scholarly journals Prelingual infants discover statistical word patterns at similar rates as adults: evidence from neural entrainment

2020 ◽  
Author(s):  
Dawoon Choi ◽  
Laura Batterink ◽  
Alexis K. Black ◽  
Ken Paller ◽  
Janet F. Werker
Keyword(s):  
Language Acquisition ◽  
Statistical Learning ◽  
Neural Mechanisms ◽  
Learning Effects ◽  
Learning Trajectories ◽  
Continuous Speech ◽  
Neural Entrainment ◽  
Perceptual Shift ◽  
Word Discrimination ◽  
Probability Information

The discovery of words in continuous speech is one of the first challenges faced by infants during language acquisition. This process is partially facilitated by statistical learning, the ability to discover and encode relevant patterns in the environment. Here, we used an EEG index of neural entrainment in 6-month-olds (n=25) to track their segmentation of words from continuous speech. Infants showed neural entrainment to embedded words that increased logarithmically over the learning period, consistent with a perceptual shift from isolated syllables to word-like units. Moreover, infants’ neural entrainment during learning predicted post-learning behavioural measures of word discrimination (n=18). Finally, the logarithmic increase in entrainment to words was comparable in infants and adults, suggesting that infants and adults follow similar learning trajectories when tracking probability information among speech sounds. Statistical learning effects in infants and adults may reflect overlapping neural mechanisms, which emerge early in life and are maintained throughout the lifespan.

Preverbal Infants Discover Statistical Word Patterns at Similar Rates as Adults: Evidence From Neural Entrainment

2020 ◽  
Vol 31 (9) ◽  
pp. 1161-1173
Author(s):  
Dawoon Choi ◽  
Laura J. Batterink ◽  
Alexis K. Black ◽  
Ken A. Paller ◽  
Janet F. Werker
Keyword(s):  
Statistical Learning ◽  
Neural Mechanisms ◽  
Continuous Speech ◽  
Behavioral Measures ◽  
Neural Entrainment ◽  
Perceptual Shift ◽  
Word Discrimination ◽  
Probability Information ◽  
Preverbal Infants ◽  
Electroencephalogram Eeg

The discovery of words in continuous speech is one of the first challenges faced by infants during language acquisition. This process is partially facilitated by statistical learning, the ability to discover and encode relevant patterns in the environment. Here, we used an electroencephalogram (EEG) index of neural entrainment to track 6-month-olds’ ( N = 25) segmentation of words from continuous speech. Infants’ neural entrainment to embedded words increased logarithmically over the learning period, consistent with a perceptual shift from isolated syllables to wordlike units. Moreover, infants’ neural entrainment during learning predicted postlearning behavioral measures of word discrimination ( n = 18). Finally, the logarithmic increase in entrainment to words was comparable in infants and adults, suggesting that infants and adults follow similar learning trajectories when tracking probability information among speech sounds. Statistical-learning effects in infants and adults may reflect overlapping neural mechanisms, which emerge early in life and are maintained throughout the life span.

scholarly journals Rapid Statistical Learning Supporting Word Extraction From Continuous Speech

2017 ◽  
Vol 28 (7) ◽  
pp. 921-928 ◽  
Author(s):  
Laura J. Batterink
Keyword(s):  
Language Acquisition ◽  
Statistical Learning ◽  
Essential Role ◽  
Reaction Time Task ◽  
Speech Segmentation ◽  
Learning Effects ◽  
Continuous Speech ◽  
Statistical Structure ◽  
Early Step ◽  
On Line

The identification of words in continuous speech, known as speech segmentation, is a critical early step in language acquisition. This process is partially supported by statistical learning, the ability to extract patterns from the environment. Given that speech segmentation represents a potential bottleneck for language acquisition, patterns in speech may be extracted very rapidly, without extensive exposure. This hypothesis was examined by exposing participants to continuous speech streams composed of novel repeating nonsense words. Learning was measured on-line using a reaction time task. After merely one exposure to an embedded novel word, learners demonstrated significant learning effects, as revealed by faster responses to predictable than to unpredictable syllables. These results demonstrate that learners gained sensitivity to the statistical structure of unfamiliar speech on a very rapid timescale. This ability may play an essential role in early stages of language acquisition, allowing learners to rapidly identify word candidates and “break in” to an unfamiliar language.

