In recent decades, research has focused on motor adjustments in interception tasks within predictable environments. However, emerging studies suggest that continued practice beyond performance stabilization enhances the ability to adapt to unpredictable events. The objective of this study was to investigate the effects of practicing until performance stabilization versus extended practice through superstabilization on the ability to adjust to unpredictable perturbations in intercepting a moving target. We hypothesized superstabilization would better facilitate motor adjustments in response to unpredictable perturbations. Forty participants engaged in an interception task until they achieved either performance stabilization or superstabilization. Subsequently, both stabilization and superstabilization groups were tested in an unpredictable environment, where, in certain trials, the target's velocity unexpectedly changed after the onset of the movement. The findings revealed that the superstabilization group made more adjustments, showing more number of corrections (N-cor), in response to these perturbations than the stabilization group, attributed to their developed capacity to use online feedback as a control mechanism more efficiently. In contrast, the practice until performance stabilization did not foster this adaptive mechanism. These results support the notion that learning is a dynamic process that extends beyond the point of performance stabilization, emphasizing the benefits of continued practice for mastering motor tasks in variable contexts.

Performing a motor task depends on the level of performance stabilization and movement control, and both aspects of motor behavior are related to motor learning (retention and transfer) and adaptation (predictable and unpredictable perturbations). Yet few studies have further investigated the underlying dynamics that may elicit these benefits. In this study, we investigated the effects of two levels of performance stabilization on motor performance and control while learning to intercept a virtual moving target. We randomly divided 40 participants of both sexes ( M age = 26.02 years, SD = 2.02) into a Stabilization Group (SG) and a Superstabilization Group (SSG). We considered the performance stabilized when a moving target was intercepted three times in a row and superstabilized when the same criterion was repeated six times. We analyzed outcome variables related to performance accuracy (absolute spatial error) and variability(coefficient of variation) and motor control (relative time to peak velocity-tPV% and its coefficient of variation) on both the first and last blocks of practice trials. Both groups showed comparable increases in performance accuracy from the first to the last block ( p = .001, η p 2 = 1.00), but SSG presented higher variability than SG ( p = .05, η p 2 = .70). Concerning motor control, both groups started the experiment with low tPV% and finished with comparably high tPV% and variability. Thus, although practicing two levels of performance stabilization led to similar performance accuracy and movement control, superstabilization resulted in higher performance variability with no loss of accuracy. Enhanced stabilization may increase the ability to adapt to environmental changes, but more research is needed to demonstrate this. These findings add to an understanding of the relationship between levels of performance stabilization and performance variability and may have implications for professional interventions (e.g. sports, rehabilitation) in considering the benefits of practice beyond performance stabilization.

Phase coherence is an important measure in nonlinear science. Whereas there is no generally accepted definition for phase and therefore for phase coherence, many works associate this feature with topological aspects of the systems, such as having a well-defined rotating center. Given the relevance of this concept for synchronization problems, one aim of this paper is to argue by means of a couple of counterexamples that phase coherence is not related to the topology of the attractor. A second aim is to introduce a phase-coherence measure based on recurrence plots, for which probabilities of recurrences for two different trajectories are similar for a phase-coherent system and dissimilar for non-phase-coherent systems. The measure does not require a phase variable defined a priori.

Structural changes in a network representation of a system, due to different experimental conditions, different connectivity across layers, or to its time evolution, can provide insight on its organization, function, and on how it responds to external perturbations. The deeper understanding of how gene networks cope with diseases and treatments is maybe the most incisive demonstration of the gains obtained through this differential network analysis point of view, which led to an explosion of new numeric techniques in the last decade. However, where to focus one's attention, or how to navigate through the differential structures in the context of large networks, can be overwhelming even for a few experimental conditions. In this paper, we propose a theory and a methodological implementation for the characterization of shared "structural roles" of nodes simultaneously within and between networks. Inspired by recent methodological advances in chaotic phase synchronization analysis, we show how the information about the shared structures of a set of networks can be split and organized in an automatic fashion, in scenarios with very different (i) community sizes, (ii) total number of communities, and (iii) even for a large number of 100 networks compared using numerical benchmarks generated by a stochastic block model. Then, we investigate how the network size, number of networks, and mean size of communities influence the method performance in a series of Monte Carlo experiments. To illustrate its potential use in a more challenging scenario with real-world data, we show evidence that the method can still split and organize the structural information of a set of four gene coexpression networks obtained from two cell types × two treatments (interferon-β stimulated or control). Aside from its potential use as for automatic feature extraction and preprocessing tool, we discuss that another strength of the method is its "story-telling"-like characterization of the information encoded in a set of networks, which can be used to pinpoint unexpected shared structure, leading to further investigations and providing new insights. Finally, the method is flexible to address different research-field-specific questions, by not restricting what scientific-meaningful characteristic (or relevant feature) of a node shall be used.

