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Journal articles on the topic 'Motor learning and execution'
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Diedrichsen, Jörn, and Katja Kornysheva. "Motor skill learning between selection and execution." Trends in Cognitive Sciences 19, no. 4 (April 2015): 227–33. http://dx.doi.org/10.1016/j.tics.2015.02.003.
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Torriero, Sara, Massimiliano Oliveri, Giacomo Koch, Emanuele Lo Gerfo, Silvia Salerno, Fabio Ferlazzo, Carlo Caltagirone, and Laura Petrosini. "Changes in Cerebello-motor Connectivity during Procedural Learning by Actual Execution and Observation." Journal of Cognitive Neuroscience 23, no. 2 (February 2011): 338–48. http://dx.doi.org/10.1162/jocn.2010.21471.
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The cerebellum is involved in motor learning of new procedures both during actual execution of a motor task and during observational training. These processes are thought to depend on the activity of a neural network that involves the lateral cerebellum and primary motor cortex (M1). In this study, we used a twin-coil TMS technique to investigate whether execution and observation of a visuomotor procedural learning task is related to modulation of cerebello-motor connectivity. We observed that, at rest, a magnetic conditioning pulse applied over the lateral cerebellum reduced the motor-evoked potentials obtained by stimulating the contralateral M1, indicating activation of a cerebello-motor connection. Furthermore, during procedural learning, cerebellar stimulation resulted in selective facilitation, not inhibition, of contralateral M1 excitability. The effects were evident when motor learning was obtained by actual execution of the task or by observation, but they disappeared if procedural learning had already been acquired by previous observational training. These results indicate that changes in cerebello-motor connectivity occur in relation to specific phases of procedural learning, demonstrating a complex pattern of excitatory and inhibitory drives modulated across time.
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Jäger, Anna-Thekla P., Julia M. Huntenburg, Stefanie A. Tremblay, Uta Schneider, Sophia Grahl, Julia Huck, Christine L. Tardif, et al. "Motor sequences; separating the sequence from the motor. A longitudinal rsfMRI study." Brain Structure and Function 227, no. 3 (October 27, 2021): 793–807. http://dx.doi.org/10.1007/s00429-021-02412-7.
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AbstractIn motor learning, sequence specificity, i.e. the learning of specific sequential associations, has predominantly been studied using task-based fMRI paradigms. However, offline changes in resting state functional connectivity after sequence-specific motor learning are less well understood. Previous research has established that plastic changes following motor learning can be divided into stages including fast learning, slow learning and retention. A description of how resting state functional connectivity after sequence-specific motor sequence learning (MSL) develops across these stages is missing. This study aimed to identify plastic alterations in whole-brain functional connectivity after learning a complex motor sequence by contrasting an active group who learned a complex sequence with a control group who performed a control task matched for motor execution. Resting state fMRI and behavioural performance were collected in both groups over the course of 5 consecutive training days and at follow-up after 12 days to encompass fast learning, slow learning, overall learning and retention. Between-group interaction analyses showed sequence-specific decreases in functional connectivity during overall learning in the right supplementary motor area (SMA). We found that connectivity changes in a key region of the motor network, the superior parietal cortex (SPC) were not a result of sequence-specific learning but were instead linked to motor execution. Our study confirms the sequence-specific role of SMA that has previously been identified in online task-based learning studies, and extends it to resting state network changes after sequence-specific MSL.
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Torriani-Pasin, Camila, Gisele Carla dos Santos Palma, Cristiane Matsumoto Jakabi, Cinthya Walter, Andrea Michele Freudenheim, and Umberto César Correa. "Motor Learning of a cognitive-motor task after stroke." Revista Brasileira de Educação Física e Esporte 34, no. 1 (June 4, 2020): 1–9. http://dx.doi.org/10.11606/1807-5509202000010001.
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The aim of this study was investigated a maze learning in stroke individuals. Forty participants assigned into two groups: experimental (stroke participants; n = 20) and control (neurologically healthy participants; n = 20). The study involved an acquisition phase, a transfer test, and a short-and longterm retention tests. The task consisted in complete a maze, with paper and pen, in the shortest time possible. The dependent variables were execution time and error. Data were analyzed with an Anova- two way with Repeated Measures for these variables. Results showed learning for both groups, but with the experimental group having worse performance compared to control group mainly related error. It was also seen the impact promoted in the task has impaired both groups in the transfer test performance.
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Torriani-Pasin, Camila, Gisele Carla dos Santos Palma, Cristiane Matsumoto Jakabi, Cinthya Walter, Andrea Michele Freudenheim, and Umberto César Correa. "Motor Learning of a cognitive-motor task after stroke." Revista Brasileira de Educação Física e Esporte 34, no. 1 (June 4, 2020): 1–9. http://dx.doi.org/10.11606/issn.1981-4690.v34i1p1-9.
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The aim of this study was investigated a maze learning in stroke individuals. Forty participants assigned into two groups: experimental (stroke participants; n = 20) and control (neurologically healthy participants; n = 20). The study involved an acquisition phase, a transfer test, and a short-and longterm retention tests. The task consisted in complete a maze, with paper and pen, in the shortest time possible. The dependent variables were execution time and error. Data were analyzed with an Anova- two way with Repeated Measures for these variables. Results showed learning for both groups, but with the experimental group having worse performance compared to control group mainly related error. It was also seen the impact promoted in the task has impaired both groups in the transfer test performance.
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Domingues, Clayton Amaral, Sergio Machado, Emerson Garcia Cavaleiro, Vernon Furtado, Mauricio Cagy, Pedro Ribeiro, and Roberto Piedade. "Alpha absolute power: motor learning of practical pistol shooting." Arquivos de Neuro-Psiquiatria 66, no. 2b (June 2008): 336–40. http://dx.doi.org/10.1590/s0004-282x2008000300010.
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The present study aimed at investigating changes in behavior (shooting precision) and electrophysiological variables (absolute alpha power) during the motor learning of practical pistol shooting. The sample was composed of 23 healthy subjects, right-handed, male, between 18 and 20 years of age. The task consisted of four learning blocks. A One-way ANOVA with repeated measures and a post hoc analysis were employed to observe modifications on behavioral and electrophysiological measures (p<0.05). The results showed significative differences between blocks according to motor learning, and a significant improvement in shooting's accuracy from both blocks. It was observed a decrease in alpha power in all electrodes examined during task execution when compared with baseline and learning control blocks. The findings suggest that alpha power decreases as the function of the motor learning task when subjects are engaged in the motor execution.
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Sobierajewicz, Jagna, Sylwia Szarkiewicz, Anna Przekoracka-Krawczyk, Wojciech Jaśkowski, and Rob H. J. van der Lubbe. "To What Extent Can Motor Imagery Replace Motor Execution While Learning a Fine Motor Skill?" Advances in Cognitive Psychology 12, no. 4 (December 31, 2016): 178–91. http://dx.doi.org/10.5709/acp-0197-1.
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Stoter, Arjan J. R., Erik J. A. Scherder, Yvo P. T. Kamsma, and Theo Mulder. "Rehearsal Strategies during Motor-Sequence Learning in Old Age: Execution vs Motor Imagery." Perceptual and Motor Skills 106, no. 3 (June 2008): 967–78. http://dx.doi.org/10.2466/pms.106.3.967-978.
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Ariani, Giacomo, and Jörn Diedrichsen. "Sequence learning is driven by improvements in motor planning." Journal of Neurophysiology 121, no. 6 (June 1, 2019): 2088–100. http://dx.doi.org/10.1152/jn.00041.2019.
