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.
|Original language||English (US)|
|Number of pages||7|
|State||Published - 2022|
|Event||44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022 - Toronto, Canada|
Duration: Jul 27 2022 → Jul 30 2022
|Conference||44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022|
|Period||7/27/22 → 7/30/22|
Bibliographical noteFunding Information:
This research was supported by the Luxembourg National Research Fund (ATTRACT/2016/ID/11242114/DIGILEARN) and (INTER Mobility/2017-2/ID/11765868/ULALA).
© 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)
- Cognitive Constructs
- Cognitive Control
- Cognitive Tasks
- Natural Language Processing