Abstract One of the challenges faced by companies in the oil and gas industry is the difficulty in assessing and quantifying subsurface uncertainties when planning for hydrocarbon exploitation. A commonly employed approach is to use available exploration and appraisal data to produce a range of possible subsurface realisations, through which hydrocarbon production forecasts are generated. Prediction of hydrocarbon production from these simulations are then used to assess the viability of a planned development concept and the associated subsurface uncertainties. However, due to the sparsity of field data and unpredictability of underground geology coupled with the typically large dataset sizes, the ability to rapidly quantify prediction uncertainty and provide an overview of the range of underground geologies leaves much to be desired. Here we show that the application of a network science approach to oil production prediction data provides an intuitive way to visualize and assess reservoir uncertainty. A network transformation utilizing Pearson correlation and mean absolute error as similarity measures were applied to a dataset containing time series predictions of oil production for 10 wells simulated in 50 different subsurface realisations. Realisations were generated using a synthetic reservoir spanning 20 years of production. It was found that the network representation enabled the inference of reservoir uncertainty by simple visual inspection. Additionally, network measures such as the beta index were used with results supporting their viability in quantifying uncertainty. The application of clustering algorithms to the resulting networks was also shown to simplify the time series into component characteristic subsurface realisations. We propose a method to quantify subsurface uncertainties and create a simplified representation of the characteristic modes associated with a range of subsurface realisations, greatly reducing the time required to conduct a cursory analysis. Our results demonstrate how the application of network science ideas may be applied to provide new ways of analyzing production data and performace predictions. These methods provide an opportunity to further refine descriptions of uncertainty by incorporating stochastic elements into the network as well.

Multivariate singular spectrum analysis (M-SSA), with a structured varimax rotation, is a method that allows a deep characterization of phase synchronization (PS) phenomena in an almost automatic fashion. It has been increasingly used in the study of PS in networks of nonlinear, real-world, and numeric systems. This paper investigates the impact of the other recently developed structured orthomax rotations on the M-SSA ability to characterize PS. The results show that by using the structured quartimax rotation, a very faint and intermittent PS regime can be detected, in contrast with the structured varimax (which demands a stronger, more consolidated PS regime). This is due to the fact that the different rotations do not have the same efficiency in achieving a simple structure of the M-SSA eigenvectors. Nevertheless, for well-established PS regimes, the same robustness of the original M-SSA approach against high levels of additive Gaussian noise was found for the structured quartimax and biquartimax rotations. However, for all approaches we found an overshoot of the qualitative range for the PS onset due to noise.

Allowing learners to control feedback has been an effective strategy in motor skills learning. However, most studies of self-controlled (SC) feedback have used simple tasks that may be dissimilar to sports skills that generally demand more degrees of freedom and cognition. Thus, this study investigated the effects of SC knowledge of results (KR) on learning a complex Taekwondo skill. Twenty-four undergraduate volunteers of both sexes, aged 18-35 years, practiced a specific serial Taekwondo skill that was novel to them. We divided participants randomly into SC and yoked groups and compared their performance after they learned a specific displacement sequence, finishing with a lateral kick (bandal-tchagui) at a punching bag within a target time span. During acquisition, all participants performed 48 trials divided into six blocks and, on a retention test 24 hours later, they performed 10 more trials. We found that both groups reduced their errors from the first to the last block of the acquisition phase and that the SC group showed a better performance on the retention test, relative to the yoked control group. SC KR participants requested KR mainly after good trials, though they showed no statistically significant differences between trials with and without KR. Their inefficiency in estimating their own errors may have been due to task complexity, since many aspects of the task beyond its temporal requirement demanded the learners' attention. Our results, using a novel Taekwondo serial skill, confirm and extend the benefits of SC KR from just simple motor learning in past studies to learning complex motor skills.