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The ability to perform complex sequences of movements quickly and accurately is critical for many motor skills. Although training improves performance in a large variety of motor sequence tasks, the precise mechanisms behind such improvements are poorly understood. Here we investigated the contribution of single-action selection, sequence preplanning, online planning, and motor execution to performance in a discrete sequence production task. Five visually presented numbers cued a sequence of five finger presses, which had to be executed as quickly and accurately as possible. To study how sequence planning influenced sequence production, we manipulated the amount of time that participants were given to prepare each sequence by using a forced-response paradigm. Over 4 days, participants were trained on 10 sequences and tested on 80 novel sequences. Our results revealed that participants became faster in selecting individual finger presses. They also preplanned three or four sequence items into the future, and the speed of preplanning improved for trained, but not for untrained, sequences. Because preplanning capacity remained limited, the remaining sequence elements had to be planned online during sequence execution, a process that also improved with sequence-specific training. Overall, our results support the view that motor sequence learning effects are best characterized by improvements in planning processes that occur both before and concurrently with motor execution. NEW & NOTEWORTHY Complex skills often require the production of sequential movements. Although practice improves performance, it remains unclear how these improvements are achieved. Our findings show that learning effects in a sequence production task can be attributed to an enhanced ability to plan upcoming movements. These results shed new light on planning processes in the context of movement sequences and have important implications for our understanding of the neural mechanisms that underlie skill acquisition.
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Cho, Nam Jun, Sang Hyoung Lee, Jong Bok Kim, and Il Hong Suh. "Learning, Improving, and Generalizing Motor Skills for the Peg-in-Hole Tasks Based on Imitation Learning and Self-Learning." Applied Sciences 10, no. 8 (April 15, 2020): 2719. http://dx.doi.org/10.3390/app10082719.
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We propose a framework based on imitation learning and self-learning to enable robots to learn, improve, and generalize motor skills. The peg-in-hole task is important in manufacturing assembly work. Two motor skills for the peg-in-hole task are targeted: “hole search” and “peg insertion”. The robots learn initial motor skills from human demonstrations and then improve and/or generalize them through reinforcement learning (RL). An initial motor skill is represented as a concatenation of the parameters of a hidden Markov model (HMM) and a dynamic movement primitive (DMP) to classify input signals and generate motion trajectories. Reactions are classified as familiar or unfamiliar (i.e., modeled or not modeled), and initial motor skills are improved to solve familiar reactions and generalized to solve unfamiliar reactions. The proposed framework includes processes, algorithms, and reward functions that can be used for various motor skill types. To evaluate our framework, the motor skills were performed using an actual robotic arm and two reward functions for RL. To verify the learning and improving/generalizing processes, we successfully applied our framework to different shapes of pegs and holes. Moreover, the execution time steps and path optimization of RL were evaluated experimentally.
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Herszage, Jasmine, Haggai Sharon, and Nitzan Censor. "Reactivation-induced motor skill learning." Proceedings of the National Academy of Sciences 118, no. 23 (June 4, 2021): e2102242118. http://dx.doi.org/10.1073/pnas.2102242118.
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Learning motor skills commonly requires repeated execution to achieve gains in performance. Motivated by memory reactivation frameworks predominantly originating from fear-conditioning studies in rodents, which have extended to humans, we asked the following: Could motor skill learning be achieved by brief memory reactivations? To address this question, we had participants encode a motor sequence task in an initial test session, followed by brief task reactivations of only 30 s each, conducted on separate days. Learning was evaluated in a final retest session. The results showed that these brief reactivations induced significant motor skill learning gains. Nevertheless, the efficacy of reactivations was not consistent but determined by the number of consecutive correct sequences tapped during memory reactivations. Highly continuous reactivations resulted in higher learning gains, similar to those induced by full extensive practice, while lower continuity reactivations resulted in minimal learning gains. These results were replicated in a new independent sample of subjects, suggesting that the quality of memory reactivation, reflected by its continuity, regulates the magnitude of learning gains. In addition, the change in noninvasive brain stimulation measurements of corticospinal excitability evoked by transcranial magnetic stimulation over primary motor cortex between pre- and postlearning correlated with retest and transfer performance. These results demonstrate a unique form of rapid motor skill learning and may have far-reaching implications, for example, in accelerating motor rehabilitation following neurological injuries.
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Pavlides, C., E. Miyashita, and H. Asanuma. "Projection from the sensory to the motor cortex is important in learning motor skills in the monkey." Journal of Neurophysiology 70, no. 2 (August 1, 1993): 733–41. http://dx.doi.org/10.1152/jn.1993.70.2.733.
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1. The projection from the somatosensory cortex to the primary motor cortex has been proposed to play an important role in learning novel motor skills. This hypothesis was examined by studying the effects of lesions to the sensory cortex on learning of new motor skills. 2. We used two experimental paradigms to reveal the effects of lesions on learning of new motor skills. One task was to catch a food pellet falling at various velocities. The other task was to catch a food pellet from a rotating level. Both tasks required acquisition of novel motor skills. 3. The training was started after a lesion of the hand area in the somatosensory cortex of one hemisphere. In both tasks, monkeys had severe difficulty in learning the new skills with the hand contralateral to the ablated somatosensory cortex, compared with the hand contralateral to the intact hemisphere. 4. After acquisition of the motor skill in the hand contralateral to intact hemisphere, lesion of the somatosensory cortex hand area did not abolish the learned motor skill. 5. In control experiments, monkeys were trained to pick up a food pellet from a rotating board. This task did not necessitate acquisition of new motor skills, but could be performed by utilizing existing motor skills. Lesion in the somatosensory cortex before or after the training did not affect the execution of this task by either hand. 6. It is concluded that the corticocortical projection from the somatosensory to the motor cortex plays an important role in learning new motor skills, but not in the execution of existing motor skills.
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Nakahara, Hiroyuki, Kenji Doya, and Okihide Hikosaka. "Parallel Cortico-Basal Ganglia Mechanisms for Acquisition and Execution of Visuomotor Sequences—A Computational Approach." Journal of Cognitive Neuroscience 13, no. 5 (July 1, 2001): 626–47. http://dx.doi.org/10.1162/089892901750363208.
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Experimental studies have suggested that many brain areas, including the basal ganglia (BG), contribute to procedural learning. Focusing on the basal ganglia-thalamo-cortical (BG-TC) system, we propose a computational model to explain how different brain areas work together in procedural learning. The BG-TC system is composed of multiple separate loop circuits. According to our model, two separate BG-TC loops learn a visuomotor sequence concurrently but using different coordinates, one visual, and the other motor. The visual loop includes the dorsolateral prefrontal (DLPF) cortex and the anterior part of the BG, while the motor loop includes the supplementary motor area (SMA) and the posterior BG. The concurrent learning in these loops is based on reinforcement signals carried by dopaminergic (DA) neurons that project divergently to the anterior (“visual”) and posterior (“motor”) parts of the striatum. It is expected, however, that the visual loop learns a sequence faster than the motor loop due to their different coordinates. The difference in learning speed may lead to inconsistent outputs from the visual and motor loops, and this problem is solved by a mechanism called a “coordinator,” which adjusts the contribution of the visual and motor loops to a final motor output. The coordinator is assumed to be in the presupplementary motor area (pre-SMA). We hypothesize that the visual and motor loops, with the help of the coordinator, achieve both the quick acquisition of novel sequences and the robust execution of well-learned sequences. A computational model based on the hypothesis is examined in a series of computer simulations, referring to the results of the 2 × 5 task experiments that have been used on both monkeys and humans. We found that the dual mechanism with the coordinator was superior to the single (visual or motor) mechanism. The model replicated the following essential features of the experimental results: (1) the time course of learning, (2) the effect of opposite hand use, (3) the effect of sequence reversal, and (4) the effects of localized brain inactivations. Our model may account for a common feature of procedural learning: A spatial sequence of discrete actions (subserved by the visual loop) is gradually replaced by a robust motor skill (subserved by the motor loop).
