Performance anxiety can profoundly affect motor performance, even in experts such as professional athletes and musicians. Previously, the neural mechanisms underlying anxiety-induced performance deterioration have predominantly been investigated for individual one-shot actions. Sports and music, however, are characterized by action sequences, where many individual actions are assembled to develop a performance. Here, utilizing a novel differential sequential motor learning paradigm, we first show that performance at the junctions between pre-learnt action sequences is particularly prone to anxiety. Next, utilizing functional magnetic resonance imaging (fMRI), we reveal that performance deterioration at the junctions is parametrically correlated with activity in the dorsal anterior cingulate cortex (dACC). Finally, we show that 1 Hz repetitive transcranial magnetic stimulation of the dACC attenuates the performance deterioration at the junctions. These results demonstrate causality between dACC activity and impairment of sequential motor performance due to anxiety, and suggest new intervention techniques against the deterioration.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6753143 | PMC |
http://dx.doi.org/10.1038/s41467-019-12205-6 | DOI Listing |
Sensors (Basel)
December 2024
Intelligent Manufacturing Laboratory, Production Engineering Institute, Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.
Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Computer Science and Engineering, Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of Korea.
This research work presents an integrated method leveraging Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to enhance the accuracy of predictive maintenance and fault detection in DC motor drives of industrial robots. We propose a new hybrid deep learning framework that combines CNNs with RNNs to improve the accuracy of fault prediction that may occur on a DC motor drive during task processing. The CNN-RNN model determines the optimal maintenance strategy based on data collected from sensors, such as air temperature, process temperature, rotational speed, and so forth.
View Article and Find Full Text PDFPsychol Rev
January 2025
Department of Psychological and Brain Sciences, Dartmouth College.
Our premodern ancestors had perceptual, motoric, and cognitive functional domains that were modularly encapsulated. Some of these came to interact through a new type of cross-modular binding in our species. This allowed previously domain-dedicated, encapsulated motoric and sensory operators to operate on operands for which they had not evolved.
View Article and Find Full Text PDFPflugers Arch
January 2025
Laboratory of Biophysics of Synaptic Processes, Kazan Institute of Biochemistry and Biophysics, FRC Kazan Scientific Center of RAS, 2/31 Lobachevsky St, Kazan, 420111, RT, Russia.
Many synaptic vesicles undergo exocytosis in motor nerve terminals during neuromuscular communication. Endocytosis then recovers the synaptic vesicle pool and presynaptic membrane area. The kinetics of endocytosis may shape neuromuscular transmission, determining its long-term reliability.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!