ReviewLanguage, speech, and hearing services in schools2018
The Role of Statistical Learning in Understanding and Treating Spoken Language Outcomes in Deaf Children With Cochlear Implants.
Review in Language, speech, and hearing services in schools, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed, 46 citations in OpenAlex.
- Auditory sequence learning with degraded input: children with cochlear implants ('nature effect') compared to children from low and high socio-economic backgrounds ('nurture effect').Scientific reports · 2025Article
- An approach designed to fail deaf children and their parents and how to change it.Harm reduction journal · 2024Review
- The Effects of Cooperative and Competitive Situations on Statistical Learning.Brain sciences · 2022Article
- Visual statistical learning in deaf and hearing infants and toddlers.Infancy : the official journal of the International Society on Infant Studies · 2022Article
- Accuracy and cue use in word segmentation for cochlear-implant listeners and normal-hearing listeners presented vocoded speech.The Journal of the Acoustical Society of America · 2021Article
- Statistical Learning and Language Impairments: Toward More Precise Theoretical Accounts.Perspectives on psychological science : a journal of the Association for Psychological Science · 2021Review
- How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning.Neuroscience and biobehavioral reviews · 2020Review
- Statistical Learning in Late Talkers and Normal Peers.Advanced biomedical research · 2020Article
- Time-Gated Word Recognition in Children: Effects of Auditory Access, Age, and Semantic Context.Journal of speech, language, and hearing research : JSLHR · 2019Article
- Visual sequential processing and language ability in children who are deaf or hard of hearing.Journal of child language · 2019Article
- Long-Term Language Development in Children With Early Simultaneous Bilateral Cochlear Implants.Ear and hearingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Purpose: Statistical learning-the ability to learn patterns in environmental input-is increasingly recognized as a foundational mechanism necessary for the successful acquisition of spoken language. Spoken language is a complex, serially presented signal that contains embedded statistical relations among linguistic units, such as phonemes, morphemes, and words, which represent the phonotactic and syntactic rules of language. In this review article, we first review recent work that demonstrates that, in typical language development, individuals who display better nonlinguistic statistical learning abilities also show better performance on different measures of language. We next review research findings that suggest that children who are deaf and use cochlear implants may have difficulties learning sequential input patterns, possibly due to auditory and/or linguistic deprivation early in development, and that the children who show better sequence learning abilities also display improved spoken language outcomes. Finally, we present recent findings suggesting that it may be possible to improve core statistical learning abilities with specialized training and interventions and that such improvements can potentially impact and facilitate the acquisition and processing of spoken language. Method: We conducted a literature search through various online databases including PsychINFO and PubMed, as well as including relevant review articles gleaned from the reference sections of other review articles used in this review. Search terms included various combinations of the following: sequential learning, sequence learning, statistical learning, sequence processing, procedural learning, procedural memory, implicit learning, language, computerized training, working memory training, statistical learning training, deaf, deafness, hearing impairment, hearing impaired, DHH, hard of hearing, cochlear implant(s), hearing aid(s), and auditory deprivation. To keep this review concise and clear, we limited inclusion to the foundational and most recent (2005-2018) relevant studies that explicitly included research or theoretical perspectives on statistical or sequential learning. We here summarize and synthesize the most recent and relevant literature to understanding and treating language delays in children using cochlear implants through the lens of statistical learning. Conclusions: We suggest that understanding how statistical learning contributes to spoken language development is important for understanding some of the difficulties that children who are deaf and use cochlear implants might face and argue that it may be beneficial to develop novel language interventions that focus specifically on improving core foundational statistical learning skills.
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Registered trials
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.