Using EEG to decode semantics during an artificial language learning task
Autor: | Olave E. Krigolson, Chris Foster, Chad C. Williams, Alona Fyshe |
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Rok vydání: | 2021 |
Předmět: |
Computer science
First language media_common.quotation_subject Neurosciences. Biological psychiatry. Neuropsychiatry Multilingualism Representation (arts) Meaning (non-linguistic) computer.software_genre Semantics Language Development language learning 050105 experimental psychology 03 medical and health sciences Behavioral Neuroscience 0302 clinical medicine Humans 0501 psychology and cognitive sciences semantics Original Research media_common language business.industry 05 social sciences Electroencephalography Language acquisition Constructed language Symbol machine learning Semantic mapping Artificial intelligence business computer 030217 neurology & neurosurgery Natural language processing RC321-571 |
Zdroj: | Brain and Behavior Brain and Behavior, Vol 11, Iss 8, Pp n/a-n/a (2021) |
ISSN: | 2162-3279 |
Popis: | Background As we learn a new nonnative language (L2), we begin to build a new map of concepts onto orthographic representations. Eventually, L2 can conjure as rich a semantic representation as our native language (L1). However, the neural processes for mapping a new orthographic representation to a familiar meaning are not well understood or characterized. Methods Using electroencephalography and an artificial language that maps symbols to English words, we show that it is possible to use machine learning models to detect a newly formed semantic mapping as it is acquired. Results Through a trial‐by‐trial analysis, we show that we can detect when a new semantic mapping is formed. Our results show that, like word meaning representations evoked by a L1, the localization of the newly formed neural representations is highly distributed, but the representation may emerge more slowly after the onset of the symbol. Furthermore, our mapping of word meanings to symbols removes the confound of the semantics to the visual characteristics of the stimulus, a confound that has been difficult to disentangle previously. Conclusion We have shown that the L1 semantic representation conjured by a newly acquired L2 word can be detected using decoding techniques, and we give the first characterization of the emergence of that mapping. Our work opens up new possibilities for the study of semantic representations during L2 learning. Using electroencephalography (EEG) and an artificial language that maps symbols to English words, we show that it is possible to use machine learning models to detect a newly formed semantic mapping as it is acquired. |
Databáze: | OpenAIRE |
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