Motor restrictions impair divergent thinking during walking and during sitting

Creativity, specifically divergent thinking, has been shown to benefit from unrestrained walking. Despite these findings, it is not clear if it is the lack of restriction that leads to the improvement. Our goal was to explore the effects of motor restrictions on divergent thinking for different movement states. In addition, we assessed whether spontaneous eye blinks, which are linked to motor execution, also predict performance. In experiment 1, we compared the performance in Guilford’s alternate uses task (AUT) during walking vs. sitting, and analysed eye blink rates during both conditions. We found that AUT scores were higher during walking than sitting. Albeit eye blinks differed significantly between movement conditions (walking vs. sitting) and task phase (baseline vs. thinking vs. responding), they did not correlate with task performance. In experiment 2 and 3, participants either walked freely or in a restricted path, or sat freely or fixated on a screen. When the factor restriction was explicitly modulated, the effect of walking was reduced, while restriction showed a significant influence on the fluency scores. Importantly, we found a significant correlation between the rate of eye blinks and creativity scores between subjects, depending on the restriction condition. Our study shows a movement state-independent effect of restriction on divergent thinking. In other words, similar to unrestrained walking, unrestrained sitting also improves divergent thinking. Importantly, we discuss a mechanistic explanation of the effect of restriction on divergent thinking based on the increased size of the focus of attention and the consequent bias towards flexibility.

Motor imagery helps updating internal models during microgravity exposure

Skilled movements result from a mixture of feedforward and feedback mechanisms conceptualized by internal models. These mechanisms subserve both motor execution and motor imagery. Current research suggests that imagery allows updating feedforward mechanisms, leading to better performance in familiar contexts. Does this still hold in radically new contexts? Here, we test this ability by asking participants to imagine swinging arm movements around shoulder in normal gravity condition and in microgravity in which studies showed that movements slow down. We timed several cycles of actual and imagined arm pendular movements in three groups of subjects during parabolic flight campaign. The first, control, group remained on the ground. The second group was exposed to microgravity but did not imagine movements inflight. The third group was exposed to microgravity and imagined movements inflight. All groups performed and imagined the movements before and after the flight. We predicted that a mere exposure to microgravity would induce changes in imagined movement duration. We found this held true for the group who imagined the movements, suggesting an update of internal representations of gravity. However, we did not find a similar effect in the group exposed to microgravity despite the fact participants lived the same gravitational variations as the first group. Overall, these results suggest that motor imagery contributes to update internal representations of movement in unfamiliar environments, while a mere exposure proved to be insufficient.

Computational Approaches to Music Motor Performance: Clustering of Percussion Kinematics Underlying Performance Style

The present study investigated motor kinematics underlying performance-related movements in marimba performance. Participants played a marimba while motion capture equipment tracked movements of the torso, shoulders, elbows, wrists, and hands. Principal components analysis was applied to assess the movements during the performance related to sound production and sound preparation. Subsequent cluster analyses sought to identify coupling of limb segment movements that may best characterize performance styles present in the performance. The analysis revealed four clusters that were thought to reflect performance styles of expressive performance, postural sway, energy efficiency, and a blend of the former styles. More specifically, the expressive cluster was best characterized by limb movements occurring along the vertical z-axis, whereas the postural sway cluster was characterized by forwards and backwards motions of the torso and upper limbs. The energy efficient cluster was characterized by movements of the body moving left to right along the marimba, whereas the blended style demonstrated limited delineation from the alternate styles. Such findings were interpreted as evidence that performance styles occur within a framework of biomechanical constraints and hierarchical stylistic factors. Overall, the results provided a more holistic understanding of motor execution in percussion performance.

A PROPOSED SIGN LANGUAGE MODEL FOR SPEECHLESS PERSONS USING EEG SIGNALS

Recently, algorithms of machine learning are widely used with the field of electroencephalography (EEG)-Brain-Computer interfaces (BCI). In this paper, a sign language software model based on the EEG brain signal was implemented, to help the speechless persons to communicate their thoughts to others. The preprocessing stage for the EEG signals was performed by applying the Principle Component Analysis (PCA) algorithm to extract the important features and reducing the data redundancy. A model for classifying ten classes of EEG signals, including Facial Expression(FE) and some Motor Execution(ME) processes, had been designed. A neural network of three hidden layers with deep learning classifier had been used in this work. Data set from four different subjects were collected using a 14 channels Emotiv epoc+ device. A classification results with accuracy 95.75% were obtained ‎for the collected samples. An optimization process was performed on the predicted class with the aid of user, and then sign class will be connected to the specified sentence under a predesigned lock up table.

