A theory has been posited that microscale learning, which involves short intervals of a few seconds during explicit motor skill learning, considerably enhances performance. This phenomenon correlates with diminished beta-band activity in the frontal and parietal regions. However, there is a lack of neurophysiological studies regarding the relationship between microscale learning and implicit motor skill learning. In the present study, we aimed to determine the effects of transcranial alternating current stimulation (tACS) during short rest periods on microscale learning in an implicit motor task. We investigated the effects of 20-Hz β-tACS delivered during short rest periods while participants performed an implicit motor task. In Experiments 1 and 2, β-tACS targeted the right dorsolateral prefrontal cortex and the right frontoparietal network, respectively. The participants performed a finger-tapping task using their nondominant left hand, and microscale learning was separately analyzed for micro-online gains (MOnGs) and micro-offline gains (MOffGs). Contrary to our expectations, β-tACS exhibited no statistically significant effects on MOnGs or MOffGs in either Experiment 1 or Experiment 2. In addition, microscale learning during the performance of the implicit motor task was improved by MOffGs in the early learning phase and by MOnGs in the late learning phase. These results revealed that the stimulation protocol employed in this study did not affect microscale learning, indicating a novel aspect of microscale learning in implicit motor tasks. This is the first study to examine microscale learning in implicit motor tasks and may provide baseline information that will be useful in future studies.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.bbr.2023.114770 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Thermal Annealing for High Performance and Memory Behavior in n-Type Organic Electrochemical Transistors.
Adv Mater
December 2024
Organic Bioelectronics Laboratory, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
N-type organic mixed ionic electronic conductors (n-OMIECs) struggle to match the performance of p-type counterparts, particularly in bioelectronics' flagship device, the organic electrochemical transistor. Enhancing n-type transistor performance typically necessitates the synthesis of new materials. More sustainable post-synthetic treatments, known to improve organic devices in dry and oxygen-free conditions, are not applied to n-OMIECs.
View Article and Find Full Text PDFSimple and Efficient Equivariant Message-Passing Neural Network Model for Non-local Potential Energy Surfaces.
J Phys Chem A
December 2024
Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
Machine learning potential has become increasingly successful in atomistic simulations. Many of these potentials are based on an atomistic representation in a local environment, but an efficient description of nonlocal interactions that exceed a common local environment remains a challenge. Herein, we propose a simple and efficient equivariant model, EquiREANN, to effectively represent a nonlocal potential energy surface.
View Article and Find Full Text PDFQuantitative Insight into the Electric Field Effect on CO Electrocatalysis via Machine Learning Spectroscopy.
J Am Chem Soc
December 2024
Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China.
During chemical reactions, especially for electrocatalysis and electrosynthesis, the electric field is the most central driving force to regulate the reaction process. However, due to the difficulty of quantitatively measuring the electric field effects caused at the microscopic level, the regulation of electrocatalytic reactions by electric fields has not been well digitally understood yet. Herein, we took the infrared/Raman spectral signals of CO molecules as descriptors to quantitatively predict the effects of different electric fields on the catalytic properties.
View Article and Find Full Text PDFDigital-SMLM for precisely localizing emitters within the diffraction limit.
Nanophotonics
August 2024
Nanophotonics Research Center, Institute of Microscale Optoelectronics & State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering & Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen 518060, China.
Article Synopsis
- Pinpointing emitter positions under the diffraction limit is essential for biomedical research but has been difficult until the introduction of single molecule localization microscopy (SMLM).
- The new approach, Digital-SMLM, combines experimental datasets with deep learning, achieving 98% accuracy in predicting emitter numbers and positions, with a root mean square error as low as 14 nm.
- Digital-SMLM not only outperforms existing methods like Deep-STORM but also enhances SMLM's capabilities, allowing for better analysis of high-density cellular structures.
Deep learning methods for high-resolution microscale light field image reconstruction: a survey.
Front Bioeng Biotechnol
November 2024
College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Deep learning is progressively emerging as a vital tool for image reconstruction in light field microscopy. The present review provides a comprehensive examination of the latest advancements in light field image reconstruction techniques based on deep learning algorithms. First, the review briefly introduced the concept of light field and deep learning techniques.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!