Alphanumeric and non-alphanumeric Rapid Automatized Naming in children with reading and/or spelling difficulties and mathematical difficulties

Donker, Monika H.; Kroesbergen, Evelyn H.; Slot, Esther M.; Viersen, Sietske van; Bree, Elise de

doi:10.1016/j.lindif.2015.12.011

Cited by 63 publications

(64 citation statements)

References 60 publications

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“…Wilson et al (2015) examined RAN performance among RD and MD adults and found similar deficits in both groups. Two recent studies found RAN deficits in children with both MD and RD (Donker, Kroesbergen, Slot, Van Viersen, & De Bree, 2016;Slot et al, 2016). Moreover, other studies found that RAN deficit was a shared risk factor between ADHD and reading (McGrath et al, 2011;Shanahan et al, 2006).…”

Section: The Role Of Perception Speed In Mathematics Abilitiesmentioning

confidence: 97%

Multiple skills underlie arithmetic performance: A large-scale structural equation modeling analysis

Ashkenazi

Silverman

2017

J. Numer. Cogn.

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Current theoretical approaches point to the importance of several cognitive skills not specific to mathematics for the etiology of mathematics disorders (MD). In the current study, we examined the role of many of these skills, specifically: rapid automatized naming, attention, reading, and visual perception, on mathematics performance among a large group of college students (N = 1,322) with a wide range of arithmetic proficiency. Using factor analysis, we discovered that our data clustered to four latent variables 1) mathematics, 2) perception speed, 3) attention and 4) reading. In subsequent structural equation modeling, we found that the latent variable perception speed had a strong and meaningful effect on mathematics performance. Moreover, sustained attention, independent from the effect of the latent variable perception speed, had a meaningful, direct effect on arithmetic fact retrieval and procedural knowledge. The latent variable reading had a modest effect on mathematics performance. Specifically, reading comprehension, independent from the effect of the latent variable reading, had a meaningful direct effect on mathematics, and particularly on number line knowledge. Attention, tested by the attention network test, had no effect on mathematics, reading or perception speed. These results indicate that multiple factors can affect mathematics performance supporting a heterogeneous approach to mathematics. These results have meaningful implications for the diagnosis and intervention of pure and comorbid learning disorders.Keywords: perception speed, mathematics performance, pure and comorbid mathematics disability Nobes, and Gabriel (2013) proposed that abnormalities in executive functions, specifically inhibition and visuospatial working memory, are the source of MD, rather than mathematics-specific quantitative understanding. Moreover, there is evidence that these cognitive skills affect individual differences in arithmetic abilities across the spectrum of mathematics proficiency. Szűcs, Devine, Soltesz, Nobes, and Gabriel (2014) demonstrated that individual differences in arithmetic abilities was predicted by phonological processing, verbal knowledge, visuospatial short-term and working memory, spatial ability and executive functioning. Journal of Numerical Cognitionjnc.psychopen.eu | 2363-8761The prevalence rate of MD is between 3-6% among children (Reigosa-Crespo et al., 2012;R. Shalev, Manor, Amir, & Gross-Tsur, 1993;von Aster, Schweiter, & Weinhold Zulauf, 2007;von Aster & Shalev, 2007) while there is less evidence in regard to prevalence rates among adults. However, the prevalence of the comorbidity of MD with other disorders that affect learning (such as reading and attention deficits) vary dramatically between studies, and range between 17-66% (Barbaresi, Katusic, Colligan, Weaver, & Jacobsen, 2005;Dirks, Spyer, van Lieshout, & de Sonneville, 2008;Gross-Tsur, Manor, & Shalev, 1996;Landerl & Moll, 2010;Lewis, Hitch, & Walker, 1994;R. Shalev et al., 1993;. Even the lowest reported comorbidit...

