A Self-organizing Model of Sequential and Simultaneous Late Language Learning
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2009-03-30
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en
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Abstract
Language learning is typical a sequential process, in which one language is learned after the other. There is reason to believe however that simultaneous language learning, or learning words from multiple languages for one concept at same time, is more efficient. Not only can early learners successfully learn languages simultaneously, associative learning also predicts simultaneous learning to be advantageous in general. Moreover, the integrated nature of the lexicon, with all languages in one storage, seems well fit for simultaneous multilingual learning. To test the likelihood of the hypothesis that simultaneous language learning is indeed beneficial, we developed a model of the lexicon called the Self Organizing Model
of MUltingual Processing (SOMMUP) using self-organizing maps. One map successfully learned semantic similarities, the other one orthographic similarities. Importantly, none of
the maps developed any language-specificity. The model was able to successfully predict the
patterns in reaction times as found in specific and generalized lexical decision tasks depending
on word frequency, neighborhood density, and neighborhood frequency. Using the validated
model, we tested the effect of sequential, mixed, and simultaneous language learning. Due to
imbalances in the tests we could not draw conclusions on the results however, though signs of
relevant patterns were found. Combined, these results not only warrant further research into
the possibility of simultaneous language learning, but also have interesting consequences for
our view of the human lexicon and models thereof.
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Faculteit der Sociale Wetenschappen