ArticlePLoS computational biology2022
Modelling brain representations of abstract concepts.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Resolving the Abstract-Concrete Paradox in the Angular Gyrus: A Multimethod Investigation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- fruit-SALAD: A Style Aligned Artwork Dataset to reveal similarity perception in image embeddings.Scientific data · 2025Article
- Reconstructing Spatiotemporal Trajectories of Visual Object Memories in the Human Brain.eNeuro · 2024Article
- Assessing naming errors using an automated machine learning approach.Neuropsychology · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
conceptual representations are critical for human cognition. Despite their importance, key properties of these representations remain poorly understood. Here, we used computational models of distributional semantics to predict multivariate fMRI activity patterns during the activation and contextualization of abstract concepts. We devised a task in which participants had to embed abstract nouns into a story that they developed around a given background context. We found that representations in inferior parietal cortex were predicted by concept similarities emerging in models of distributional semantics. By constructing different model families, we reveal the models' learning trajectories and delineate how abstract and concrete training materials contribute to the formation of brain-like representations. These results inform theories about the format and emergence of abstract conceptual representations in the human brain.
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