Tuesday, December 15, 2015
Allen, M.A., Ukrainetz, T.A., & Carswell, A.L. (2012). The narrative language performance of three types of at-risk first-grade readers. Language, Speech, and Hearing Services in Schools, 43. 205-221.
Response to Intervention (RTI) is a three-tiered approach to identifying and supporting children with reading difficulties. In order to read fluently, a reader must be skilled in both decoding and comprehension. Decoding involves recognizing letters and their corresponding sounds, while comprehension requires the reader to put words together to understand a whole sentence or text. In this study, Allen, Ukrainetz, and Carswell suggest that current RTI models are mainly focused on the decoding processes required in reading, but pay less attention to the linguistic aspects of reading.
In this study, first grade at-risk readers completed measures of expressive and receptive language at 2 points in the school year and prior to intervention. Early resolvers who demonstrated an improvement of reading skills continued with the Tier 1 classroom program. The remaining children received four-weeks of Tier 2 intervention focusing on either fluency or accuracy. Those who received the intervention were grouped as good or poor responders based on the intervention outcome. The retrospective analysis revealed that narrative skills as measured in story retelling (story length; number & variety of words) were better for the good responders than either the poor responders or early resolvers. The early resolvers also used less productive language and recounted fewer elements of the narratives compared to the good responders.
The authors suggested that good responders may have had early reading differences related to experience, but had stronger language skills to support their reading improvement throughout the intervention. The early responders, who demonstrated reading skills expected for their grade, still demonstrated language difficulties on the narrative retell task. Because responses to the code-based RTI model differed based on narrative language skills, the authors argued that RTI models should also consider linguistic aspects of reading difficulty. Including a narrative language component in RTI models would widen the scope of RTI models to identify and support children with language deficits.
Blogger: Alex Cross is completing a combined MClSc and PhD in speech language pathology. Her work focusing on reading will be part of both the Language and Working Memory and the Language, Reading, and Cognitive Neuroscience labs.
Thursday, December 10, 2015
Kapantzoglou, M., Restrepo, M.A., Gray, S., & Thompson, M.S. (2015). Language ability groups in bilingual children: A latent profile analysis
Children with a relatively specific and unexpected atypical language development are considered to have a Specific Language Impairment (SLI). Nevertheless, SLI groups are notoriously heterogeneous such that some children have relative deficits in some or another aspect of language. One effort to understand this heterogeneity is to examine language profiles in groups in the population.
This studied was based on a large group of predominantly Spanish speaking 5-7 year old children who were receiving school instruction in English. Measures of lexical diversity, grammar, length of utterance, rapid naming, nonword repetition, and nonverbal intelligence were completed in Spanish. A Latent Profile Analysis, a statistical method for identifying latent groups with similar profiles based on the variables (scores) considered. Results of this analysis revealed three groups: an average ability group, a group with relative deficits in the measure of grammar, and a group with relative deficits in nonword repetition. The nonword repetition deficit was considered to reflect a deficit in working memory.
The authors suggested that children might have difficulty learning language for two reasons: difficulties with grammar or processing phonological aspects of language. Further, they called for the assessment of both grammar and working memory for phonological information in children with developmental concerns about language.
Blogger: Lisa Archibald
Tuesday, November 17, 2015
Graf Estes, K. & Lew-Williams, C. (2015). Listening Through Voices: Infant Statistical Word Segmentation Across Multiple Speakers. Developmental Psychology, 51(11), 1517-1528.
Statistical language learning refers to learning aspects of language based on the patterns of the language. For example, sounds within a word are more likely to occur together than are sounds that cross word boundaries. These patterns might help infants learn to distinguish words from word boundaries. In this study, Graf Estes and Lew-Williams focused on the statistical learning in infants.
In a real environment, infants are exposed to multiple voices, each with a unique speaking style and rate. Graf Estes and Lew-Williams used multiple voices in a monotone speech stream to mimic this natural environment. Infants from this study listened to a language produced by eight different voices that changed frequently for six minutes. Results suggested that infants were able to learn the artificial language when tested with a common voice or a novel voice, also suggesting that infants can generalize representations.
In the second series of experiments, infants listened to two different voices. The researchers suggested that the use of two dominant voices may better mimic the infant’s environment (e.g. two parents). Infants, however, failed to display signs of language acquisition. Graf Estes and Lew-Williams suggested two possible explanations: Infants were able to learn both the words and part-words, thus showing no discrimination during test phase, or the used of two voices resulted in a focus on discerning the voices, thus inhibiting language acquisition.
Although the mixed results require further investigation, the findings highlight the efficiency of learning in the context of variability (see also, Plante et al. 2014).
Blogger: Hosung (Joel) Kang is a neuroscience student completing his undergraduate thesis project in the Language and Working Memory Lab.