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Kadmon Harpaz, Naama, Kiah Hardcastle, and Bence P. Ölveczky. "Learning-induced changes in the neural circuits underlying motor sequence execution." Current Opinion in Neurobiology 76 (October 2022): 102624. http://dx.doi.org/10.1016/j.conb.2022.102624.
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King, Bradley R., Florian A. Kagerer, Jose L. Contreras-Vidal, and Jane E. Clark. "Evidence for Multisensory Spatial-to-Motor Transformations in Aiming Movements of Children." Journal of Neurophysiology 101, no. 1 (January 2009): 315–22. http://dx.doi.org/10.1152/jn.90781.2008.
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The extant developmental literature investigating age-related differences in the execution of aiming movements has predominantly focused on visuomotor coordination, despite the fact that additional sensory modalities, such as audition and somatosensation, may contribute to motor planning, execution, and learning. The current study investigated the execution of aiming movements toward both visual and acoustic stimuli. In addition, we examined the interaction between visuomotor and auditory-motor coordination as 5- to 10-yr-old participants executed aiming movements to visual and acoustic stimuli before and after exposure to a visuomotor rotation. Children in all age groups demonstrated significant improvement in performance under the visuomotor perturbation, as indicated by decreased initial directional and root mean squared errors. Moreover, children in all age groups demonstrated significant visual aftereffects during the postexposure phase, suggesting a successful update of their spatial-to-motor transformations. Interestingly, these updated spatial-to-motor transformations also influenced auditory-motor performance, as indicated by distorted movement trajectories during the auditory postexposure phase. The distorted trajectories were present during auditory postexposure even though the auditory-motor relationship was not manipulated. Results suggest that by the age of 5 yr, children have developed a multisensory spatial-to-motor transformation for the execution of aiming movements toward both visual and acoustic targets.
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Sobierajewicz, Jagna, Anna Przekoracka-Krawczyk, Wojciech Jaśkowski, and Rob H. J. van der Lubbe. "How effector-specific is the effect of sequence learning by motor execution and motor imagery?" Experimental Brain Research 235, no. 12 (September 30, 2017): 3757–69. http://dx.doi.org/10.1007/s00221-017-5096-z.
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de Almeida Marcelino, Ana Luísa, Andreas Horn, Patricia Krause, Andrea A. Kühn, and Wolf-Julian Neumann. "Subthalamic neuromodulation improves short-term motor learning in Parkinson’s disease." Brain 142, no. 8 (June 6, 2019): 2198–206. http://dx.doi.org/10.1093/brain/awz152.
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Abstract The basal ganglia and cerebellum are implicated in both motor learning and Parkinson’s disease. Deep brain stimulation (DBS) is an established treatment for advanced Parkinson’s disease that leads to motor and non-motor effects by modulating specific neural pathways. Recently, a disynaptic projection from the subthalamic nucleus (STN) to cerebellar hemispheres was discovered. To investigate the functional significance of this pathway in motor learning, short-term improvement in motor execution in 20 patients with Parkinson’s disease on and off STN-DBS and 20 age-matched healthy controls was studied in a visuomotor task combined with whole-brain connectomics. Motor learning was impaired in Parkinson’s disease off stimulation but was partially restored through DBS. Connectivity between active DBS contacts and a distributed network of brain regions correlated with improvement in motor learning. Region of interest analysis revealed connectivity from active contact to cerebellar hemisphere ipsilateral to hand movement as the strongest predictor for change in motor learning. Peak predictive voxels in the cerebellum localized to Crus II of lobule VII, which also showed higher STN than motor cortex connectivity, suggestive of a connection surpassing motor cortex. Our findings provide new insight into the circuit nature of Parkinson’s disease and the distributed network effects of DBS in motor learning.
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Tsuji, Toshio, Yusuke Ishida, Koji Ito, Mitsuo Nagamachi, and Tatsuo Nishino. "Motor Schema Model Learned by Structural Neural Networks." Journal of Robotics and Mechatronics 2, no. 4 (August 20, 1990): 258–65. http://dx.doi.org/10.20965/jrm.1990.p0258.
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Human beings remember plans concerning typical motions which occur frequently as schema, and by selecting suitable schema depending on conditions, generate muscular motion almost unconsciously. Though a motor schema represents typical motions, it is equipped with superior plan structure taking into consideration the concurrency and seriality of motions as seen in grasping actions and walking motions, and the structure of plans can be acquired by learning. In this paper, a study is made of the modeling of such motor schema with the use of neural networks. For this purpose, the neural network is structured beforehand into the part which generates action sequences in the form containing concurrency (concurrent action generation part) and the part which modifies the action sequences to satisfy constraints which cannot be executed concurrently (constraint representation part). After learning in each part model the neural network can generate motion sequences while taking into consideration the seriality and concurrency of motion by combining the parts at the time of execution. Finally, this model is applied to the formation of typewriting action motor schema, and it is demonsted that generates motion sequences which take into consideration the constraint of the motion system accompanying the execution of motion.
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Floyer-Lea, A., and P. M. Matthews. "Changing Brain Networks for Visuomotor Control With Increased Movement Automaticity." Journal of Neurophysiology 92, no. 4 (October 2004): 2405–12. http://dx.doi.org/10.1152/jn.01092.2003.
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Learning a motor skill is associated with changes in patterns of brain activation with movement. Here we have further characterized these dynamics during fast (short-term) learning of a visuomotor skill using functional magnetic resonance imaging. Subjects ( n = 15) were studied as they learned to visually track a moving target by varying the isometric force applied to a pressure plate held in the right hand. Learning was confirmed by demonstration of improved performance and automaticity (the relative lack of need for conscious attention during task execution). We identified two distinct, time-dependent patterns of functional changes in the brain associated with these behavioral changes. An initial, more attentionally demanding stage of learning was associated with the greatest relative activity in widely distributed, predominantly cortical regions including prefrontal, bilateral sensorimotor, and parietal cortices. The caudate nucleus and ipsilateral cerebellar hemisphere also showed significant activity. Over time, as performance improved, activity in these regions progressively decreased. There was an increase in activity in subcortical motor regions including that of the cerebellar dentate and the thalamus and putamen. Short-term motor-skill learning thus is associated with a progressive reduction of widely distributed activations in cortical regions responsible for executive functions, processing somatosensory feedback and motor planning. The results suggest that early performance gains rely strongly on prefrontal-caudate interactions with later increased activity in a subcortical circuit involving the cerebellum and basal ganglia as the task becomes more automatic. Characterization of these changes provides a potential tool for functional “dissection” of pathologies of movement and motor learning.
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Lomelin-Ibarra, Vicente A., Andres E. Gutierrez-Rodriguez, and Jose A. Cantoral-Ceballos. "Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks." Sensors 22, no. 16 (August 15, 2022): 6093. http://dx.doi.org/10.3390/s22166093.
