The geometry of neural representations reflecting abstraction in humans

摘要 抽象的过程包括识别过去经验中共享的特征，以便表示 仅使用少量变量的复杂环境。抽象消除了表示所有 所有功能的值组合，并支持对新环境的泛化。这样的概括是 行为、认知和情绪反应的快速、灵活调整的基础。然而，它重新- 主要未知人脑是如何学会代表过去的经验来反映它们的共同特征和 实现概括，也不知道这个过程是如何被不同的学习、记忆巩固的时间尺度所调制的- 抽象、多层次抽象和激励状态。为了回答这些问题，我们采用了人类fmri。 非人灵长类动物最新研究的理论框架和分析方法。健康人 受试者学习一项复杂的反转学习任务，其中多个刺激通过一个隐藏的结构联系在一起，可以 由一小部分变量表示。在试点数据中，受试者学习这种结构，他们展示了这种结构 通过推断：一个刺激物的变化足以推断其余刺激物的新值。我们分析 多体素功能磁共振成像活动，以探测神经表示之间的关系，或几何，我们 测试“抽象格式”，即能够进行泛化并量化其维度的格式，或者 表示大量(非抽象)变量的能力。在目标1中，我们探索了神经的进化。 在多个时间尺度(从几个小时到一周)的学习过程中进行陈述，以提供对 抽象表象的形成和巩固。我们预测：(1)抽象格式将会出现 首先是在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) 在经过多天的训练和巩固后，我们预测由任务的时态定义的“隐藏”变量 统计数据将以抽象格式表示，首先通过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比较了不同级别的抽象，从识别跨特定实例的共享功能到 可以转化为新问题的一般状态系统。我们比较了神经几何学和大脑 支持这些不同水平的区域(例如，海马体与内嗅觉皮质)。目标3调查角色 欲望与厌恶的结果，这些结果深刻地影响着决策和学习，但它们的独特之处在于 抽象中的角色是未知的。考虑到许多精神障碍涉及受损的人，这一差距是惊人的 抽象和概括与令人厌恶的经验有关。为了弥补这一差距，受试者轮流表演 增益域或损失域下的任务版本。我们将测试动机效价如何影响抽象学习 和神经几何学。在所有目标中，我们将行为和情感处理的个体差异与 神经几何学上的差异。展望未来，该框架为连接神经几何学提供了基础 对认知神经科学和精神病理学具有广泛适用性的认知和情绪功能。

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.