Multivariate singular spectrum analysis (M-SSA) is a useful tool to detect phase synchronization (PS) without any a priori need for phase estimation. The discriminatory power of M-SSA is often enhanced by using only the time series of the variable that provides the best observability of the dynamics. In the case of a network, however, diverse factors could prevent access to this variable at some nodes. Hence, other variables should be used instead, resulting in a mixed set of variables. The aim of the present work is to investigate, in a systematic way, the impact of using a mixed/incomplete measurement set in the M-SSA of chains of Rössler systems and cord oscillators. Results show that (i) the measurement of some variable from all oscillators does not guarantee detection of PS; (ii) typically one good observable per cluster should be recorded in order to detect PS among such clusters and that (iii) dropping poor variables does not reveal new PS transitions but improves on the resolution of what was already seen with such variables. The procedure is robust to noise.

Recurrence network analysis (RNA) is a remarkable technique for the detection of dynamical transitions in experimental applications. However, in practical experiments, often only a scalar time series is recorded. This requires the state-space reconstruction from this single time series which, as established by embedding and observability theory, is shown to be hampered if the recorded variable conveys poor observability. In this work, we investigate how RNA metrics are impacted by the observability properties of the recorded time series. Following the framework of Zou et al. [Chaos 20, 043130 (2010)], we use the Rössler and Duffing-Ueda systems as benchmark models for our study. It is shown that usually RNA metrics perform badly with variables of poor observability as for recurrence quantification analysis. An exception is the clustering coefficient, which is rather robust to observability issues. Along with its efficacy to detect dynamical transitions, it is shown to be an efficient tool for RNA—especially when no prior information of the variable observability is available.

Inappropriate patient-ventilator interactions' (PVI) quality is associated with adverse clinical consequences, such as patient anxiety/fear and increased need of sedative and paralytic agents. Thus, technological devices/tools to support the recognition and monitoring of different PVI quality are of great interest. In the present study, we investigate two tools based on a recent landmark study which applied recurrence plots (RPs) and recurrence quantification analysis (RQA) techniques in non-invasive mechanical ventilation. Our interest is in how this approach could be a daily part of critical care professionals' routine (which are not familiar with dynamical systems theory methods and concepts). Two representative time series of three typical PVI "scenarios" were selected from 6 critically ill patients subjected to invasive mechanical ventilation. First, both the (i) main signatures in RPs and the (ii) respective signals that provide the most (visually) discriminant RPs were identified. This allows one to propose a visual identification protocol for PVIs' quality through the RPs' overall aspect. Support for the effectiveness of this visual based assessment tool is given by a RQA-based assessment tool. A statistical analysis shows that both the recurrence rate and the Shannon entropy are able to identify the selected PVI scenarios. It is then expected that the development of an objective method can reliably identify PVI quality, where the results corroborate the potential of RPs/RQA in the field of respiratory pattern analysis.

Classical definitions of observability classify a system as either being observable or not. Observability has been recognized as an important feature to study complex networks, and as for dynamical systems the focus has been on determining conditions for a network to be observable. About twenty years ago continuous measures of observability for nonlinear dynamical systems started to be used. In this paper various aspects of observability that are established for dynamical systems will be investigated in the context of networks. In particular it will be discussed in which ways simple networks can be ranked in terms of observability using continuous measures of such a property. Also it is pointed out that the analysis of the network topology is typically not sufficient for observability purposes, since both the dynamics and the coupling of such nodes play a vital role. Some of the main ideas are illustrated by means of numerical simulations.

Observability is the property that enables recovering the state of a dynamical system from a reduced number of measured variables. In high-dimensional systems, it is therefore important to make sure that the variable recorded to perform the analysis conveys good observability of the system dynamics. The observability of a network of neuron models depends nontrivially on the observability of the node dynamics and on the topology of the network. The aim of this paper is twofold. First, to perform a study of observability using four well-known neuron models by computing three different observability coefficients. This not only clarifies observability properties of the models but also shows the limitations of applicability of each type of coefficients in the context of such models. Second, to study the emergence of phase synchronization in networks composed of neuron models. This is done performing multivariate singular spectrum analysis which, to the best of the authors' knowledge, has not been used in the context of networks of neuron models. It is shown that it is possible to detect phase synchronization: (i) without having to measure all the state variables, but only one (that provides greatest observability) from each node and (ii) without having to estimate the phase.

