Practice makes efficient: Effects of golf practice on brain activity

This study employed a test-retest design to examine changes in brain activity associated with practice of a motor skill. We recorded EEG activity from twelve right-handed recreational golfers (mean handicap: 23) as they putted 50 balls to a 2.4m distant hole, before and after a 3-day practice. We measured changes in putting performance, conscious processing, and regional EEG alpha activity. Putting performance improved and conscious processing decreased after practice. Mediation analyses revealed that performance improvements were associated with changes in EEG alpha, whereby activity in task-irrelevant cortical regions (temporal regions) was inhibited and functionally isolated from activity in task-relevant regions (central regions). These findings provide evidence for the development of greater neurophysiological efficiency with practice of a motor skill

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

Time Series Clustering with Deep Reservoir Computing

This paper proposes a method for clustering of time series, based upon the ability of deep Reservoir Computing networks to grasp the dynamical structure of the series that is presented as input. A standard clustering algorithm, such as k-means, is applied to the network states, rather than the input series themselves. Clustering is thus embedded into the network dynamical evolution, since a clustering result is obtained at every time step, which in turn serves as initialisation at the next step. We empirically assess the performance of deep reservoir systems in time series clustering on benchmark datasets, considering the influence of crucial hyperparameters. Experimentation with the proposed model shows enhanced clustering quality, measured by the silhouette coefficient, when compared to both static clustering of data, and dynamic clustering with a shallow network

Ring Reservoir Neural Networks for Graphs

Machine Learning for graphs is nowadays a research topic of consolidated relevance. Common approaches in the field typically resort to complex deep neural network architectures and demanding training algorithms, highlighting the need for more efficient solutions. The class of Reservoir Computing (RC) models can play an important role in this context, enabling to develop fruitful graph embeddings through untrained recursive architectures. In this paper, we study progressive simplifications to the design strategy of RC neural networks for graphs. Our core proposal is based on shaping the organization of the hidden neurons to follow a ring topology. Experimental results on graph classification tasks indicate that ring-reservoirs architectures enable particularly effective network configurations, showing consistent advantages in terms of predictive performance

Reservoir Topology in Deep Echo State Networks

Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning. In this paper we study the impact of constrained reservoir topologies in the architectural design of deep reservoirs, through numerical experiments on several RC benchmarks. The major outcome of our investigation is to show the remarkable effect, in terms of predictive performance gain, achieved by the synergy between a deep reservoir construction and a structured organization of the recurrent units in each layer. Our results also indicate that a particularly advantageous architectural setting is obtained in correspondence of DeepESNs where reservoir units are structured according to a permutation recurrent matrix.Comment: Preprint of the paper published in the proceedings of ICANN 201

Reservoir Topology in Deep Echo State Networks

Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning. In this paper we study the impact of constrained reservoir topologies in the architectural design of deep reservoirs, through numerical experiments on several RC benchmarks. The major outcome of our investigation is to show the remarkable effect, in terms of predictive performance gain, achieved by the synergy between a deep reservoir construction and a structured organization of the recurrent units in each layer. Our results also indicate that a particularly advantageous architectural setting is obtained in correspondence of DeepESNs where reservoir units are structured according to a permutation recurrent matrix

Richness of Deep Echo State Network Dynamics

Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs). Recently, the advantages of the RC approach have been extended to the context of multi-layered RNNs, with the introduction of the Deep Echo State Network (DeepESN) model. In this paper, we study the quality of state dynamics in progressively higher layers of DeepESNs, using tools from the areas of information theory and numerical analysis. Our experimental results on RC benchmark datasets reveal the fundamental role played by the strength of inter-reservoir connections to increasingly enrich the representations developed in higher layers. Our analysis also gives interesting insights into the possibility of effective exploitation of training algorithms based on stochastic gradient descent in the RC field.Comment: Preprint of the paper accepted at IWANN 201

Lower left temporal-frontal connectivity characterizes expert and accurate performance: High-alpha T7-Fz connectivity as a marker of conscious processing during movement

