In order to acquire their native language, infants must learn to identify and segment word forms in continuous speech. This word segmentation ability is thus crucial for language acquisition. Previous behavioral studies have shown that it emerges during the first year of life, and that early segmentation differs according to the language in acquisition. In particular, linguistic rhythm, which differs across classes of languages, has been found to have an early impact on segmentation abilities. For French, behavioral evidence showed that infants could use the rhythmic unit appropriate to their native language (the syllable) to segment fluent speech by 12months of age, but failed to show whole word segmentation at that age, a surprising delay compared to the emergence of segmentation abilities in other languages. Given the implications of such findings, the present study reevaluates the issue of whole word and syllabic segmentation, using an electrophysiological method, high-density ERPs (event-related potentials), rather than a behavioral technique, and by testing French-learning 12-month-olds on bisyllabic word segmentation. The ERP data show evidence of whole word segmentation while also confirming that French-learning infants rely on syllables to segment fluent speech. They establish that segmentation and recognition of words/syllables happen within 500ms of their onset, and raise questions regarding the interaction between syllabic segmentation and multisyllabic word recognition.
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http://dx.doi.org/10.1016/j.brainres.2010.03.047 | DOI Listing |
Genes (Basel)
December 2024
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
Background/objectives: Understanding the relationship between DNA sequences and gene expression levels is of significant biological importance. Recent advancements have demonstrated the ability of deep learning to predict gene expression levels directly from genomic data. However, traditional methods are limited by basic word encoding techniques, which fail to capture the inherent features and patterns of DNA sequences.
View Article and Find Full Text PDFDev Sci
March 2025
Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Études Cognitives, ENS, EHESS, CNRS, PSL University, Paris, France.
Before they even talk, infants become sensitive to the speech sounds of their native language and recognize the auditory form of an increasing number of words. Traditionally, these early perceptual changes are attributed to an emerging knowledge of linguistic categories such as phonemes or words. However, there is growing skepticism surrounding this interpretation due to limited evidence of category knowledge in infants.
View Article and Find Full Text PDFRev Cardiovasc Med
December 2024
Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 100050 Beijing, China.
Cardiac magnetic resonance (CMR) imaging enables a one-stop assessment of heart structure and function. Artificial intelligence (AI) can simplify and automate work flows and improve image post-processing speed and diagnostic accuracy; thus, it greatly affects many aspects of CMR. This review highlights the application of AI for left heart analysis in CMR, including quality control, image segmentation, and global and regional functional assessment.
View Article and Find Full Text PDFBehav Res Methods
December 2024
Department of Education Studies, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong.
The absence of explicit word boundaries is a distinctive characteristic of Chinese script, setting it apart from most alphabetic scripts, leading to word boundary disagreement among readers. Previous studies have examined how this feature may influence reading performance. However, further investigations are required to generate more ecologically valid and generalizable findings.
View Article and Find Full Text PDFFront Neurol
December 2024
Bakırköy Dr. Sadi Konuk Eğitim ve Araştırma Hastanesi, Istanbul, Türkiye.
Objective: We aimed to investigate the relationship between volumetric measurements of specific brain regions which were measured with artificial intelligence (AI) and various neuropsychological tests in patients with clinically isolated syndrome.
Materials And Methods: A total of 28 patients diagnosed with CIS were included in the study. The patients were administered Öktem Verbal Memory Processes Test, Symbol Digit Modalities Test (SDMT), Backward-Forward Digit Span Test, Stroop Test, Trail Making Test, Controlled Oral Word Association Test (COWAT), Brief Visuospatial Memory Test, Judgement of Line Orientation Test, Beck Depression Scale, Beck Anxiety Scale and Fatigue Severity Scale.
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