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Section: The Role Of Perception Speed In Mathematics Abilitiesmentioning

confidence: 97%

Multiple skills underlie arithmetic performance: A large-scale structural equation modeling analysis

Ashkenazi

Silverman

2017

J. Numer. Cogn.

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“…We opted for inclusion of alphanumeric RAN only as we wanted to assess the specific phonology and orthography association. Furthermore, it has been reported that nonalphanumeric RAN does not relate to spelling (Donker, Kroesbergen, Slot, van Viersen, & de Bree, 2016). A longitudinal study of both nonalphanumeric and alphanumeric RAN is required to gain more insight into the general and specific contributions of RAN to spelling.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Knowing what we don’t know: cognitive correlates of early spelling of different target types

Bree

Boer

2019

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Although research on cognitive correlates of spelling has been conducted, these studies generally do not distinguish between different types of targets that need to be spelled. Arguably, the contributions of these skills differ for words opposed to pseudowords and for targets that can be spelled on the basis of phoneme-to-grapheme conversion (phonologically consistent) and those that cannot (phonologically inconsistent, relying on orthographic knowledge). We assessed these issues in early spelling. A total of 83 first graders and 58 second graders completed word and pseudoword dictation tasks, containing phonologically consistent and inconsistent items. They also completed tasks reflecting potential cognitive correlates of spelling: phoneme awareness, rapid automatized naming, visual attention span and word reading. Dictation outcomes demonstrated that more spelling errors were made in inconsistent than in consistent items. The effect of lexicality differed across grades: More errors were made in spelling words than pseudowords in Grade 1, whereas the opposite pattern was found in Grade 2. Phoneme awareness and rapid naming, but not visual attention span, were found to be significant correlates of spelling outcomes. However, the cognitive correlates captured only a limited amount of variance in spelling errors. Moreover, these effects mostly disappeared once word reading was entered as a predictor. These findings indicate that research into predictors of spelling should distinguish between words and pseudowords, consistent and inconsistent items, and should consider more spelling-based rather than reading-based correlates of spelling performance. Keywords Consistency • Phoneme awareness • Rapid automatised naming • Spelling • Visual attention span • Word type Elise de Bree and Madelon van den Boer have contributed equally to this work.

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“…Yet another explanation for nonalphanumeric RAN being slower is because it requires an extra processing step. In addition to the processes underpinning RAN in general (Donker, Kroesbergen, Slot, Van Viersen, & De Bree, ; Georgiou et al, ), nonalphanumeric RAN is assumed to require access to semantic information, whereas alphanumeric RAN does not. Yet, when Cummine et al () examined RAN performance from a neurological perspective by using functional magnetic resonance imaging (fMRI), they found that RAN objects activated fewer regions than RAN digits and letters did.…”

Section: Introductionmentioning

confidence: 99%

Rapid automatized naming in a developmental perspective between ages 4 and 10

2019

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During the last decades, rapid automatized naming (RAN) has been widely examined as a predictor of reading ability, but very few studies have examined the development of RAN itself. The present study followed children from ages 4 until 10, focusing on RAN performance in a developmental perspective. Relations within and between alphanumeric and nonalphanumeric RAN were investigated both concurrently and over time. The result shows that individual differences in RAN objects are stable between ages 4 and 10, with the most rapid growth of mean performance between ages 4 and 6. Early performance on RAN objects predicts both performance on RAN digits and RAN letters at age 8 and the further development between ages 8 and 10. Further, low‐performing children at age 4 develop their performance on RAN digits and RAN letters later compared with high‐performing children, and RAN digits performance reaches a plateau earlier than RAN letters performance.

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Alphanumeric and non-alphanumeric Rapid Automatized Naming in children with reading and/or spelling difficulties and mathematical difficulties

Cited by 63 publications

References 60 publications

Multiple skills underlie arithmetic performance: A large-scale structural equation modeling analysis

Multiple skills underlie arithmetic performance: A large-scale structural equation modeling analysis

Knowing what we don’t know: cognitive correlates of early spelling of different target types

Rapid automatized naming in a developmental perspective between ages 4 and 10

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