The Theory of Reinvestment argues that conscious processing can impair motor performance. The present study tested the utility of left temporal-frontal cortical connectivity as a neurophysiological marker of movement specific conscious processing. Expert and novice golfers completed putts while temporal-frontal connectivity was computed using high alpha Inter Site Phase Clustering (ISPC) and then analyzed as a function of experience (experts versus novices), performance (holed versus missed putts), and pressure (low versus high). Existing evidence shows that left temporal to frontal connectivity is related to dispositional conscious processing and is sensitive to the amount of declarative knowledge acquired during learning. We found that T7-Fz ISPC, but not T8-Fz ISPC, was lower in experts than novices, and lower when putts were holed than missed. Accordingly, our findings provide additional evidence that communication between verbal/language and motor areas of the brain during preparation for action and its execution is associated with poor motor performance. Our findings validate high-alpha left temporal-frontal connectivity as a neurophysiological correlate of movement specific conscious processin

Quiet eye and eye quietness: Electrooculographic methods to study ocular activity during motor skills

Camera-based eye tracking research has revealed that experts make longer fixations on the target of an action (e.g., the ball in golf putting) prior to and following movement onset, compared to novices. Yet it is not clear how ocular activity affects motor performance. It is possible that the limited temporal resolution of camera systems has held back progress on this issue. We analysed horizontal EOG (512 Hz, 0.1-30 Hz filtered) from ten expert and ten novice golfers as they putted 60 balls to a 2.4 m distant hole. We used multiple voltage thresholds to measure the duration of the final fixation (quiet eye; QE) with its pre- and post-movement onset components. We also measured ocular activity across time as the standard deviation of the EOG in 0.5 s bins, –4 to +2 s from movement onset (eye quietness; EQ): lower values correspond with greater quietness. Finally, we measured ball address and club swing durations using infrared and sound sensors. Total QE duration did not differ between groups. However, experts had shorter pre-movement QE and longer post-movement QE than novices. Experts had less EQ before movement onset and greater EQ after movement onset. EQ was inversely correlated with QE duration, concurrently validating EQ as an index of ocular activity. Experts had longer swing durations than novices. Swing duration correlated positively with post-movement QE and negatively with post-movement EQ. Our findings provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute motor skills

Conscious processing and cortico-cortical functional connectivity in golf putting

The Theory of Reinvestment argues that automated motor processes are disrupted when task-related declarative knowledge is used to control movement execution. Electroencephalographic (EEG) based high-alpha band (10-12 Hz) connectivity between the left temporal (verbal/analytic processing) area and the frontal (motor planning) area has been endorsed as a neurophysiological marker of the propensity for conscious processing of declarative knowledge during movement preparation. Our study investigated the utility of left temporal to frontal connectivity in characterizing optimal golf putting performance. Ten expert and ten novice right-handed male golfers putted 120 golf balls on a flat mat to a 2.4 m distant hole while the EEG was continuously recorded. Conscious processing was assessed by a putting-specific reinvestment scale. Functional connectivity in preparation to golf putts was computed as high-alpha inter site phase clustering (ISPC), and analyzed as a function of expertise (expert, novice), performance outcome (holed, missed) and psychological pressure (low, high). We found that left (but not right) temporal-frontal ISPC was lower in experts compared to novices (M experts = .39; M novices = .48). The experts also reported lower conscious processing compared to the novices (M experts = 2.80; M novices = 3.50). Furthermore, left temporal-frontal ISPC was higher in missed versus holed putts for experts (M holed= .37; M misses = .41) and novices (M holed = .44; M misses = .51). No pressure effect was revealed (M low = .42; M high = .45). Our findings suggest that experts engage in less conscious processing compared to novices, and, in line with the Theory of Reinvestment, suggest that errors in motor performance can be prompted by excessive conscious verbal/analytic interference with movement preparation and execution. Our study findings suggest that diminished communication between the left temporal (verbal/analytic) and the frontal (pre-motor) cortical areas during movement preparation and execution is a feature of skilled motor performance. This knowledge can now be used to design connectivity-based neurofeedback training protocols to expedite motor learning and improve motor skills