The neural representation of abstract words may arise through grounding word meaning in language itself

Autor: Lotta Lammi, Riitta Salmelin, Ali Faisal, Marijn van Vliet, Sasa L. Kivisaari, Tiina Lindh-Knuutila, Annika Hultén
Přispěvatelé: Department of Neuroscience and Biomedical Engineering, Aalto-yliopisto, Aalto University
Rok vydání: 2021
Předmět:
Adult
Male
Text corpus
abstract concepts
Adolescent
decoding
Computer science
Property (programming)
Concept Formation
Word processing
concrete words
Referent
Semantics
computer.software_genre
050105 experimental psychology
Machine Learning
Young Adult
03 medical and health sciences
0302 clinical medicine
RSA
word processing
Humans
0501 psychology and cognitive sciences
Radiology
Nuclear Medicine and imaging
Visual Word
semantics
Cerebral Cortex
Brain Mapping
Models
Statistical
Psycholinguistics
MEG
Radiological and Ultrasound Technology
business.industry
05 social sciences
Magnetoencephalography
Statistical model
machine learning
Reading
Neurology
Female
Neurology (clinical)
Artificial intelligence
Anatomy
business
computer
030217 neurology & neurosurgery
Word (computer architecture)
Natural language processing
Zdroj: Human Brain Mapping. 42:4973-4984
ISSN: 1097-0193
1065-9471
DOI: 10.1002/hbm.25593
Popis: 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.
Databáze: OpenAIRE
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