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Motor imagery is a complex mental task that represents muscular movement without the execution of muscular action, involving cognitive processes of motor planning and sensorimotor proprioception of the body. Since the mental task has similar behavior to that of the motor execution process, it can be used to create rehabilitation routines for patients with some motor skill impairment. However, due to the nature of this mental task, its execution is complicated. Hence, the classification of these signals in scenarios such as brain–computer interface systems tends to have a poor performance. In this work, we study in depth different forms of data representation of motor imagery EEG signals for distinct CNN-based models as well as novel EEG data representations including spectrograms and multidimensional raw data. With the aid of transfer learning, we achieve results up to 93% accuracy, exceeding the current state of the art. However, although these results are strong, they entail the use of high computational resources to generate the samples, since they are based on spectrograms. Thus, we searched further for alternative forms of EEG representations, based on 1D, 2D, and 3D variations of the raw data, leading to promising results for motor imagery classification that still exceed the state of the art. Hence, in this work, we focus on exploring alternative methods to process and improve the classification of motor imagery features with few preprocessing techniques.
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Kase, Kei, Noboru Matsumoto, and Tetsuya Ogata. "Leveraging Motor Babbling for Efficient Robot Learning." Journal of Robotics and Mechatronics 33, no. 5 (October 20, 2021): 1063–74. http://dx.doi.org/10.20965/jrm.2021.p1063.
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Deep robotic learning by learning from demonstration allows robots to mimic a given demonstration and generalize their performance to unknown task setups. However, this generalization ability is heavily affected by the number of demonstrations, which can be costly to manually generate. Without sufficient demonstrations, robots tend to overfit to the available demonstrations and lose the robustness offered by deep learning. Applying the concept of motor babbling – a process similar to that by which human infants move their bodies randomly to obtain proprioception – is also effective for allowing robots to enhance their generalization ability. Furthermore, the generation of babbling data is simpler than task-oriented demonstrations. Previous researches use motor babbling in the concept of pre-training and fine-tuning but have the problem of the babbling data being overwritten by the task data. In this work, we propose an RNN-based robot-control framework capable of leveraging targetless babbling data to aid the robot in acquiring proprioception and increasing the generalization ability of the learned task data by learning both babbling and task data simultaneously. Through simultaneous learning, our framework can use the dynamics obtained from babbling data to learn the target task efficiently. In the experiment, we prepare demonstrations of a block-picking task and aimless-babbling data. With our framework, the robot can learn tasks faster and show greater generalization ability when blocks are at unknown positions or move during execution.
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Gehringer, James E., David J. Arpin, Elizabeth Heinrichs-Graham, Tony W. Wilson, and Max J. Kurz. "Neurophysiological changes in the visuomotor network after practicing a motor task." Journal of Neurophysiology 120, no. 1 (July 1, 2018): 239–49. http://dx.doi.org/10.1152/jn.00020.2018.
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Although it is well appreciated that practicing a motor task updates the associated internal model, it is still unknown how the cortical oscillations linked with the motor action change with practice. The present study investigates the short-term changes (e.g., fast motor learning) in the α- and β-event-related desynchronizations (ERD) associated with the production of a motor action. To this end, we used magnetoencephalography to identify changes in the α- and β-ERD in healthy adults after participants practiced a novel isometric ankle plantarflexion target-matching task. After practicing, the participants matched the targets faster and had improved accuracy, faster force production, and a reduced amount of variability in the force output when trying to match the target. Parallel with the behavioral results, the strength of the β-ERD across the motor-planning and execution stages was reduced after practice in the sensorimotor and occipital cortexes. No pre/postpractice changes were found in the α-ERD during motor planning or execution. Together, these outcomes suggest that fast motor learning is associated with a decrease in β-ERD power. The decreased strength likely reflects a more refined motor plan, a reduction in neural resources needed to perform the task, and/or an enhancement of the processes that are involved in the visuomotor transformations that occur before the onset of the motor action. These results may augment the development of neurologically based practice strategies and/or lead to new practice strategies that increase motor learning. NEW & NOTEWORTHY We aimed to determine the effects of practice on the movement-related cortical oscillatory activity. Following practice, we found that the performance of the ankle plantarflexion target-matching task improved and the power of the β-oscillations decreased in the sensorimotor and occipital cortexes. These novel findings capture the β-oscillatory activity changes in the sensorimotor and occipital cortexes that are coupled with behavioral changes to demonstrate the effects of motor learning.
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Elsayed, Nesma E., Ahmed S. Tolba, Magdi Z. Rashad, Tamer Belal, and Shahenda Sarhan. "A Deep Learning Approach for Brain Computer Interaction-Motor Execution EEG Signal Classification." IEEE Access 9 (2021): 101513–29. http://dx.doi.org/10.1109/access.2021.3097797.
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Al-Anbary, Areej Hameed, and Salih Mahdi Al-Qaraawi. "Classification of EEG signals for facial expression and motor execution with deep learning." TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no. 5 (October 1, 2021): 1588. http://dx.doi.org/10.12928/telkomnika.v19i5.19850.
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Al-Anbary, Areej Hameed, and Salih Mahdi Al-Qaraawi. "Classification of EEG signals for facial expression and motor execution with deep learning." TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no. 5 (October 1, 2021): 1588. http://dx.doi.org/10.12928/telkomnika.v19i5.19850.
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Wagner, Mark J., Tony Hyun Kim, Jonathan Kadmon, Nghia D. Nguyen, Surya Ganguli, Mark J. Schnitzer, and Liqun Luo. "Shared Cortex-Cerebellum Dynamics in the Execution and Learning of a Motor Task." Cell 177, no. 3 (April 2019): 669–82. http://dx.doi.org/10.1016/j.cell.2019.02.019.
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Ranganathan, Rajiv, and Karl M. Newell. "Motor Learning through Induced Variability at the Task Goal and Execution Redundancy Levels." Journal of Motor Behavior 42, no. 5 (September 2010): 307–16. http://dx.doi.org/10.1080/00222895.2010.510542.
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Zhang, Hang, Lele Xu, Rushao Zhang, Mingqi Hui, Zhiying Long, Xiaojie Zhao, and Li Yao. "Parallel Alterations of Functional Connectivity during Execution and Imagination after Motor Imagery Learning." PLoS ONE 7, no. 5 (May 18, 2012): e36052. http://dx.doi.org/10.1371/journal.pone.0036052.
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McDougle, Samuel D., Matthew J. Boggess, Matthew J. Crossley, Darius Parvin, Richard B. Ivry, and Jordan A. Taylor. "Credit assignment in movement-dependent reinforcement learning." Proceedings of the National Academy of Sciences 113, no. 24 (May 31, 2016): 6797–802. http://dx.doi.org/10.1073/pnas.1523669113.
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When a person fails to obtain an expected reward from an object in the environment, they face a credit assignment problem: Did the absence of reward reflect an extrinsic property of the environment or an intrinsic error in motor execution? To explore this problem, we modified a popular decision-making task used in studies of reinforcement learning, the two-armed bandit task. We compared a version in which choices were indicated by key presses, the standard response in such tasks, to a version in which the choices were indicated by reaching movements, which affords execution failures. In the key press condition, participants exhibited a strong risk aversion bias; strikingly, this bias reversed in the reaching condition. This result can be explained by a reinforcement model wherein movement errors influence decision-making, either by gating reward prediction errors or by modifying an implicit representation of motor competence. Two further experiments support the gating hypothesis. First, we used a condition in which we provided visual cues indicative of movement errors but informed the participants that trial outcomes were independent of their actual movements. The main result was replicated, indicating that the gating process is independent of participants’ explicit sense of control. Second, individuals with cerebellar degeneration failed to modulate their behavior between the key press and reach conditions, providing converging evidence of an implicit influence of movement error signals on reinforcement learning. These results provide a mechanistically tractable solution to the credit assignment problem.
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Jastrzębski, Marcin, and Jacek Kabziński. "Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks." Energies 14, no. 18 (September 7, 2021): 5619. http://dx.doi.org/10.3390/en14185619.
