Statistical learning and language acquisition

By Alexa R. Romberg, Jenny R. Saffran. Wiley Interdisciplinary Reviews: Cognitive Science, 2010

510 citations recorded by the source corpus.

Human learners, including infants, are highly sensitive to structure in their environment. Statistical learning refers to the process of extracting this structure. A major question in language acquisition in the past few decades has been the extent to which infants use statistical learning mechanisms to acquire their native language. There have been many demonstrations showing infants’ ability to extract structures in linguistic input, such as the transitional probability between adjacent elements. This paper reviews current research on how statistical learning contributes to language acquisition. Current research is extending the initial findings of infants’ sensitivity to basic statistical information in many different directions, including investigating how infants represent regularities, learn about different levels of language, and integrate information across situations. These current directions emphasize studying statistical language learning in context: within language, within the infant learner, and within the environment as a whole.

Subfields: Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/8dce0a65f5c57e24efb4b8edf4f33425e36d4148

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In Developmental and Educational Psychology

Citations in the corpus

Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner (2016, unknown): “Even though a lot of work has been devoted to study the importance of transition probabilities as a possibly cue to signal word boundaries in infants (Romberg & Saffran, 2010, for a review), it turns out that this cue alone yields disappointingly poor segmentation performance in a real corpus.”
The Cerebellum, Sensitive Periods, and Autism (2014, background): “For example, extracting the structure contained in language requires statistical learning and attending to awide variety of nonverbal cues (Romberg and Saffran, 2010).”
Humanized Foxp2 accelerates learning by enhancing transitions from declarative to procedural performance (2014, background): “One possibility raised by our findings is that efficient proceduralization might accelerate probabilistic learning of language features (10) by chunking single speech and languagerelated actions into sequences, a chunking function that has been suggested to be a core property of the striatum in expe”
Essentials of a Theory of Language Cognition (2019, background): “…influence of frequency of experience upon learning and representation (N. Ellis, 2002), of connectionist and statistical learning mechanisms (N. Ellis, 1998, 2003; MacWhinney, 1997; Rebuschat & Williams, 2012), and of the importance of implicit language cognition (N. Ellis, 1994; Rebuschat, 2015).”
INCIDENTAL VOCABULARY LEARNING THROUGH LISTENING TO SONGS (2019, background): “Statistical learning approach to language learning suggests that language learners can gradually learn individual words and sequences of words through repeated encounters with these items in input (Romberg & Saffran, 2010).”
Beyond Cumulative Risk: A Dimensional Approach to Childhood Adversity (2016, background): “Pattern learning plays a central role in language acquisition and expertise (Romberg & Saffran, 2010; Spencer, Kaschak, Jones, & Lonigan, 2015).”