scholarly journals Dynamics of nonlinguistic statistical learning: From neural entrainment to the emergence of explicit knowledge

2020 ◽  
Author(s):  
Julia Moser ◽  
Laura Batterink ◽  
Yiwen Li Hegner ◽  
Franziska Schleger ◽  
Christoph Braun ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Explicit Knowledge ◽  
Neural Mechanisms ◽  
Explicit Learning ◽  
Behavioral Performance ◽  
Source Reconstruction ◽  
Random Condition ◽  
Dynamic Relation ◽  
Wide Range ◽  
Neural Entrainment

AbstractHumans are highly attuned to patterns in the environment. This ability to detect environmental patterns, referred to as statistical learning, plays a key role in many diverse aspects of cognition. However, the spatiotemporal neural mechanisms underlying implicit statistical learning, and how these mechanisms may relate or give rise to explicit learning, remain poorly understood. In the present study, we investigated these different aspects of statistical learning by using an auditory nonlinguistic statistical learning paradigm combined with magnetoencephalography. Twenty-four healthy volunteers were exposed to structured and random tone sequences, and statistical learning was quantified by neural entrainment. Already early during exposure, participants showed strong entrainment to the embedded tone patterns. A significant increase in entrainment over exposure was detected at central sensors only in the structured condition, reflecting the trajectory of learning. While source reconstruction revealed a wide range of brain areas involved in this process, entrainment in right temporo-parietal and frontal areas as well as left pre-central and frontal areas significantly predicted behavioral performance. Increased neural entrainment in the structured compared to the random condition additionally tended to predict behavioral performance more strongly as exposure went on. These results give insights into the dynamic relation between neural entrainment and explicit learning of triplet structures, suggesting that these two aspects are systematically related yet dissociable. Neural entrainment reflects robust, implicit learning of underlying patterns, whereas the emergence of explicit knowledge, likely built on the implicit encoding of structure, varies across individuals and may depend on factors such as sufficient exposure time and attention.

scholarly journals Capacities and neural mechanisms for auditory statistical learning across species

2019 ◽  
Vol 376 ◽  
pp. 97-110 ◽  
Author(s):  
Jennifer K. Schiavo ◽  
Robert C. Froemke
Keyword(s):  
Statistical Learning ◽  
Neural Mechanisms

scholarly journals Deconstructing Procedural Memory: Different Learning Trajectories and Consolidation of Sequence and Statistical Learning

2019 ◽  
Vol 9 ◽  
Author(s):  
Peter Simor ◽  
Zsofia Zavecz ◽  
Kata Horváth ◽  
Noémi Éltető ◽  
Csenge Török ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Procedural Memory ◽  
Learning Trajectories

scholarly journals Reinforced statistical learning of auditory categories

2020 ◽  
Author(s):  
Bob McMurray ◽  
Samantha Chiu
Keyword(s):  
Language Acquisition ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Statistical Learning ◽  
Relevant Dimension ◽  
Learning Paradigm ◽  
Statistical Structure ◽  
Critical Step ◽  
Specific Category ◽  
L2 Learning

A critical step in language acquisition is learning phoneme categories. While L1 learning has been thought to use unsupervised learning (using the distributional statistics of cues), recent research raises the possibility of supervised learning (using teaching signals). Similarly, L2 learning is studied with supervised learning, but unsupervised may also contribute. We developed the reinforced statistical learning paradigm to examine their interaction. Participants first underwent unsupervised learning, hearing a series of non-linguistic sounds whose statistical structure reflected two categories. In subsequent supervised learning, categories either matched or mismatched. Supervised learning was faster when phases matched, though benefits were limited to specific category configurations. Unsupervised learning did not affect the steepness of categorization along the relevant dimension, but it helped subjects learn to ignore irrelevant dimensions. Unsupervised learning may set the stage for supervised learning, but its role may be to determine which dimensions are important, and not to directly acquire categories.

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