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.
Highlights
Understanding abstract and concrete concepts is a fundamental aspect of human language that enables us to discuss matters ranging from everyday objects to fantastic stories of fiction
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
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
Summary
Understanding abstract and concrete concepts is a fundamental aspect of human language that enables us to discuss matters ranging from everyday objects to fantastic stories of fiction. The word “tomato” is linked with the look, feel and taste of a tomato. This view of lexical semantics asserts that these types of physical associations form the building blocks of how words are encoded in the brain. The grounding framework fails to account for abstract words, which lack physical referents and, in many cases, an emotion or an internal state to which the word meaning can be grounded. This issue can be overcome if word meanings can be grounded in the experience of language. If language is seen as another physical environment that a person can interact with, language becomes equivalent to perceptual data, enabling what has been coined as linguistic
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