The curse of dimensionality plagues models of reinforcement learning and decision making. The process of abstraction solves this by constructing variables describing features shared by different instances, reducing dimensionality and enabling generalization in novel situations. Here, we characterized neural representations in monkeys performing a task described by different hidden and explicit variables. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training, which requires a particular geometry of neural representations. Neural ensembles in prefrontal cortex, hippocampus, and simulated neural networks simultaneously represented multiple variables in a geometry reflecting abstraction but that still allowed a linear classifier to decode a large number of other variables (high shattering dimensionality). Furthermore, this geometry changed in relation to task events and performance. These findings elucidate how the brain and artificial systems represent variables in an abstract format while preserving the advantages conferred by high shattering dimensionality.

维度诅咒困扰着强化学习和决策模型。抽象过程通过构建描述不同实例共享的特征的变量、降低维度并在新情况下实现泛化来解决这个问题。在这里，我们表征了执行由不同隐藏和显式变量描述的任务的猴子的神经表征。抽象是使用神经解码器在未用于训练的任务条件下的泛化性能在操作上定义的，这需要特定的神经表示几何结构。前额叶皮层、海马、和模拟神经网络同时表示几何中反映抽象的多个变量，但这仍然允许线性分类器解码大量其他变量（高破碎维度）。此外，这种几何形状与任务事件和性能有关。这些发现阐明了大脑和人工系统如何以抽象格式表示变量，同时保留高维数赋予的优势。