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New results in the area of neural network modeling applied in electric drive automation are presented. Reliable models of permanent magnet motor flux as a function of current and rotor position are particularly useful in control synthesis—allowing one to minimize the losses, analyze motor performance (torque ripples etc.) and to identify motor parameters—and may be used in the control loop to compensate flux and torque variations. The effectiveness of extreme learning machine (ELM) neural networks used for approximation of permanent magnet motor flux distribution is evaluated. Two original network modifications, using preliminary information about the modeled relationship, are introduced. It is demonstrated that the proposed networks preserve all appealing features of a standard ELM (such as the universal approximation property and extremely short learning time), but also decrease the number of parameters and deal with numerical problems typical for ELMs. It is demonstrated that the proposed modified ELMs are suitable for modeling motor flux versus position and current, especially for interior permanent magnet motors. The modeling methodology is presented. It is shown that the proposed approach produces more accurate models and provides greater robustness against learning data noise. The execution times obtained experimentally from well-known DSP boards are short enough to enable application of derived models in modern algorithms of electric drive control.
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Bacelar, Mariane F. B., Keith R. Lohse, and Matthew W. Miller. "The Effect of Rewards and Punishments on Learning Action Selection and Execution Components of a Motor Skill." Journal of Motor Learning and Development 8, no. 3 (December 1, 2020): 475–96. http://dx.doi.org/10.1123/jmld.2019-0039.
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It is unknown whether rewards improve the capability to select appropriate targets for one’s movement (action selection) and/or the movement itself (action execution). Thus, we devised an experimental task wherein participants categorized a complex visual stimulus to determine toward which one of two targets to execute an action (putt a golf ball) on each trial under one of three conditions: reward, punishment, or neutral. After practicing the task under their assigned condition, participants performed an immediate, 24-hr, and 7-day post-test. Results revealed participants putted to the correct target more frequently during the post-tests than the first practice block, and putted more accurately during the post-tests than a pretest. However, the condition in which participants practiced did not moderate post-test performance (for either task component). Additionally, motivation scores explained action selection and action execution for the immediate post-test performance but not long-term retention, suggesting that motivation might be related to immediate performance, but not long-term learning. Further, the present task may be useful for researchers studying action selection and execution, since the task yielded learning effects that could be moderated by factors of interest.
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Murgia, Mauro, and Alessandra Galmonte. "Editorial: The Role of Sound in Motor Perception and Execution." Open Psychology Journal 8, no. 1 (December 31, 2015): 171–73. http://dx.doi.org/10.2174/1874350101508010171.
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“Perception and action” is one of the main research fields in which experimental psychologists work together with experts of other disciplines, such as medicine, physiotherapy, engineering, and sport. Traditionally, researchers have mainly focused on visual perception and on its influences on motor processes, while less attention has been dedicated to the role of auditory perception. However, in the last decade, the interest towards the influence of sounds on both action perception and motor execution has increased significantly. On the one hand, researchers have been interested in determining how humans can represent motor actions through the sounds associated with movements, as well as which auditory cues are salient for recognizing and discriminating different features of movement [1-10]. On the other hand, researchers have studied how auditory stimuli affect the production of complex movements in different domains [11-21]. The general aim of this special issue is to provide an overview of the relationship between sounds and movements by addressing theoretical, methodological, and applied issues from a multidisciplinary perspective. ORGANIZATION OF THE VOLUME At the beginning of this special issue we report the contributions that deal with theoretical (Steenson & Rodger; Pizzera & Hohmann) and methodological (Dyer, Stapleton & Rodger) issues regarding auditory perception and action. After providing a theoretical and methodological background, we report those contributions that focus on possible applications of auditory training in the domain of sport and exercise psychology (O, Law & Rymal; Sors, Murgia, Santoro & Agostini), rehabilitation (Murgia, Corona, Pili, Sors, Agostini, Casula, Pau & Guicciardi), and motor learning (Effenberg, Schmitz, Baumann, Rosenhahn & Kroeger). In the first article, Steenson and Rodger highlight that despite the fact that sounds are helpful in executing many dayto- day and context-specific movements and skills in everyday life, there is a surprising lack of exploration of this topic in psychological studies. In fact, the authors review the auditory perception literature and note that auditory perception theories mainly describe the rules governing the processing and representation of sounds in memory, and largely disregard the meaning that sounds have to individuals engaged in movement and the subsequent use of movement sounds in movement priming and execution. Steenson and Rodger’s work can be framed in the context of Gibson’s ecological psychology, as they emphasize the role of sound as a very important affordance that we use to interact with our environment. In the second contribution, Pizzera and Hohmann extensively review studies that address the relevance of the mutual interactions between perception and motor control. Again, these authors highlight the scarcity of research on acoustic information, especially when comparing it with the amount of evidence available in the visual domain. Pizzera and Hohmann offer their perspective on the role of auditory information in controlling and integrating the perception and action cycle. The authors present both behavioral and neurophysiological evidence in support of the importance of auditory information in perception and action, and propose valuable suggestions that future investigators should consider in order to advance the state of knowledge in this domain. The methodological contribution of Dyer, Stapleton and Rodger highlights the feasibility of movement sonification as an effective feedback tool for enhancing motor skill learning and performance, particularly in novices. The authors critically discuss the strengths and weaknesses of movement sonification in the context of providing efficient perceptual feedback information to learners. Dyer, Stapleton and Rodger conclude that a well-defined framework for sonification mapping has yet to be established and that there is still need for controlled trials in motor learning. However, the authors do suggest that new technologies relevant to movement sound recording, mapping, and sonification are available to researchers and can facilitate meaningful and much-needed future research on this promising perceptual feedback method. With regards to the possible applications of audio-based interventions, the fourth article of the issue by O, Law, and Rymal provides an overview of imagery and modeling research in sport psychology and motor learning, documenting evidence supporting the cognitive processing similarities between imagery and modeling. Within this background, the authors critically examine the role of the auditory sense in modeling and imagery, analyzing both theoretical issues and empirical evidence. From a bio-informational theory perspective, O, Law, and Rymal offer several examples of potential applications of the deliberate integration of the auditory sense in movement teaching and instruction, but also offer a strong caveat regarding the severe lack of applied research on the auditory sense focused on sport populations, especially in the domain of imagery. In their conclusions the authors propose detailed recommendations for future research. A second contribution on audio-based interventions in sports is provided by Sors, Murgia, Santoro and Agostini. The authors extensively define the concepts of augmented feedback and modeling, and review studies demonstrating the effectiveness of sounds in improving the execution of simple rhythmic motor tasks. Then, Sors and colleagues describe both a theoretical background and neurophysiological evidence illustrating the mechanisms that are possibly influenced by audio-based interventions. Finally, they provide a complete description of the literature on auditory modeling and auditory augmented feedback in sports, specifying the methodological details of previous studies and proposing future directions for both, application and research. In the sixth article, Murgia, Corona, Pili, Sors, Agostini, Casula, Pau and Guicciardi illustrate the perceptual-motor impairments of patients affected by Parkinsons’ disease and new frontiers in assessment and interventions. They extensively review the empirical evidence concerning the Rhythmic Auditory Stimulation (RAS) method, describing the mechanisms underpinning its effectiveness. The authors propose possible methods for integrating auditory cues into physical therapy interventions as well as assessments. Last, Murgia and colleagues describe the biomechanical advantages of three-dimensional quantitative gait analysis, and discuss the potential impact of the incorporation of ecological footstep sounds in the modulation of patients’ gait. In the seventh and last contribution of this special issue, Effenberg, Schmitz, Baumann, Rosenhahn and Kroeger present a new method based on sonification called “Sound- Script”, which is aimed to facilitate the acquisition of writing. This method consists of the sonification of handwriting, that is, the conversion of physical parameters (i.e., position of the pen, pressure) into movement sounds, which provides children with auditory information which correlates with visual information of their handwriting performance. The authors report pilot data, showing that the multisensory integration elicited by SoundScript leads to a more adequate reproduction of writing kinematics. Effenberg and colleagues conclude by highlighting the potential of this new method and suggesting future steps for research. In sum, we hope that the papers presented in this special issue constitute a useful reference for movement researchers in the field of auditory perception and action, as well as for practitioners in the domains of sport, rehabilitation, and motor learning.
