The current experiment investigated generalizability of motor learning in proximal versus distal effectors in upper extremities. Twenty-eight participants were divided into three groups: training proximal effectors, training distal effectors, and no training control group (CG). Performance was tested pre- and post-training for specific learning and three learning transfer conditions: (1) bilateral learning transfer between homologous effectors, (2) lateral learning transfer between non-homologous effectors, and (3) bilateral learning transfer between non-homologous effectors. With respect to specific learning, both training groups showed significant, similar improvement for the trained proximal and distal effectors, respectively. In addition, there was significant learning transfer to all three transfer conditions, except for bilateral learning transfer between non-homologous effectors for the distal training group. Interestingly, the proximal training group showed significantly larger learning transfer to other effectors compared to the distal training group. The CG did not show significant improvements from pre- to post-test. These results show that learning is partly effector independent and generalizable to different effectors, even though transfer is suboptimal compared to specific learning. Furthermore, there is a proximal-distal gradient in generalizability, in that learning transfer from trained proximal effectors is larger than from trained distal effectors, which is consistent with neuroanatomical differences in activation of proximal and distal muscles.
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http://dx.doi.org/10.3389/fpsyg.2017.01530 | DOI Listing |
Sci Rep
December 2024
Department of Informatics, University of Hamburg, Hamburg, Germany.
Central to the development of universal learning systems is the ability to solve multiple tasks without retraining from scratch when new data arrives. This is crucial because each task requires significant training time. Addressing the problem of continual learning necessitates various methods due to the complexity of the problem space.
View Article and Find Full Text PDFNPJ Sci Learn
December 2024
Academy of Medical Engineering and Translational Medicine (AMT), Tianjin University, Tianjin, China.
Generalization is central to motor learning. However, few studies are on the learning generalization of BCI-actuated supernumerary robotic finger (BCI-SRF) for human-machine interaction training, and no studies have explored its longitudinal neuroplasticity mechanisms. Here, 20 healthy right-handed participants were recruited and randomly assigned to BCI-SRF group or inborn finger group (Finger) for 4-week training and measured by novel SRF-finger opposition sequences and multimodal MRI.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Electrical Engineering, College of Engineering, Taif University, P.O. BOX 11099, 21944, Taif, Saudi Arabia.
Weather recognition is crucial due to its significant impact on various aspects of daily life, such as weather prediction, environmental monitoring, tourism, and energy production. Several studies have already conducted research on image-based weather recognition. However, previous studies have addressed few types of weather phenomena recognition from images with insufficient accuracy.
View Article and Find Full Text PDFJ Comput Chem
January 2025
Department of Chemistry, University of Nevada Reno, Reno, Nevada, USA.
Hydrogen gas (H) can be produced via entirely solar-driven photocatalytic water splitting (PWS). A promising set of organic materials for facilitating PWS are the so-called inverted singlet-triplet, INVEST, materials. Inversion of the singlet (S) and triplet (T) energies reduces the population of triplet states, which are otherwise destructive under photocatalytic conditions.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA.
Introduction: Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to the microstructural properties of brain tissues and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest without considering the underlying fiber geometry.
Methods: We propose a novel macrostructure-informed normative tractometry (MINT) framework to investigate how white matter (WM) microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia.
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