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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.
Regularization is a common process in natural languages; regularized forms can replace irregular ones (such as with "cows" and "kine") or coexist with them (such as with "formulae" and "formulas" or "hepatitides" and "hepatitises"). Erroneous regularization is also called overregularization. In overregularization, the regular ways of modifying ...
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]
Mutual exclusivity is a word learning constraint that involves the tendency to assign one label/name, and in turn avoid assigning a second label, to a single object. [1] ...
In cognitive psychology, fast mapping is the term used for the hypothesized mental process whereby a new concept is learned (or a new hypothesis formed) based only on minimal exposure to a given unit of information (e.g., one exposure to a word in an informative context where its referent is present).
The main advantage of the score test over the Wald test and likelihood-ratio test is that the score test only requires the computation of the restricted estimator. [4] This makes testing feasible when the unconstrained maximum likelihood estimate is a boundary point in the parameter space.
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In mathematics, statistics, finance, [1] and computer science, particularly in machine learning and inverse problems, regularization is a process that converts the answer of a problem to a simpler one. It is often used in solving ill-posed problems or to prevent overfitting. [2]