Raoul Huys

Global information pickup underpins anticipation of tennis shot direction

The authors examined the importance of local dynamical information when anticipating tennis shot direction. In separate experiments, they occluded the arm and racket, shoulders, hips, trunk, and legs and locally neutralized dynamical differences between shot directions, respectively. The authors examined the impact of these manipulations on resulting (display) dynamics and the ability of participants with varying perceptual skills to anticipate shot direction. The occlusion manipulation affected the display dynamics to a larger extent than did the neutralization manipulation. Although the authors observed a decrement in performance when local information from the arm and racket was occluded or neutralized and when information from the trunk and legs was neutralized, the results generally suggest that participants anticipated shot direction through a more global perceptual approach, particularly in perceptually skilled participants.

Changes in interictal spike features precede the onset of temporal lobe epilepsy

One cornerstone event during epileptogenesis is the occurrence of the first spontaneous seizure (SZ1). It is therefore important to identify biomarkers of the network alterations leading to SZ1. In experimental models of temporal lobe epilepsy (TLE), interictal‐like activity (ILA) precedes SZ1 by several days. The goal of this study was to determine whether ILA dynamics bore electrophysiological features signaling the impeding transition to SZ1.

Dynamical coupling between locomotion and respiration

Abstract.In search of the formative principles underwriting locomotor-respiratory coupling, we reanalyzed and modeled the data collected by Siegmund and coworkers (1999) on the synchronization of respiration during rowing. Apart from the frequency doubling in respiration reported earlier, detailed time-resolved spectral analyses revealed decreasing stability of entrainment close to abrupt changes in frequency relations as well as switches in the relative phase between respiration and locomotion. A single physiological, albeit mechanically constrained, quantity sufficed to explain the observed frequency and phase locking phenomena: the effective value of oxygen volume in the lungs. The cyclic abdominal pressure modulates the self-sustaining rhythmic respiration, modifies the total lung pressure, and causes (local) maxima at frequency ratios between movement and respiration that are composed of small integers. Hence, optimizing the effective oxygen volume can be seen as the mechanism that drives respiration to synchronize with locomotion.

Brain Dynamics of Aging: Multiscale Variability of EEG Signals at Rest and during an Auditory Oddball Task

Recently, the study of brain signal fluctuations is widely put forward as a promising entry point to characterize brain dynamics in health and disease. Although interesting results have been reported regarding how variability of brain activations can serve as an indicator of performance and adaptability in elderly, many uncertainties and controversies remain with regard to the comparability, reproducibility, and generality of the described findings, as well as the ensuing interpretations. Abstract The present work focused on the study of fluctuations of cortical activity across time scales in young and older healthy adults. The main objective was to offer a comprehensive characterization of the changes of brain (cortical) signal variability during aging, and to make the link with known underlying structural, neurophysiological, and functional modifications, as well as aging theories. We analyzed electroencephalogram (EEG) data of young and elderly adults, which were collected at resting state and during an auditory oddball task. We used a wide battery of metrics that typically are separately applied in the literature, and we compared them with more specific ones that address their limits. Our procedure aimed to overcome some of the methodological limitations of earlier studies and verify whether previous findings can be reproduced and extended to different experimental conditions. In both rest and task conditions, our results mainly revealed that EEG signals presented systematic age-related changes that were time-scale-dependent with regard to the structure of fluctuations (complexity) but not with regard to their magnitude. Namely, compared with young adults, the cortical fluctuations of the elderly were more complex at shorter time scales, but less complex at longer scales, although always showing a lower variance. Additionally, the elderly showed signs of spatial, as well as between, experimental conditions dedifferentiation. By integrating these so far isolated findings across time scales, metrics, and conditions, the present study offers an overview of age-related changes in the fluctuation electrocortical activity while making the link with underlying brain dynamics.

The multiscale entropy: Guidelines for use and interpretation in brain signal analysis

