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The role of statistical learning in language acquisition has been particularly well documented in the area of lexical acquisition. [1] One important contribution to infants' understanding of segmenting words from a continuous stream of speech is their ability to recognize statistical regularities of the speech heard in their environments. [1]
Statistical language acquisition, a branch of developmental psycholinguistics, studies the process by which humans develop the ability to perceive, produce, comprehend, and communicate with natural language in all of its aspects (phonological, syntactic, lexical, morphological, semantic) through the use of general learning mechanisms operating on statistical patterns in the linguistic input.
Statistical learning (and more broadly, distributional learning) can be accepted as a component of language acquisition by researchers on either side of the "nature and nurture" debate. From the perspective of that debate, an important question is whether statistical learning can, by itself, serve as an alternative to nativist explanations for ...
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. [ 1 ] [ 2 ] [ 3 ] Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data.
Proponents of statistical learning believe that it is the basis for higher level learning, and that humans use the statistical information to create a database which allows them to learn higher-order generalizations and concepts. For a child acquiring language, the challenge is to parse out discrete segments from a continuous speech stream.
In her 1987 work "Second-language acquisition, social interaction, and the classroom" [20] Teresa Pica also posits that interactions including negotiations of meaning between a teacher and a student may not be as effective for the acquisition of a second language due to the imbalance of the teacher-student relationship. An example of this ...
For example, if English-learning infants are exposed to a prevoiced /d/ to voiceless unaspirated /t/ continuum (similar to the /d/ - /t/ distinction in Spanish) with the majority of the tokens occurring near the endpoints of the continuum, i.e., showing extreme prevoicing versus long voice onset times (bimodal distribution) they are better at ...
For example, Abend et al. built a Bayesian inference model that mimics a child's acquisition of English, using only data from a single child in the CHILDES corpus. They found that the model successfully learned English word order, mappings between word labels and semantic meanings of words (i.e. word learning), and used surrounding syntax to ...