Contemporary theories of 1/f noise in motor control (original) (raw)

On the origins of 1/f noise in human motor behavior

Journal of Sport and Exercise Psychology, 2008

This ubiquity is per se an intriguing phenomenon, and the origin of such fluctuations remains in question. One considers that this very specific kind of fluctuation plays an essential role in the stability of behavior, and in the adaptability and flexibility of organisms. A number of hypotheses have been proposed for accounting for this phenomenon. Currently, two categories of explanations can be discerned:

A theory of 1 / f noise in human cognition

Physica A-statistical Mechanics and Its Applications, 2009

The brain is probably the most interesting example of a complex network having 1/f variability as determined through the analysis of EEG time series and magnetoencephalogram recordings. Herein we develop a theory of 1/f noise of human cognition to explain the recent experimental observations that increasing the difficultly of cognitive tasks accelerates the transition from observed 1/f noise to white noise in decision-making time series.

Theories and models for 1/fβ noise in human movement science

Human Movement Science, 2009

Human motor behavior is often characterized by long-range, slowly decaying serial correlations or 1/f b noise. Despite its prevalence, the role of the 1/f b phenomenon in human movement research has been rather modest and unclear. The goal of this paper is to outline a research agenda in which the study of 1/f b noise can contribute to scientific progress. In the first section of this article we discuss two popular perspectives on 1/f b noise: the nomothetic perspective that seeks general explanations, and the mechanistic perspective that seeks domain-specific models. We believe that if 1/f b noise is to have an impact on the field of movement science, researchers should develop and test domain-specific mechanistic models of human motor behavior. In the second section we illustrate our claim by showing how a mechanistic model of 1/f b noise can be successfully integrated with currently established models for rhythmic self-paced, synchronized, and bimanual tapping. This model synthesis results in a unified account of the observed longrange serial correlations across a range of different tasks.

Long-range correlation properties in motor timing are individual and task specific

2011

Abstract 1/f β noise represents a specific form of (long-range) correlations in a time series that is pervasive across many sensorimotor variables. Recent studies have shown that the precise properties of the correlations demonstrated by a group of test participants may vary as a function of experimental conditions or factors characterizing the group.

Noise-driven activation in human intermittent control: a double-well potential model

In controlling unstable systems humans switch intermittently between the passive and active behavior instead of controlling the system in a continuous manner. The notion of noise-driven control activation provides a richer alternative to the conventional threshold-based models of intermittent motor control. The present study represents the control activation as a random walk in a continuously changing double-well potential. The match between the proposed model and the previous data on human balancing of virtual stick prompts that the double-well approach can aid in explaining complex dynamics of human behavior in control processes.

Neuromotor Noise Is Malleable by Amplifying Perceived Errors

Variability in motor performance results from the interplay of error correction and neuromo-tor noise. This study examined whether visual amplification of error, previously shown to improve performance, affects not only error correction, but also neuromotor noise, typically regarded as inaccessible to intervention. Seven groups of healthy individuals, with six participants in each group, practiced a virtual throwing task for three days until reaching a performance plateau. Over three more days of practice, six of the groups received different magnitudes of visual error amplification; three of these groups also had noise added. An additional control group was not subjected to any manipulations for all six practice days. The results showed that the control group did not improve further after the first three practice days, but the error amplification groups continued to decrease their error under the manipulations. Analysis of the temporal structure of participants' corrective actions based on sto-chastic learning models revealed that these performance gains were attained by reducing neuromotor noise and, to a considerably lesser degree, by increasing the size of corrective actions. Based on these results, error amplification presents a promising intervention to improve motor function by decreasing neuromotor noise after performance has reached an asymptote. These results are relevant for patients with neurological disorders and the elderly. More fundamentally, these results suggest that neuromotor noise may be accessible to practice interventions. It is widely recognized that neuromotor noise limits human motor performance, generating errors and variability even in highly skilled performers. Arising from many spatiotem-poral scales within the physiological system, the intrinsic noise component is commonly assumed to be invariant by most computational models of human neuromotor control. We challenge this assumption and show that after an individual has reached a performance plateau, amplifying perceived errors elicits continued reductions in observed

A theory of noise in human cognition

Physica A: Statistical Mechanics and its Applications, 2009

Complexity in Neurobiology: Perspectives from the study of noise in human motor systems

Critical Reviews in Biomedical Engineering, 2012

This article serves as an introduction to the themed special issue on "Complex Systems in Neurobiology." The study of complexity in neurobiology has been sensitive to the stochastic processes that dominate the micro-level architecture of neurobiological systems and the deterministic processes that govern the macroscopic behavior of these systems. A large body of research has traversed these scales of interest, seeking to determine how noise at one spatial or temporal scale influences the activity of the system at another scale. In introducing this special issue, we pay special attention to the history of inquiry in complex systems and why scientists have tended to favor linear, causally driven, reductionist approaches in Neurobiology. We follow this with an elaboration of how an alternative approach might be formulated. To illustrate our position on how the sciences of complexity and the study of noise can inform neurobiology, we use three systematic examples from the study of human motor control and learning: 1) phase transitions in bimanual coordination; 2) balance, intermittency, and discontinuous control; and 3) sensorimotor synchronization and timing. Using these examples and showing that noise is adaptively utilized by the nervous system, we make the case for the studying complexity with a perspective of understanding the macroscopic stability in biological systems by focusing on component processes at extended spatial and temporal scales. This special issue continues this theme with contributions in topics as diverse as neural network models, physical biology, motor learning, and statistical physics.

Neuromotor Noise, Error Tolerance and Velocity-Dependent Costs in Skilled Performance

PLoS Computational Biology, 2011

In motor tasks with redundancy neuromotor noise can lead to variations in execution while achieving relative invariance in the result. The present study examined whether humans find solutions that are tolerant to intrinsic noise. Using a throwing task in a virtual set-up where an infinite set of angle and velocity combinations at ball release yield throwing accuracy, our computational approach permitted quantitative predictions about solution strategies that are tolerant to noise. Based on a mathematical model of the task expected results were computed and provided predictions about error-tolerant strategies (Hypothesis 1). As strategies can take on a large range of velocities, a second hypothesis was that subjects select strategies that minimize velocity at release to avoid costs associated with signal-or velocity-dependent noise or higher energy demands (Hypothesis 2). Two experiments with different target constellations tested these two hypotheses. Results of Experiment 1 showed that subjects chose solutions with high error-tolerance, although these solutions also had relatively low velocity. These two benefits seemed to outweigh that for many subjects these solutions were close to a high-penalty area, i.e. they were risky. Experiment 2 dissociated the two hypotheses. Results showed that individuals were consistent with Hypothesis 1 although their solutions were distributed over a range of velocities. Additional analyses revealed that a velocity-dependent increase in variability was absent, probably due to the presence of a solution manifold that channeled variability in a taskspecific manner. Hence, the general acceptance of signal-dependent noise may need some qualification. These findings have significance for the fundamental understanding of how the central nervous system deals with its inherent neuromotor noise.

Contemporary theories of 1/f noise in motor control (original) (raw)

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