Words direct visual attention in infants, children, and adults, presumably by activating representations of referents that then direct attention to matching stimuli in the visual scene. Novel, unknown, words have also been shown to direct attention, likely via the activation of more general representations of naming events. To examine the critical issue of how novel words and visual attention interact to support word learning we coded frame-by-frame the gaze of 17- to 31-month-old children (n = 66, 38 females) while generalizing novel nouns. We replicate prior findings of more attention to shape when generalizing novel nouns, and a relation to vocabulary development. However, we also find that following a naming event, children who produce fewer nouns take longer to look at the objects they eventually select and make more transitions between objects before making a generalization decision. Children who produce more nouns look to the objects they eventually select more quickly following the naming event and make fewer looking transitions. We discuss these findings in the context of prior proposals regarding children's few-shot category learning, and a developmental cascade of multiple perceptual, cognitive, and word-learning processes that may operate in cases of both typical development and language delay. RESEARCH HIGHLIGHTS: Examined how novel words guide visual attention by coding frame-by-frame where children look when asked to generalize novel names. Gaze patterns differed with vocabulary size: children with smaller vocabularies attended to generalization targets more slowly and did more comparison than those with larger vocabularies. Demonstrates a relationship between vocabulary size and attention to object properties during naming. This work has implications for looking-based tests of early cognition, and our understanding of children's few-shot category learning.
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http://dx.doi.org/10.1111/desc.13399 | DOI Listing |
Sci Rep
January 2025
Department of Electrical Power, Adama Science and Technology University, Adama, 1888, Ethiopia.
Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in computer vision. In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving the overall network design. Differences between the two domains, such as significant variations in the scale of visual things and the higher granularity of pixels in images compared to words in the text, make it difficult to transfer Transformer from language to vision.
View Article and Find Full Text PDFSci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
View Article and Find Full Text PDFSurv Ophthalmol
January 2025
Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China; Key Lab of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing 100730, China. Electronic address:
Because of its benign nature and rarity, circumscribed choroidal hemangioma (CCH) often receives limited attention, leading to a high rate of misdiagnosis and a lack of standardized treatment protocols. We provide a thorough clarification of the demographics, clinical features, diagnosis, management, and prognosis of CCH. We conducted a systematic search of the PubMed, EMBASE, and Ovid databases up to December, 2023, to identify relevant studies.
View Article and Find Full Text PDFAnal Chem
January 2025
Department of Laboratory Medicine, School of Medicine, Yangtze University, Jingzhou 434023, P.R. China.
Acylaminoacyl-peptide hydrolase (APEH), a serine peptidase that belongs to the prolyl oligopeptidase (POP) family, catalyzes removal of N-terminal acetylated amino acid residues from peptides. As a key regulator of protein N-terminal acetylation, APEH was involved in many important physiological processes while its aberrant expression was correlated with progression of various diseases such as inflammation, diabetics, Alzheimer's disease (AD), and cancers. However, while emerging attention has been attracted in APEH-related disease diagnosis and drug discovery, the mechanisms behind APEH and related disease progression are still unclear; thus, further investigating the physiological role and function of APEH is of great importance.
View Article and Find Full Text PDFNutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
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