Rule learning (RL) refers to infants' ability to extract high-order, repetition-based rules from a sequence of elements and to generalize them to new items. RL has been demonstrated in both the auditory and the visual modality, but no studies have investigated infants' transfer of learning across these two modalities, a process that is fundamental for the development of many complex cognitive skills. Using a visual habituation procedure within a cross-modal RL task, we tested 7-month-old infants' transfer of learning both from speech to vision (auditory-visual-AV-condition) and from vision to speech (visual-auditory-VA-condition). Results showed a transfer of learning in the AV condition, but only for those infants who were able to efficiently extract the rule during the learning (habituation) phase. In contrast, in the VA condition infants provided no evidence of RL. Overall, this study indicates that 7-month-old infants can transfers high-order rules across modalities with an advantage for transferring from speech to vision, and that this ability is constrained by infants' individual differences in the way they process the to-be-learned rules.
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http://dx.doi.org/10.1111/infa.12397 | DOI Listing |
Sci Rep
January 2025
Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, India.
Sensors (Basel)
January 2025
Faculty of Science and Environmental Studies, Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada.
In recent years, significant progress has been achieved in understanding and processing tabular data. However, existing approaches often rely on task-specific features and model architectures, posing challenges in accurately extracting table structures amidst diverse layouts, styles, and noise contamination. This study introduces a comprehensive deep learning methodology that is tailored for the precise identification and extraction of rows and columns from document images that contain tables.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Cybersecurity Laboratory, Luleå University of Technology, 97187 Luleå, Sweden.
Alzheimer's disease (AD) leads to severe cognitive impairment and functional decline in patients, and its exact cause remains unknown. Early diagnosis of AD is imperative to enable timely interventions that can slow the progression of the disease. This research tackles the complexity and uncertainty of AD by employing a multimodal approach that integrates medical imaging and demographic data.
View Article and Find Full Text PDFEur Radiol
January 2025
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
Objectives: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.
View Article and Find Full Text PDFEur J Case Rep Intern Med
December 2024
Department of Geriatrics and Internal Medicine, Champmaillot Hospital, University Hospital, Dijon, France.
Introduction: According to the World Health Organization, 44 million people worldwide suffer from Alzheimer's disease. Abnormal movements are atypical symptoms of Alzheimer's disease.
Case Description: An 87-year-old woman, followed for Alzheimer's disease, experienced abnormal movements.
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