Center for Learning & Memory, The University of Texas at Austin, Austin, TX 78712.
Department of Psychology, University of Toronto, Toronto, ON M5S 3G3, Canada.
Proc Natl Acad Sci U S A. 2020 Nov 24;117(47):29338-29345. doi: 10.1073/pnas.1912338117.
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，大脑在认知图中表示记忆，这支持了对个体相关事件之间连接的推断。现实世界中的适应性行为也得益于识别众多不同情境中的共同结构；例如，根据在餐厅的先前经验，当访问一家新餐厅时，可以预期首先会有一张桌子，然后点餐、用餐，最后付账。我们使用神经计算方法来研究大脑如何提取和使用共同结构的抽象表示来支持新的决策。参与者学习从不同的三合体（ABC）中抽取的图像对（AB、BC），这些三合体共享相同的内部结构，然后测试他们推断间接（AC）关联的能力。我们发现海马体和额顶叶区域形成了抽象的表示，这些表示编码了具有共同几何结构的跨三合体关系。关键是，尽管缺乏明确的强化，仍然形成了这种共同的代表性几何结构。此外，我们发现海马旁皮质中的表示是分层的，反映了跨三合体关系和三合体之间的区别。我们提出，具有共同几何结构的表示提供了一个向量空间，该空间使用方向向量对推断的项目关系进行编码，该方向向量在三合体之间是一致的，从而支持更快的推断。使用对反应时间数据的计算建模，我们发现了分离的基于向量的检索和模式完成过程的证据，这些过程有助于成功的推断。此外，我们发现这些过程由不同的区域介导，模式完成由海马体支持，基于向量的检索由海马旁皮质和外侧顶叶皮质支持。