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De Marco, Doriana, Elisa De Stefani, and Giovanni Vecchiato. "Embodying Language through Gestures: Residuals of Motor Memories Modulate Motor Cortex Excitability during Abstract Words Comprehension." Sensors 22, no. 20 (October 12, 2022): 7734. http://dx.doi.org/10.3390/s22207734.
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There is a debate about whether abstract semantics could be represented in a motor domain as concrete language. A contextual association with a motor schema (action or gesture) seems crucial to highlighting the motor system involvement. The present study with transcranial magnetic stimulation aimed to assess motor cortex excitability changes during abstract word comprehension after conditioning word reading to a gesture execution with congruent or incongruent meaning. Twelve healthy volunteers were engaged in a lexical-decision task responding to abstract words or meaningless verbal stimuli. Motor cortex (M1) excitability was measured at different after-stimulus intervals (100, 250, or 500 ms) before and after an associative-learning training where the execution of the gesture followed word processing. Results showed a significant post-training decrease in hand motor evoked potentials at an early processing stage (100 ms) in correspondence to words congruent with the gestures presented during the training. We hypothesized that traces of individual semantic memory, combined with training effects, induced M1 inhibition due to the redundancy of evoked motor representation. No modulation of cortical excitability was found for meaningless or incongruent words. We discuss data considering the possible implications in research to understand the neural basis of language development and language rehabilitation protocols.
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Roland, P. E. "Partition of the Human Cerebellum in Sensory-Motor Activities, Learning and Cognition." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 20, S3 (May 1993): S75—S77. http://dx.doi.org/10.1017/s0317167100048563.
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ABSTRACT:The circuitry of the cerebellum is quite well understood. The computation takes place in the cerebellar cortex, which functions in synchronized strips to provide excellent timing signals to the cerebral cortex and the spinal cord. The cerebellar cortex is also the site where error signals from other parts of the central nervous system are incorporated. For voluntary limb movements the cerebellular cortex is important for the timing of the innervation of the agonist and antagonist anterior horn neurons. It is also important for the temporal order of and precision in the execution of motor programs. As will be apparent, the cerebellum is not only a computer taking care of motor programs.
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Lagarrigue, Yannick, Céline Cappe, and Jessica Tallet. "Regular rhythmic and audio-visual stimulations enhance procedural learning of a perceptual-motor sequence in healthy adults: A pilot study." PLOS ONE 16, no. 11 (November 15, 2021): e0259081. http://dx.doi.org/10.1371/journal.pone.0259081.
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Procedural learning is essential for the effortless execution of many everyday life activities. However, little is known about the conditions influencing the acquisition of procedural skills. The literature suggests that sensory environment may influence the acquisition of perceptual-motor sequences, as tested by a Serial Reaction Time Task. In the current study, we investigated the effects of auditory stimulations on procedural learning of a visuo-motor sequence. Given that the literature shows that regular rhythmic auditory rhythm and multisensory stimulations improve motor speed, we expected to improve procedural learning (reaction times and errors) with repeated practice with auditory stimulations presented either simultaneously with visual stimulations or with a regular tempo, compared to control conditions (e.g., with irregular tempo). Our results suggest that both congruent audio-visual stimulations and regular rhythmic auditory stimulations promote procedural perceptual-motor learning. On the contrary, auditory stimulations with irregular or very quick tempo alter learning. We discuss how regular rhythmic multisensory stimulations may improve procedural learning with respect of a multisensory rhythmic integration process.
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Shea, Charles H., Jin-Hoon Park, and Heather Wilde Braden. "Age-Related Effects in Sequential Motor Learning." Physical Therapy 86, no. 4 (April 1, 2006): 478–88. http://dx.doi.org/10.1093/ptj/86.4.478.
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Abstract Background and Purpose. When learning multi-element movement sequences, participants organize individual elements into subsequences. Imposing this type of structure on the elements leads to the efficient production of sequences because the processing of all but the first elements in a subsequence can be completed prior to their execution. The primary purpose of this study was to determine whether older adults organize lengthy movement sequences with the same efficiency as young adults. Subjects and Methods. Participants were young adults (N=8, 19–23 years of age) and older adults (N=8, 65–68 years of age). The task required participants to move a lever as quickly as possible to targets sequentially projected on a tabletop. At various stages during practice, random practice blocks were inserted between the repeated sequence blocks. Repeated and random sequence retention tests were administered after 24 hours. Results. The results indicated that the young adults performed the repeated sequences substantially faster than the older adults and that this difference increased over practice. On the retention tests, there were no differences in response time for the random sequence blocks, but the young performers were substantially faster than the older performers when repeated sequences were used. No differences were detected in the interview or on the recognition (χ2=1.22, P>.05) and completion (χ2=0.89, P>.05) tests designed to determine explicit or implicit knowledge of the sequences. Discussion and Conclusion. Analysis of the sequence structure indicated that the older adults did not organize their responses into subsequences as effectively as the young adults. The failure of older adults to optimally organize movement sequences may contribute to the overall slowing of sequential movement production. [Shea CH, Park JH, Wilde Braden H. Age-related effects in sequential motor learning. Phys Ther. 2006;86:478–488.]
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Orrell, Alison J., Frank F. Eves, and Rich SW Masters. "Motor Learning of a Dynamic Balancing Task After Stroke: Implicit Implications for Stroke Rehabilitation." Physical Therapy 86, no. 3 (March 1, 2006): 369–80. http://dx.doi.org/10.1093/ptj/86.3.369.
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Abstract Background and Purpose. After a stroke, people often attempt to consciously control their motor actions, which, paradoxically, disrupts optimal performance. A learning strategy that minimizes the accrual of explicit knowledge may circumvent attempts to consciously control motor actions, thereby resulting in better performance. The purpose of this study was to examine the implicit learning of a dynamic balancing task after stroke by use of 1 of 2 motor learning strategies: learning without errors and discovery learning. Participants and Methods. Ten adults with stroke and 12 older adults practiced a dynamic balancing task on a stabilometer under single-task (balance only) and concurrent-task conditions. Root-mean-square error (in degrees) from horizontal was used to measure balance performance. Results. The balance performance of the discovery (explicit) learners after stroke was impaired by the imposition of a concurrent cognitive task load. In contrast, the performance of the errorless (implicit) learners (stroke and control groups) and the discovery learning control group was not impaired. Discussion and Conclusion. The provision of explicit information during rehabilitation may be detrimental to the learning/relearning and execution of motor skills in some people with stroke. The application of implicit motor learning techniques in the rehabilitation setting may be beneficial. [Orrell AJ, Eves FF, Masters RSW. Motor learning of a dynamic balancing task after stroke: implicit implications for stroke rehabilitation. Phys Ther. 2006;86:369–380.]
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Tecchio, Franca, Filippo Zappasodi, Giovanni Assenza, Mario Tombini, Stefano Vollaro, Giulia Barbati, and Paolo Maria Rossini. "Anodal Transcranial Direct Current Stimulation Enhances Procedural Consolidation." Journal of Neurophysiology 104, no. 2 (August 2010): 1134–40. http://dx.doi.org/10.1152/jn.00661.2009.
