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.

摘要 抽象的过程包括识别过去经验中共有的特征，以便表示 只使用少量变量的复杂环境。抽象消除了表示所有 所有特征的值的组合，并且能够泛化到新的环境。这种概括是 这是快速灵活地调整行为、认知和情绪反应的基础。然而，它重新- 主要是未知的人类大脑如何学会代表过去的经验，以反映他们的共同特点， 也不知道这个过程是如何被不同的学习时间尺度所调节的，记忆巩固- 抽象的多个层次和动机状态。为了回答这些问题，我们对人类功能磁共振成像进行了调整， 一个理论框架和分析方法，从最近的工作在非人类灵长类动物。健康人 受试者学习一个复杂的反向学习任务，其中多个刺激由一个隐藏的结构连接， 由少量变量表示。在试点数据中，受试者学习了这种结构，他们展示了 通过推断：一个刺激的变化足以推断其余刺激的新值。我们分析 多体素功能磁共振成像活动，以探测神经表征之间的关系，或几何，我们 测试“抽象格式”，即，一种格式，能够泛化，以及量化其维度，或 表示大量（非抽象）变量的能力。在目标1中，我们探索了神经元的进化， 在多个时间尺度的学习过程中，从几个小时到一周，提供机械的洞察力， 抽象表现的形成和巩固。我们预测：（1）抽象格式将出现 首先对于“显式”变量（例如，反应和结果）与感觉运动处理相关的区域。（二） 经过多天的训练和巩固，我们预测由任务时间定义的“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，内侧 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。 Aim 2比较了不同的抽象级别，从识别特定实例之间的共享特性到 一般状态的系统，可以转移到新的问题。我们将神经几何学和大脑 区域（例如，海马与内嗅皮层）支持这些不同的水平。目标3：研究角色 欲望与厌恶的结果，这深刻地影响决策和学习，但他们的不同 抽象中的角色是未知的。这种差距是惊人的，因为许多精神疾病涉及受损的 抽象和概括与令人厌恶的经历有关。为了弥补这一差距，受试者进行交替 在增益或损失域下的任务的版本。我们将测试动机效价如何影响抽象学习 和神经几何学在所有的目标中，我们将行为和情感处理的个体差异与 神经几何结构的差异。展望未来，该框架为连接神经几何学提供了基础 认知和情感功能，广泛适用于认知神经科学和精神病理学。