Decoding abilities in individuals with intellectual disabilities (ID) are substantially lower than for typical readers. The underlying mechanisms of their poor reading remain uncertain. The aim of this study was to investigate the concurrent predictors of decoding ability in 136 adolescents with non-specific ID, and to evaluate the results in relation to previous findings on typical readers. The study included a broad range of cognitive and language measures as predictors of decoding ability. A LASSO regression analysis identified phonological awareness and rapid automatized naming (RAN) as the most important predictors. The predictors explained 57.73% of the variance in decoding abilities. These variables are similar to the ones found in earlier research on typically developing children, hence supporting our hypothesis of a delayed rather than a different reading profile. These results lend some support to the use of interventions and reading instructions, originally developed for typically developing children, for children and adolescents with non-specific ID.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8485868PMC
http://dx.doi.org/10.5334/joc.191DOI Listing

Publication Analysis

Top Keywords

decoding abilities
12
intellectual disabilities
8
typical readers
8
predictors decoding
8
decoding ability
8
adolescents non-specific
8
typically developing
8
developing children
8
decoding
5
abilities adolescents
4

Similar Publications

Desiccation tolerance is a complex phenomenon observed in the lichen Flavoparmelia ceparata. To understand the reactivation process of desiccated thalli, completely dried samples were rehydrated. The rehydration process of this lichen occurs in two phases.

View Article and Find Full Text PDF

DNA methylation repatterning is an epigenomic component of plant stress response, but the extent that methylome data can elucidate changes in plant growth following stress onset is not known. We applied high-resolution DNA methylation analysis to decode plant responses to short- and long-term high light stress and, integrating with gene expression data, attempted to predict components of plant growth response. We identified 105 differentially methylated genes (DMGs) following 1 h of high light treatment and 193 DMGs following 1 week of intermittent high light treatment.

View Article and Find Full Text PDF

Our ability to measure time is vital for daily life, technology use, and even mental health; however, separating pure time perception from other mental processes (like emotions) is a research challenge requiring precise tests to isolate and understand brain activity solely related to time estimation. To address this challenge, we designed an experiment utilizing hypnosis alongside electroencephalography (EEG) to assess differences in time estimation, namely underestimation and overestimation. Hypnotic induction is designed to reduce awareness and meta-awareness, facilitating a detachment from the immediate environment.

View Article and Find Full Text PDF

Adaptive cascade decoders for segmenting challenging regions in medical images.

Comput Biol Med

December 2024

Aerospace Hi-tech Holding Group Co., LTD, Harbin, Heilongjiang, 150060, China.

CNN-based techniques have achieved impressive outcomes in medical image segmentation but struggle to capture long-term dependencies between pixels. The Transformer, with its strong feature extraction and representation learning abilities, performs exceptionally well within the domain of medical image partitioning. However, there are still shortcomings in bridging local to global connections, resulting in occasional loss of positional information.

View Article and Find Full Text PDF

Convolutional neural networks (CNNs) are well established in handling local features in visual tasks; yet, they falter in managing complex spatial relationships and long-range dependencies that are crucial for medical image segmentation, particularly in identifying pathological changes. While vision transformer (ViT) excels in addressing long-range dependencies, their ability to leverage local features remains inadequate. Recent ViT variants have merged CNNs to improve feature representation and segmentation outcomes, yet challenges with limited receptive fields and precise feature representation persist.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!