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The primary motor cortex (M1) area recruitment enlarges while learning a finger tapping sequence. Also M1 excitability increases during procedural consolidation. Our aim was to investigate whether increasing M1 excitability by anodal transcranial DC stimulation (AtDCS) when procedural consolidation occurs was able to induce an early consolidation improvement. Forty-seven right-handed healthy participants were trained in a nine-element serial finger tapping task (SFTT) executed with the left hand. Random series blocks were interspersed with training series blocks. Anodal or sham tDCS was administered over the right M1 after the end of the training session. After stimulation, the motor skills of both trained and a new untrained sequential series blocks were tested again. For each block, performance was estimated as the median execution time of correct series. Early consolidation of the trained series, assessed by the performance difference between the first block after and the last block before stimulation normalized by the random, was enhanced by anodal and not by sham tDCS. Stimulation did not affect random series execution. No stimulation effect was found on the on-line learning of the trained and new untrained series. Our results suggest that AtDCS applied on M1 soon after training improves early consolidation of procedural learning. Our data highlight the importance of neuromodulation procedures for understanding learning processes and support their use in the motor rehabilitation setting, focusing on the timing of the application.
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Nakano, Hideki, Michihiro Osumi, Kozo Ueta, Takayuki Kodama, and Shu Morioka. "Changes in electroencephalographic activity during observation, preparation, and execution of a motor learning task." International Journal of Neuroscience 123, no. 12 (July 9, 2013): 866–75. http://dx.doi.org/10.3109/00207454.2013.813509.
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Malangré, Andreas, Peter Leinen, and Klaus Blischke. "Sleep-Related Offline Learning in a Complex Arm Movement Sequence." Journal of Human Kinetics 40, no. 1 (March 1, 2014): 7–20. http://dx.doi.org/10.2478/hukin-2014-0002.
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Abstract Sleep is known to elicit off-line improvements of newly learned procedural skills, a phenomenon attributed to enhancement consolidation of an internal skill representation. In the motor domain, enhancement consolidation has been reported almost exclusively for sequential-finger-tapping skills. The aim of the present study was to extend the notion of sleep-related enhancement consolidation to tasks closer to everyday motor skills. This was achieved by employing a sequence of unrestrained reaching-movements with the non-dominant arm. Fifteen reaching-movements had to be executed as fast as possible, following a spatial pattern in the horizontal plane. Terminating each movement, a peg had to be fitted into a hole on an electronic pegboard. Two experimental groups received initial training, one in the evening, the other one in the morning. Subsequently, performance in both groups was retested twelve, and again 24 hrs later. Thus, during retention each individual experienced a night of sleep, either followed or preceded by a wake interval. Performance error remained low throughout training and retests. Yet mean total execution time, indicative of task execution-speed, significantly decreased for all individuals throughout initial training (no group differences), and significantly decreased again in either group following nocturnal sleep, but not following wake. This finding does not appear to result merely from additional practice afforded at the time of retests, because only after a night of sleep individuals of both experimental groups also revealed performance improvement beyond that estimated from their initial training performance.
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Bernardi, Nicolò F., Floris T. Van Vugt, Ricardo Ruy Valle-Mena, Shahabeddin Vahdat, and David J. Ostry. "Error-related Persistence of Motor Activity in Resting-state Networks." Journal of Cognitive Neuroscience 30, no. 12 (December 2018): 1883–901. http://dx.doi.org/10.1162/jocn_a_01323.
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The relationship between neural activation during movement training and the plastic changes that survive beyond movement execution is not well understood. Here we ask whether the changes in resting-state functional connectivity observed following motor learning overlap with the brain networks that track movement error during training. Human participants learned to trace an arched trajectory using a computer mouse in an MRI scanner. Motor performance was quantified on each trial as the maximum distance from the prescribed arc. During learning, two brain networks were observed, one showing increased activations for larger movement error, comprising the cerebellum, parietal, visual, somatosensory, and cortical motor areas, and the other being more activated for movements with lower error, comprising the ventral putamen and the OFC. After learning, changes in brain connectivity at rest were found predominantly in areas that had shown increased activation for larger error during task, specifically the cerebellum and its connections with motor, visual, and somatosensory cortex. The findings indicate that, although both errors and accurate movements are important during the active stage of motor learning, the changes in brain activity observed at rest primarily reflect networks that process errors. This suggests that error-related networks are represented in the initial stages of motor memory formation.
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IORGA, Anca. "Execution of plies – basis of classical dance technique." Theatrical Colloquia 12, no. 2 (December 1, 2022): 127–33. http://dx.doi.org/10.35218/tco.2022.12.2.14.
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We rest our endeavour on the fact that the correct technical execution of classical dance movements may lead to the development of specific motor skills and their accurate performance, hence leading to higher precision in movement. This will result in increased body stability when at rest and in motion, increased number of pirouette turns and in higher joint range of motion. This research aims at determining the interindividual differences occurring in similar training conditions in children learning classical dance techniques, and the way in which the proper acquisition of movement mechanisms helps improve the execution technique. Higher mobility of lower limb joints combined with the development of muscular strength in the lower body may lead to accurately performing the classical dance technical elements. We measure the subjects’ mobility of the lower limbs and the maximum amount of force generated by their lower body muscles during the first year of study. At the end of the year, after developing the basic skills, the subjects undergo retesting. Proper training at the appropriate time is an important indicator of measuring the accuracy of performing the motor skills developed over the years, thus contributing to a long and injury-free career of the dancers.
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Heyes, C. M., and C. L. Foster. "Motor learning by observation: Evidence from a serial reaction time task." Quarterly Journal of Experimental Psychology Section A 55, no. 2 (April 2002): 593–607. http://dx.doi.org/10.1080/02724980143000389.
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This study sought evidence of observational motor learning, a type of learning in which observation of the skilled performance of another person not only facilitates motor skill acquisition but does so by contributing to the formation of effector-specific motor representations. Previous research has indicated that observation of skilled performance engages cognitive processes similar to those occurring during action execution or physical practice, but has not demonstrated that these include processes involved in effector-specific representation. In two experiments, observer subjects watched the experimenter performing a serial reaction time (SRT) task with a six-item unique sequence before sequence knowledge was assessed by response time and/or free generation measures. The results suggest that: (1) subjects can acquire sequence information by watching another person performing the task (Experiments 1-2); (2) observation results in as much sequence learning as task practice when learning is measured by reaction times (RTs) and more than task practice when sequence learning is measured by free generation performance (Experiment 2, Part 1); and (3) sequence knowledge acquired by model observation can be encoded motorically—that is, in an effector-specific fashion (Experiment 2, Part 2).
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Nguyen, Van Khanh, Vy Khang Tran, Minh Khai Nguyen, Van To Em Thach, Tran Lam Hai Pham, and Chi Ngon Nguyen. "Realtime Non-invasive Fault Diagnosis of Three-phase Induction Motor." Journal of Technical Education Science, no. 72B (October 28, 2022): 1–11. http://dx.doi.org/10.54644/jte.72b.2022.1231.
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The objective of this paper is to apply deep learning network running on an embedded system platform to diagnose faults of a three-phase electric motor by a non-contact method based on operating motor noise. To accomplish this, at first, deep learning network should be designed and trained on a computer, and then converted to an equivalent network to run on the embedded system. The network input data is a two-dimension spectrogram image of the noise emitted by the motor in four main cases, including normal operation, phase shift, phase loss and bearing failure. The execution time and accuracy of these deep learning network structures will be deployed on three microcontrollers including ESP32, ESP32-C3 and nRF52840 to determine the suitable embedded platform and network structure for real-time running. Experimental results show that the proposed deep learning network models could diagnose the faults well on both computer and embedded platform with the highest accuracies are 99,7% and 99,3%, respectively. In particular, the preliminary results are remarkable with the recognition time and accuracy at 1,7 seconds and 72%, respectively associated with the proposed deep learning network on realtime embedded system performance.
