Abstract
BackgroundAs 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.MethodsUsing 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.ResultsThrough 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.ConclusionWe 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.
Highlights
Each year millions of people will study a foreign language
In this thesis we show that semantic representations of the native word (e.g., “book”) can be decoded from electrophysiological data measured in the human brain using machine learning and a technology known as Electroencephalography (EEG)
In this thesis we show that we can: 1) detect the semantic representation of English words in EEG while participants read the symbol language, 2) measure how these semantic representations develop over time during the participant learning phase, 3) validate and compare our model against a traditional reward positivity analysis of the same experiment, 4) provide supportive evidence that suggests intuitive alignment with the participants’ task accuracies, 5) identify a delayed peak in the strength of the semantic representation correlating to the delay required for the task, and 6) provide further evidence that the source of semantic representations in the human brain is highly distributed and not attributable to a single area of the cortex
Summary
Each year millions of people will study a foreign language. At first, the foreign symbols have no meaning. Our methodology adapts an existing semantic analysis approach based on machine learning and applies it to an EEG-based reinforcement learning experiment paradigm. This allows us to model the development of semantic mappings. The model that we use attempts to find a mapping between the EEG data and semantic word vectors used in computational linguistics. Machine learning is a subfield of artificial intelligence that gives computers the ability to improve their performance at a task in response to data about that task (called training data). The algorithm learns to map from a given input x to a predicted output yusing training data sets with inputs X and matching outputs Y
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