Prior work has shown that the brain represents memories within a cognitive map that supports inference about connections between individual related events. Real-world adaptive behavior is also supported by recognizing common structure among numerous distinct contexts; for example, based on prior experience with restaurants, when visiting a new restaurant one can expect to first get a table, then order, eat, and finally pay the bill. We used a neurocomputational approach to examine how the brain extracts and uses abstract representations of common structure to support novel decisions. Participants learned image pairs (AB, BC) drawn from distinct triads (ABC) that shared the same internal structure and were then tested on their ability to infer indirect (AC) associations. We found that hippocampal and frontoparietal regions formed abstract representations that coded cross-triad relationships with a common geometric structure. Critically, such common representational geometries were formed despite the lack of explicit reinforcement to do so. Furthermore, we found that representations in parahippocampal cortex are hierarchical, reflecting both cross-triad relationships and distinctions between triads. We propose that representations with common geometric structure provide a vector space that codes inferred item relationships with a direction vector that is consistent across triads, thus supporting faster inference. Using computational modeling of response time data, we found evidence for dissociable vector-based retrieval and pattern-completion processes that contribute to successful inference. Moreover, we found evidence that these processes are mediated by distinct regions, with pattern completion supported by hippocampus and vector-based retrieval supported by parahippocampal cortex and lateral parietal cortex.

先前的研究表明，大脑代表认知图谱中的记忆，该记忆图谱支持推断各个相关事件之间的联系。现实世界中的自适应行为还可以通过识别众多不同上下文之间的通用结构来支持。例如，根据与餐厅的先验经验，当访问新餐厅时，可以期望先获得一张桌子，然后点餐，用餐，最后支付账单。我们使用一种神经计算方法来检查大脑如何提取和使用通用结构的抽象表示来支持新的决策。参与者从共享相同内部结构的不同三合会（ABC）中学习了图像对（AB，BC），然后测试了它们推断间接（AC）关联的能力。我们发现，海马区和额顶区形成了抽象表示，该抽象表示编码具有共同几何结构的交叉三元关系。至关重要的是，尽管缺乏显式的增强，但仍形成了这种常见的表示几何。此外，我们发现海马旁皮层中的表示是分层的，既反映了跨三人关系，又反映了三人之间的区别。我们建议具有共同几何结构的表示提供一个向量空间，该向量空间使用跨三合会一致的方向向量对推断的项目关系进行编码，从而支持更快的推断。使用响应时间数据的计算模型，我们发现了基于可分离向量的检索和模式完成过程的证据，这些过程有助于成功进行推理。此外，