Evidence map›Paper›PMID 28493810›Full record

ArticlePsychological science2017

Rapid Statistical Learning Supporting Word Extraction From Continuous Speech.

Laura J Batterink

Abstract read
In one paragraph

Article in Psychological science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Laura J BatterinkDepartment of Psychology, Northwestern University.

Funding

TRAINING PROGRAM IN THE NEUROSCIENCE OF HUMAN COGNITIONT32NS047987 · NINDS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Rodrigo M Braga, Christina Maria Zelano · 2006 to 2026
$5.6M
Statistical learning, memory systems, and sleep-based memory consolidationF32HD078223 · NICHD · NORTHWESTERN UNIVERSITY · PI BATTERINK, LAURA · 2014 to 2015
$106k
NICHD NIH HHS F32 HD078223NINDS NIH HHS T32 NS047987
6 · The paper itself

Abstract

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.

Indexed as

AdolescentFemaleHumansLanguage DevelopmentLanguage TherapyLearningMaleReaction TimeSpeechSpeech PerceptionVerbal LearningVocabularyYoung Adultlanguage acquisitionopen dataopen materialsreaction timespeech segmentationstatistical learning

Identifiers

PMID28493810
PMCPMC5507727

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.