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García-Murillo, Daniel Guillermo, Andres Alvarez-Meza, and German Castellanos-Dominguez. "Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks." Sensors 21, no. 8 (April 13, 2021): 2750. http://dx.doi.org/10.3390/s21082750.
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Motor learning is associated with functional brain plasticity, involving specific functional connectivity changes in the neural networks. However, the degree of learning new motor skills varies among individuals, which is mainly due to the between-subject variability in brain structure and function captured by electroencephalographic (EEG) recordings. Here, we propose a kernel-based functional connectivity measure to deal with inter/intra-subject variability in motor-related tasks. To this end, from spatio-temporal-frequency patterns, we extract the functional connectivity between EEG channels through their Gaussian kernel cross-spectral distribution. Further, we optimize the spectral combination weights within a sparse-based ℓ2-norm feature selection framework matching the motor-related labels that perform the dimensionality reduction of the extracted connectivity features. From the validation results in three databases with motor imagery and motor execution tasks, we conclude that the single-trial Gaussian functional connectivity measure provides very competitive classifier performance values, being less affected by feature extraction parameters, like the sliding time window, and avoiding the use of prior linear spatial filtering. We also provide interpretability for the clustered functional connectivity patterns and hypothesize that the proposed kernel-based metric is promising for evaluating motor skills.
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Barramuño, Mauricio, Pablo Valdés-Badilla, and Exequiel Guevara. "Variations in glenohumeral movement control when implementing an auditory feedback system: A pilot study." Revista de la Facultad de Medicina 67, no. 4 (October 1, 2019): 477–83. http://dx.doi.org/10.15446/revfacmed.v67n4.69456.
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Introduction: Human motor control requires a learning process and it can be trained by means of various sensory feedback sources.Objective: To determine variations in glenohumeral movement control by learning in young adults exposed to an auditory feedback system while they perform object translation tasks classified by difficulty level.Materials and methods: The study involved 45 volunteers of both sexes (22 women), aged between 18 and 32 years. Glenohumeral movement control was measured by means of the root mean square (RMS) of the accelerometry signal, while task execution speed (TES) was measured using an accelerometer during the execution of the task according to its difficulty (easy, moderate and hard) in four stages of randomized intervention (control, pre-exposure, exposure-with auditory feedback, and post-exposure).Results: Statistically significant differences (p<0.001) were found between the pre-exposure and exposure stages and between pre-exposure and post-exposure stages. A significant increase (p <0.001) in TES was identified between the pre-exposure and exposure stages for tasks classified as easy and hard, respectively.Conclusion: The use of an auditory feedback system in young adults without pathologies enhanced learning and glenohumeral movement control without reducing TES. This effect was maintained after the feedback, so the use of this type of feedback system in healthy individuals could result in a useful strategy for the training of motor control of the shoulder.
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Sato, Hayaho, and Hajime Igarashi. "Deep learning-based surrogate model for fast multi-material topology optimization of IPM motor." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 41, no. 3 (November 3, 2021): 900–914. http://dx.doi.org/10.1108/compel-03-2021-0086.
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Purpose This paper aims to present a deep learning–based surrogate model for fast multi-material topology optimization of an interior permanent magnet (IPM) motor. The multi-material topology optimization based on genetic algorithm needs large computational burden because of execution of finite element (FE) analysis for many times. To overcome this difficulty, a convolutional neural network (CNN) is adopted to predict the motor performance from the cross-sectional motor image and reduce the number of FE analysis. Design/methodology/approach To predict the average torque of an IPM motor, CNN is used as a surrogate model. From the input cross-sectional motor image, CNN infers dq-inductance and magnet flux to compute the average torque. It is shown that the average torque for any current phase angle can be predicted by this approach, which allows the maximization of the average torque by changing the current phase angle. The individuals in the multi-material topology optimization are evaluated by the trained CNN, and the limited individuals with higher potentials are evaluated by finite element method. Findings It is shown that the proposed method doubles the computing speed of the multi-material topology optimization without loss of search ability. In addition, the optimized motor obtained by the proposed method followed by simplification for manufacturing is shown to have higher average torque than a reference model. Originality/value This paper proposes a novel method based on deep learning for fast multi-material topology optimization considering the current phase angle.
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Rohleder, Jonas, and Tobias Vogt. "EFFICACY OF WRIST STRATEGY COACHING ON HANDSTAND PERFORMANCES IN NOVICES: INVERTING EXPLICIT AND IMPLICIT LEARNING OF SKILL-RELATED MOTOR TASKS." Science of Gymnastics Journal 11, no. 2 (June 1, 2019): 209–22. http://dx.doi.org/10.52165/sgj.11.2.209-222.
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In gymnastics, mainstream handstand coaching emphasizes developing an aligned rigid body configuration, frequently leaving wrist-controlled balance work to implicit learning. However, skill-related motor behavioral research suggests the wrists to primarily contribute postural control in handstands. Considering recent research on handstands revealing experience-dependent motor behavior, the present study aimed to examine motor learning effects of explicit wrist usage coaching on handstand performances in skilled and less skilled novices. Therefore, twenty-five volunteering sport students served as participants completing a three-week training intervention which solely and explicitly addressed successful wrist usage during handstand. A video-tutorial introducing participants to the wrist strategy of hand balance preceded five practical training sessions that all neglected providing explicit postural advice. Participants performed three handstands on a plane gymnastics mat prior to (pre-test) and after (post-test) completing the training intervention. Standardized video recordings of each trial allowed retrospective group assignment (skilled and less skilled novices) based on pre-test mean balance times. With this, balance times, expert assessments (postural execution and balance control strategies) and goniometric analyses of shoulder and hip joint angles served to detect practical changes in handstand performances. Enhanced balance times as well as increased scores for postural execution and balance control strategies were revealed for less skilled novices (p < .05), but not for skilled novices (p > .05). Furthermore, in both groups changes in shoulder and hip joint angles failed significance. In conclusion, present findings suggest practitioners to make entirely unexperienced handstand learners explicitly aware of the wrist strategy’s operating principle.
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Bütepage, Judith, Silvia Cruciani, Mia Kokic, Michael Welle, and Danica Kragic. "From Visual Understanding to Complex Object Manipulation." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 2019): 161–79. http://dx.doi.org/10.1146/annurev-control-053018-023735.
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Planning and executing object manipulation requires integrating multiple sensory and motor channels while acting under uncertainty and complying with task constraints. As the modern environment is tuned for human hands, designing robotic systems with similar manipulative capabilities is crucial. Research on robotic object manipulation is divided into smaller communities interested in, e.g., motion planning, grasp planning, sensorimotor learning, and tool use. However, few attempts have been made to combine these areas into holistic systems. In this review, we aim to unify the underlying mechanics of grasping and in-hand manipulation by focusing on the temporal aspects of manipulation, including visual perception, grasp planning and execution, and goal-directed manipulation. Inspired by human manipulation, we envision that an emphasis on the temporal integration of these processes opens the way for human-like object use by robots.
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Dong, Daqi, and Stan Franklin. "A New Action Execution Module for the Learning Intelligent Distribution Agent (LIDA): The Sensory Motor System." Cognitive Computation 7, no. 5 (March 4, 2015): 552–68. http://dx.doi.org/10.1007/s12559-015-9322-3.
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