Visual-motor integration and reading Chinese in children with/without dyslexia

Meng, Ze‐Long; Wydell, Taeko N.; Bi, Hong‐Yan

doi:10.1007/s11145-018-9876-z

Cited by 27 publications

(13 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Classen et al (1998) also reported a decrease in activation coherence in the beta band in a visuomotor force-tracking task but not in visual-or motor-only tasks, suggesting that beta oscillations are related to visuomotor integration (Classen et al, 1998;Kilavik et al, 2013;Müller et al, 2003). Visuomotor skills are particularly important to Chinese reading (Meng et al, 2018;Tan et al, 2005b), and deficits in visuomotor integration have been observed in Chinese dyslexic children in previous behavioral studies (Cheng-Lai et al, 2013;McBride-Chang et al, 2011). Visuomotor integration was not directly assessed in the present study.…”

Section: Network Topology In the Beta Bandmentioning

confidence: 48%

Resting-state EEG reveals global network deficiency in dyslexic children

et al. 2020

View full text Add to dashboard Cite

Developmental dyslexia is known to involve dysfunctions in multiple brain regions; however, a clear understanding of the brain networks behind this disorder is still lacking. The present study examined the functional network connectivity in Chinese dyslexic children with resting-state electroencephalography (EEG) recordings. EEG data were recorded from 27 dyslexic children and 40 age-matched controls, and a minimum spanning tree (MST) analysis was performed to examine the network connectivity in the delta, theta, alpha, and beta frequency bands. The results show that, compared to age-matched controls, Chinese dyslexic children had global network deficiencies in the beta band, and the network topology was more path-like. Moderate correlations are observed between MST degree metric and rapid automatized naming and morphological awareness tests. These observations, together with the findings in alphabetic languages, show that brain network deficiency is a common neural underpinning of dyslexia across writing systems.

show abstract

Section: Network Topology In the Beta Bandmentioning

confidence: 48%

Resting-state EEG reveals global network deficiency in dyslexic children

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In Chinese characters, the parameters of the handwriting product are measured by the number of words written out of the grid, word size, and the numbers of the wrong stroke, additional stroke, missing stroke, concatenated stroke, and reverse stroke. According to Meng, Wydell [25], writing Chinese characters requires children to discriminate against the difference in the structures of characters through the visual-motor abilities to coordinate finger motion and basic visual input. This further explains why VMI and MEM were significantly related to the Chinese handwriting product.…”

Section: Discussionmentioning

confidence: 99%

Evaluating Chinese Handwriting Performance of Primary School Students Using the Smart Handwriting Analysis and Recognition Platform (SHARP)

Li‐Tsang

Yang

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Handwriting is one of the fundamental skills for school-aged children, and it is an intricate and interacting process. An innovative computerized handwriting assessment system, the Smart Handwriting Analysis and Recognition Platform (SHARP) was used to provide a comprehensive and quantitative analysis of Chinese handwriting performance and identify children with handwriting difficulties objectively, accurately, and efficiently. Aims/Objectives: The purpose of this study is to provide quantitative data on children’s Chinese handwriting process and products using SHARP; to investigate the handwriting difficulties in children with neurodevelopmental problems, and to explore the associations between handwriting and sensorimotor performance, including fine motor skills, visual perceptual skills, visual-motor integration, and oculomotor proficiency. Material and Methods: Typically developing students and students with neurodevelopmental problems were recruited through teachers and therapists from six primary schools in Hong Kong. SHARP and tests of sensorimotor skills were performed. Results: The quantitative analysis using the SHARP system showed a progressive developmental change in handwriting performance among the typically developing children at their primary education. There were also significant differences between the typically developing students group and those with neurodevelopmental problems, both in handwriting speed and writing errors. Significant associations between the handwriting process parameters and fine motor skills were identified, while handwriting product parameters were closely correlated with visual-perceptual skills of these typical developing students. Conclusions and Significance: SHARP appears to provide a comprehensive, quantitative and objective evaluation of students’ handwriting performances, thus assisting both teachers and parents in comparing students’ performance objectively, thus identifying students with handwriting difficulties as early as P1 level. It was observed that handwriting performance has strong relationships with young children's visual perceptual skills and fine motor skills. The findings could help to facilitate early intervention for those students with handwriting problems.

show abstract

“…It is plausible that deep orthographic processing decodes characters by utilizing the spatial relation of strokes both visually, through the visual analysis decoding system, and sequentially, through the motor gesture decoding system. It has been shown that children's visual‐motor integration ability during writing effectively predicts their reading performance (Meng et al., 2018; Zemlock et al., 2018). Through visual‐motor integration, writing motor and visual orthographic information mutually contribute to deep orthographic processing.…”

Section: Discussionmentioning

confidence: 99%