Rhythmic tapping difficulties in adults who stutter: a deficit in Central Clock and/or Motor Implementation?

The study aims to better understand the origin of increased tapping variability and inaccuracy in people who stutter during paced and un-paced tapping. The overall question is to what extent these timing difficulties are related to a central clock deficit, a deficit in motor execution, or both.Finger tapping behavior of 16 adults who stutter (PWS) with different levels of musical training was compared with performance of 16 matching controls (PNS) in three finger tapping synchronization tasks ― a simple 1:1 isochronous pattern, a complex non-isochronous pattern, and a 4 tap:1 beat isochronous pattern ―, a continuation task (without external stimulation), and a reaction task involving aperiodic and unpredictable patterns. The results show that PWS exhibited larger negative asynchrony (expressed as phase angles), and increased synchronization variability (expressed as phase locking values) in paced tapping tasks, and that these differences from the PNS group were modulated by rhythmic complexity and musical training. The tapping asynchrony with a simple isochronous pattern correlated significantly with the average inter-tap duration, and with tap reaction times during the reaction task. The synchronization variability with a simple isochronous pattern correlated significantly with both the central clock and motor implementation variances as extracted during un-paced tapping, according to the Wing and Kristofferson’s model of timing.The results support the idea that increased tapping variability of PWS is associated with both a central clock and a motor execution deficit. The greater Negative Mean Asynchrony of PWS does not appear to be attributable to a deficit in time estimation but rather to a motor deficit. Several models and theories related to deficits in sensorimotor integration were considered to explain the interactions with beat strength, pattern complexity, and musical training.

Exploring the orthogonal relationship between controlled and automated processes

After identifying some of the weaknesses associated with linear, or serial, models of skill learning—with a focus on their failure to fully account for the ongoing relevance of motor control and attention to action—this chapter synthesizes the evidence presented over the course of this book to construct a model of skilled action that captures the complex relationship between automaticity and attentional focus. This model explains how these two processes operate in a synergistic fashion to help experts overcome the challenges they face in seeking to not only maintain but to continue to improve performance proficiency over long timescales, to update and improve motor execution in training contexts, and to stabilize performance under pressurized conditions. The chapter concludes by briefly discussing the role metacognition plays in allowing expert performers to identify and apply situationally appropriate modes of control.

Brain Processes Involved in Motor Planning Are a Dominant Factor for Inducing Event-Related Desynchronization

Event-related desynchronization (ERD) is a relative attenuation in the spectral power of an electroencephalogram (EEG) observed over the sensorimotor area during motor execution and motor imagery. It is a well-known EEG feature and is commonly employed in brain-computer interfaces. However, its underlying neural mechanisms are not fully understood, as ERD is a single variable correlated with external events involving numerous pathways, such as motor intention, planning, and execution. In this study, we aimed to identify a dominant factor for inducing ERD. Participants were instructed to grasp their right hand with three different (10, 25, or 40%MVF: maximum voluntary force) levels under two distinct experimental conditions: a closed-loop condition involving real-time visual force feedback (VF) or an open-loop condition in a feedforward (FF) manner. In each condition, participants were instructed to repeat the grasping task a certain number of times with a timeline of Rest (10.0 s), Preparation (1.0 s), and Motor Execution (4.0 s) periods, respectively. EEG signals were recorded simultaneously with the motor task to evaluate the time-course of the event-related spectrum perturbation for each condition and dissect the modulation of EEG power. We performed statistical analysis of mu and beta-ERD under the instructed grasping force levels and the feedback conditions. In the FF condition (i.e., no force feedback), mu and beta-ERD were significantly attenuated in the contralateral motor cortex during the middle of the motor execution period, while ERD in the VF condition was maintained even during keep grasping. Only mu-ERD at the somatosensory cortex tended to be slightly stronger in high load conditions. The results suggest that the extent of ERD reflects neural activity involved in the motor planning process for changing virtual equilibrium point rather than the motor control process for recruiting motor neurons to regulate grasping force.