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Overregularization research led by Daniel Slobin argues against B.F. Skinner's view of language development through reinforcement. It shows that children actively construct words' meanings and forms during the child's own development. [6] Differing views on the causes of overregularization and its extinction have been presented.
In overregularization, the regular ways of modifying or connecting words are mistakenly applied to words that require irregular modifications or connections. It is a normal effect observed in the language of beginner and intermediate language-learners, whether native-speaker children or foreign-speaker adults.
A new model called the guidance-organization (GO) model was recently proposed to potentially explain the missing letter effect. It is a combination of the two models proposed by Healy, Koriat, and Greenberg and illuminates the idea that word frequency and function together influence the rate of letter detection errors and omissions.
It is unclear if the word-learning constraints are specific to the domain of language, or if they apply to other cognitive domains. Evidence suggests that the whole object assumption is a result of an object's tangibility; children assume a label refers to a whole object because the object is more salient than its properties or functions. [7]
Genetic research has found two major factors predicting successful language acquisition and maintenance. These include inherited intelligence, and the lack of genetic anomalies that may cause speech pathologies, such as mutations in the FOXP2 gene which cause verbal dyspraxia. The role of inherited intelligence increases with age, accounting ...
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Language transfer is the application of linguistic features from one language to another by a bilingual or multilingual speaker. Language transfer may occur across both languages in the acquisition of a simultaneous bilingual, from a mature speaker's first language (L1) to a second language (L2) they are acquiring, or from an L2 back to the L1. [1]
A comparison between the L1 ball and the L2 ball in two dimensions gives an intuition on how L1 regularization achieves sparsity. Enforcing a sparsity constraint on can lead to simpler and more interpretable models. This is useful in many real-life applications such as computational biology. An example is developing a simple predictive test for ...