论文标题
通过科学文献的联合嵌入认知科学中的理论和方法：认知控制的示例
Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control
论文作者
论文摘要
传统上，人类领域专家通过文献评论将认知控制的理论和实践联系在一起。但是，这种方法不足以跟踪不断增长的文学作品。它也可能是偏见的，产生了冗余和混乱。 在这里，我们提出了另一种方法。我们对大量科学文本进行了自动文本分析，以创建任务和构造的共同表示。更具体地说，首先使用基于变形金刚的语言模型将385,705个科学摘要首先映射到嵌入式空间中。然后，使用文档嵌入来识别一个任务构成的图形嵌入，该图形嵌入，该图形在任务上构造，并通过利用图表中的约束随机步行来支持构造的细微含义。 可以查询这种联合任务构成图嵌入，以生成针对特定构造的任务电池，可能会揭示文献中的知识差距，并激发新的任务和新假设。
Traditionally, theory and practice of Cognitive Control are linked via literature reviews by human domain experts. This approach, however, is inadequate to track the ever-growing literature. It may also be biased, and yield redundancies and confusion. Here we present an alternative approach. We performed automated text analyses on a large body of scientific texts to create a joint representation of tasks and constructs. More specifically, 385,705 scientific abstracts were first mapped into an embedding space using a transformers-based language model. Document embeddings were then used to identify a task-construct graph embedding that grounds constructs on tasks and supports nuanced meaning of the constructs by taking advantage of constrained random walks in the graph. This joint task-construct graph embedding, can be queried to generate task batteries targeting specific constructs, may reveal knowledge gaps in the literature, and inspire new tasks and novel hypotheses.
