共享特征到 可以转移到新问题的一般状态系统我们比较了神经几何和大脑。 目标 3 研究了支持这些不同水平的区域（例如海马体与内嗅皮层）的作用。 食欲与厌恶的结果，深刻地影响决策和学习，但它们截然不同 鉴于许多精神疾病都涉及受损，这种差距是惊人的。 为了解决这一差距，受试者交替进行抽象和概括。 我们将测试动机效价如何影响抽象学习。 在所有目标中，我们将行为和情感处理的个体差异联系起来。 展望未来，该框架为链接神经几何提供了基础。 认知和情感功能，广泛适用于认知神经科学和精神病理学。