Este estudo teve como objetivos avaliar o controle postural de jovens e idosos em relação à direção predominante e à área de deslocamento do centro de pressão, buscando verifi car se diferentes bases de suporte evidenciam diferenças entre os grupos para estas duas variáveis. A amostra foi composta por oito adultos jovens (22,6±2,33 anos) e oito idosos (75,4±5,7 anos), e a tarefa consistiu em permanecer de pé sobre uma plataforma de força por 30 segundos, em duas bases de suporte: normal (BSN) e semi-tandem (BSST), sendo realizadas três tentativas em cada uma delas. Avaliou-se as variáveis “área” e “direção” de oscilação, ambas derivadas no centro de pressão. Os resultados mostraram que idosos apresentaram maior oscilação corporal que jovens e a restrição na base de suporte ampliou a oscilação de ambos os grupos etários. Conclui-se que idosos e jovens apresentam comportamentos distintos em relação à oscilação corporal e que o aumento na difi culdade da tarefa é capaz de evidenciar parcialmente essas diferenças.

BACKGROUND There is aconsensus that repetition observed in constant practice producesminimalbenefits to the transfer of learning. OBJECTIVE The purpose of this study was to investigatein 3 experiments the effects of constant practicein transfer contexts. METHODOLOGY Participants were asked during acquisition phase, in all experiments, to press four keys sequentially with different requirements of absolute timing in a same relative timing structure. In the transfer tests, they were tested in a novel absolute timing criterion. RESULTS The results of experiment 1 and 2 showed that the relative timing structure was maintained only when the transfer required parameter scaling close to the parameter value practiced in acquisition. The transfer parameter that is far to the parameter practiced did not affect the movement parameterization. The result of experiment 3 showed that relative timing structure is disrupted in the transfer test when constant practice has high and low amount of practice. CONCLUSION Some specific aspects interfere in the transfer test when constant practice is experienced.

Multivariate singular spectrum analysis (M-SSA) was recently adapted to study systems of coupled oscillators. It does not require an a priori definition for phase nor detailed knowledge of the individual oscillators, but it uses all the variables of each system. This aspect could be restrictive for practical applications, since usually just a few (sometimes only one) variables are measured. Based on dynamical systems and observability theories, we first show how to apply the M-SSA with only one variable and show the conditions to achieve good performance. Next, we provide numerical evidence that this single-variable approach enhances the explanatory power compared to the original M-SSA when computed with all the system variables. This could have important practical implications, as pointed out using benchmark oscillators.

Groth and Ghil [Phys. Rev. E 84, 036206 (2011)PLEEE81539-375510.1103/PhysRevE.84.036206] developed a modified varimax rotation aimed at enhancing the ability of the multivariate singular spectrum analysis (M-SSA) to characterize phase synchronization in systems of coupled chaotic oscillators. Due to the special structure of the M-SSA eigenvectors, the modification proposed by Groth and Ghil imposes a constraint in the rotation of blocks of components associated with the different subsystems. Accordingly, here we call it a structured varimax rotation (SVR). The SVR was presented as successive pairwise rotations of the eigenvectors. The aim of this paper is threefold. First, we develop a closed matrix formulation for the entire family of structured orthomax rotation criteria, for which the SVR is a special case. Second, this matrix approach is used to enable the use of known singular value algorithms for fast computation, allowing a simultaneous rotation of the M-SSA eigenvectors (a Python code is provided in the Appendix). This could be critical in the characterization of phase synchronization phenomena in large real systems of coupled oscillators. Furthermore, the closed algebraic matrix formulation could be used in theoretical studies of the (modified) M-SSA approach. Third, we illustrate the use of the proposed singular value algorithm for the SVR in the context of the two benchmark examples of Groth and Ghil: the Rössler system in the chaotic (i) phase-coherent and (ii) funnel regimes. Comparison with the results obtained with Kaiser's original (unstructured) varimax rotation (UVR) reveals that both SVR and UVR give the same result for the phase-coherent scenario, but for the more complex behavior (ii) only the SVR improves on the M-SSA.

Objectives: We compared the spatial concepts given to sounds' directions by blind football players with both blind non-athletes and sighted individuals.Method: Participants verbally described the directions of sounds around them by using predefined spatial concept labels, under two blocked conditions: 1) facing front, 2) pointing with the hand towards the stimulus.Results: Blind football players categorized the directions more precisely (i.e., they used simple labels for describing the cardinal directions and combined labels for the intermediate ones) than the other groups, and their categorization was less sensitive to the response conditions than blind non-athletes. Sighted participants' categorization was similar to previous studies, in which the front and back regions were generally more precisely described than the sides, where simple labels were often used for describing directions around the absolute left and right.Conclusions: The differences in conceptual categorization of sound directions are a) in sighted individuals, influenced by the representation of the visual space b) in blind individuals, influenced by the level of expertise in action and locomotion based on non-visual information, which can be increased by auditive stimulation Provided by blind football training. (C) 2015 Elsevier Ltd. All rights reserved.