There is no research about age difference in the process of sequential learning in non-human primates. Is there any difference between young and adults in sequential learning process? Six chimpanzees (Pan troglodytes), 3 young and 3 adults, learned the Arabic numeral sequence 1 to 9 by touching the numerals on a touch-screen monitor in ascending order. Initially, the sequence always started with the numeral 1, i.e. 'start-fixed task'. Training began with the sequence 1-2, 1-2-3, and continued sequentially up to 1-2-3-4-5-6-7-8-9. Later, the subjects were introduced to sequences that started with a random numeral, but always ended with 9, i.e. 'end-fixed task'. Performance in the end-fixed task was worse relative to the familiar start-fixed task. After training with various sequences of adjacent numerals, the subjects were given a transfer test for the non-adjacent numerals. The results suggested that all chimpanzees indeed mastered sequential ordering, and although there was no fundamental difference in the acquisition process between the two age groups, there was a significant age difference in memory capacity. Based on their knowledge of sequential ordering, the subjects were then asked to perform a masking task in which once a subject touched the lowest numeral, the other numeral(s) turned to white squares. Performance of the masking task by young chimpanzees was better than that of adults in accuracy and degree of difficulty (number of numerals). Taken together, these data clearly demonstrate a similarity among subjects in the way chimpanzees acquire knowledge of sequential order regardless of age differences in sequential learning. Moreover, they reveal that once knowledge of sequential order is established, it can be a good index used to evaluate memory capacity in young and adult chimpanzees.
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http://dx.doi.org/10.1007/s10071-009-0274-4 | DOI Listing |
Front Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Anesthesiology, E-Da Cancer Hospital, I-Shou University, Kaohsiung, Taiwan.
Parkinson's disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functional medical imaging techniques such as single-photon emission computed tomography (SPECT). In this study, we utilized two SPECT data sets (n = 634 and n = 202) from different hospitals to develop a model capable of accurately predicting PD stages, a multiclass classification task. We used the entire three-dimensional (3D) brain images as input and experimented with various model architectures.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Biomedical Engineering, Islamic University, Kushtia, 7003, Bangladesh; Bio-Imaging Research Laboratory, Islamic University, Kushtia, 7003, Bangladesh. Electronic address:
Computed tomography (CT) scans play a key role in the diagnosis of stroke, a leading cause of morbidity and mortality worldwide. However, interpreting these scans is often challenging, necessitating automated solutions for timely and accurate diagnosis. This research proposed a novel hybrid model that integrates a Vision Transformer (ViT) and a Long Short Term Memory (LSTM) to accurately detect and classify stroke characteristics using CT images.
View Article and Find Full Text PDFComput Biol Med
January 2025
University of Rwanda, Rwanda. Electronic address:
Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodologies frequently inhibit some drawbacks, such as limited interpretability, extensive computational resources, and the need for extensive datasets. To tackle these issues, we introduced two novel algorithms: the Dynamic Co-Occurrence Grey Level Matrix (DC-GLM) and the Contextual Adaptation Multiscale Gabor Network (CAMSGNeT).
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Institute for Better Health, Trillium Health Partners, Mississauga, ON, Canada.
Background: Patient portals, or secure websites linked to electronic medical records, have emerged as tools to provide patients with timely access to their health information. To support the potential benefits of patient portals such as improved engagement in health care, it is essential to understand how patients and caregivers experience these portals.
Objective: This study aimed to explore patient and caregiver experiences, facilitators, and barriers to accessing and using a patient portal called MyChart during the initial stages of its implementation.
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