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.

本文采用了自动化文本分析的方法，创建一种联合任务 - 构建图嵌入表示，可以用于构建针对特定构建的任务电池，在文献中发现知识缺口并激发新的任务和新的假设。