BACKGROUNDnMultiscale entropy (MSE) estimates the predictability of a signal over multiple temporal scales. It has been recently applied to study brain signal variability, notably during aging. The grounds of its application and interpretation remain unclear and subject to debate.nnnMETHODnWe used both simulated and experimental data to provide an intuitive explanation of MSE and to explore how it relates to the frequency content of the signal, depending on the amount of (non)linearity and stochasticity in the underlying dynamics.nnnRESULTSnThe scaling and peak-structure of MSE curves relate to the scaling and peaks of the power spectrum in the presence of linear autocorrelations. MSE also captures nonlinear autocorrelations and their interactions with stochastic dynamical components. The previously reported crossing of young and old adults MSE curves for EEG data appears to be mainly due to linear stochastic processes, and relates to young adults EEG dynamics exhibiting a slower time constant.nnnCOMPARISON WITH EXISTING METHODSnWe make the relationship between MSE curve and power spectrum as well as with a linear autocorrelation measure, namely multiscale root-mean-square-successive-difference, more explicit. MSE allows gaining insight into the time-structure of brain activity fluctuations. Its combined use with other metrics could prevent any misleading interpretations with regard to underlying stochastic processes.nnnCONCLUSIONSnAlthough not straightforward, when applied to brain signals, the features of MSE curves can be linked to their power content and provide information about both linear and nonlinear autocorrelations that are present therein.

When Less Is More: Reduced Usefulness Training for the Learning of Anticipation Skill in Tennis

Participants in this study practiced with feedback to anticipate the left-right direction of forehand tennis shots played by stick-figure players. A technique based on principal component analysis was used to remove dynamical differences that are associated with shots to different directions. Different body regions of the stick-figure players were neutralized with this procedure in the pretests and posttests, and in the practice phases. Experiment 1 showed that training is effective if during practice information is consistently present in the whole body of the player, but not if the information is neutralized in the whole body in half of the practice trials. Experiment 2 showed that training is effective if the variance associated with the direction of the shots is consistently present in one body region but neutralized in others, and that transfer occurs from practice with information in one body region to performance in conditions with information preserved only in other regions. Experiment 3 showed that occlusion has a much larger detrimental effect on learning than the applied neutralization technique, and that transfer between body regions occurs also with occlusion. Discussed are theoretical implications for understanding how biological motion is perceived and possible applications in a type of training referred to as reduced usefulness training.

Nonlinear Dynamics in Human Behavior

Humans engage in a seemingly endless variety of different behaviors, of which some are found across species, while others are conceived of as typically human. Most generally, behavior comes about through the interplay of various constraints informational, mechanical, neural, metabolic, and so on operating at multiple scales in space and time. Over the years, consensus has grown in the research community that, rather than investigating behavior only from bottom up, it may be also well understood in terms of concepts and laws on the phenomenological level. Such top down approach is rooted in theories of synergetics and self-organization using tools from nonlinear dynamics. The present compendium brings together scientists from all over the world that have contributed to the development of their respective fields departing from this background. It provides an introduction to deterministic as well as stochastic dynamical systems and contains applications to motor control and coordination, visual perception and illusion, as well as auditory perception in the context of speech and music.The history of research on speech perception and speech production is replete with examples of nonlinearities between articulation and acoustics, and between acoustics and perception. These nonlinearities are useful for communication. They allow 1) adequate production of speech sounds and words despite people having different vocal tracts with different resonance capabilities, and 2) adequate word recognition despite variation in the acoustic signal across speakers, emphasis, background noise, etc. Yet context and the listener’s expectancies often strongly influence what is perceived; perception is dynamic, influenced by multiple factors that change slowly or quickly as speech goes on. In this chapter we present a selected history of demonstrations of nonlinearities in speech and attempt to exploit the nonlinearities in order to uncover the dynamics of both perception and production of speech.

Discrete and rhythmic movements — Just a bifurcation apart?

Whether discrete and rhythmic movements result from the same or from separate dynamical structures is yet unclear. We discuss a robust albeit computationally demanding approach to tackle this issue. Our approach capitalizes on conventional time-delay embedding techniques followed by fitting coefficients of a Kramers-Moyal expansion to the so-defined multivariate data. This procedure allows for identifying the generating dynamical systems on basis of possibly non-stationary and noisy signals. We apply this to data from recent experiments in which movement tempo was systematically modified to pinpoint spontaneous switches from discrete to rhythmic movement and back again. It appears that both movement archetypes live in the same phase space but in distinct dynamical regimes.

Functional Architectures for Complex Behaviors: Analysis and Modeling of Interacting Processes in a Hierarchy of Time Scales

Synergetics’ applications in the sciences of cognition and behavior have focused on instabilities leading to phase transitions between competing behavioral or perceptual patterns. Inspired by this scientific tradition, functional architectures are proposed as a general theoretical framework aiming at modeling the nonstationary, multiscale dynamics of complex behaviors, beyond the neighborhood of instabilities. Such architectures consist of interacting dynamical processes, operating in a hierarchy of time scales and functionally differentiated according to their mutual time scale separations. Here, the mathematical formalism of functional architectures is presented and exemplified through simulations of cursive handwriting. Then, the implications for the analysis of complex behaviors are discussed.