ArticleHuman brain mapping2021
The neural representation of abstract words may arise through grounding word meaning in language itself.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Indirect experiential grounding: semantic similarity of abstract scientific concepts is reflected in activity patterns in visual and motor cortex.Scientific reports · 2025Article
- Fast hierarchical processing of orthographic and semantic parafoveal information during natural reading.Nature communications · 2025Article
- Misspelled-Word Reading Modulates Late Cortical Dynamics.Human brain mapping · 2025Article
- Decoding the Spatiotemporal Dynamics of Neural Response Similarity in Auditory Processing: A Multivariate Analysis Based on OPM-MEG.Human brain mapping · 2025Article
- Spatio-temporal activity of brain electrical sources during processing of abstract social concepts.Social cognitive and affective neuroscience · 2025Article
- Separating feedforward and feedback dynamics using time-frequency resolved connectivity: A hybrid model of left ventral occipitotemporal cortex in word reading.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Spatiotemporal Properties of Common Semantic Categories for Words and Pictures.Journal of cognitive neuroscience · 2024Article
- Subword Representations Successfully Decode Brain Responses to Morphologically Complex Written Words.Neurobiology of language (Cambridge, Mass.) · 2024Article
- Cortical time-course of evidence accumulation during semantic processing.Communications biology · 2023Article
- Group-level brain decoding with deep learning.Human brain mapping · 2023Article
- Interpretable many-class decoding for MEG.NeuroImage · 2023Article
- The neural representation of abstract words may arise through grounding word meaning in language itself.Human brain mapping · 2021Article
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7 authors.
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Abstract
In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words, which lack a physical referent. Hebbian formalism and optimization are basic principles of brain function, and they provide an appealing approach for modeling word meanings based on word co-occurrences. We provide proof of concept that a statistical model of the semantic space can account for neural representations of both concrete and abstract words, using MEG. Here, we built a statistical model using word embeddings extracted from a text corpus. This statistical model was used to train a machine learning algorithm to successfully decode the MEG signals evoked by written words. In the model, word abstractness emerged from the statistical regularities of the language environment. Representational similarity analysis further showed that this salient property of the model co-varies, at 280-420 ms after visual word presentation, with activity in regions that have been previously linked with processing of abstract words, namely the left-hemisphere frontal, anterior temporal and superior parietal cortex. In light of these results, we propose that the neural encoding of word meanings can arise through statistical regularities, that is, through grounding in language itself.
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