The Fluidity of Concept Representations in Human Brain Signals
This work addresses the challenge of modeling human language processing for cognitive science by proposing fluid concept representations to better capture ambiguity in natural language, though it is incremental in refining existing theories.
The study tackled the problem of distinguishing concrete and abstract concepts in fMRI data, finding that while the distinction can be decoded above chance, it does not serve as a significant structuring factor in clustering and relational analyses, suggesting concept representations are more fluid than dichotomous categories.
Cognitive theories of human language processing often distinguish between concrete and abstract concepts. In this work, we analyze the discriminability of concrete and abstract concepts in fMRI data using a range of analysis methods. We find that the distinction can be decoded from the signal with an accuracy significantly above chance, but it is not found to be a relevant structuring factor in clustering and relational analyses. From our detailed comparison, we obtain the impression that human concept representations are more fluid than dichotomous categories can capture. We argue that fluid concept representations lead to more realistic models of human language processing because they better capture the ambiguity and underspecification present in natural language use.