scholarly journals An analytical method reduces noise bias in motor adaptation analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel H. Blustein ◽  
Ahmed W. Shehata ◽  
Erin S. Kuylenstierna ◽  
Kevin B. Englehart ◽  
Jonathon W. Sensinger
Keyword(s):  
Motor Planning ◽  
Motor Adaptation ◽  
Motor Command ◽  
Systematic Bias ◽  
Sources Of Information ◽  
Noise Masking ◽  
Stochastic Signal ◽  
Movement Data ◽  
Motor Error ◽  
Stochastic Signal Processing

AbstractWhen a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction, quantified as a trial-by-trial adaptation rate, provides insight into how the nervous system is operating, particularly regarding how much confidence a person places in different sources of information such as sensory feedback or motor command reproducibility. Traditional analysis has required carefully controlled laboratory conditions such as the application of perturbations or error clamping, limiting the usefulness of motor analysis in clinical and everyday environments. Here we focus on error adaptation during unperturbed and naturalistic movements. With increasing motor noise, we show that the conventional estimation of trial-by-trial adaptation increases, a counterintuitive finding that is the consequence of systematic bias in the estimate due to noise masking the learner’s intention. We present an analytic solution relying on stochastic signal processing to reduce this effect of noise, producing an estimate of motor adaptation with reduced bias. The result is an improved estimate of trial-by-trial adaptation in a human learner compared to conventional methods. We demonstrate the effectiveness of the new method in analyzing simulated and empirical movement data under different noise conditions.

scholarly journals Cutting through the noise: reducing bias in motor adaptation analysis

2020 ◽  
Author(s):  
Daniel H. Blustein ◽  
Ahmed W. Shehata ◽  
Erin S. Kuylenstierna ◽  
Kevin B. Englehart ◽  
Jonathon W. Sensinger
Keyword(s):  
Error Correction ◽  
Sensory Feedback ◽  
External Disturbance ◽  
Motor Planning ◽  
Motor Adaptation ◽  
Motor Command ◽  
Computational Method ◽  
Systematic Bias ◽  
Sources Of Information ◽  
Insight Into

AbstractDuring goal-directed movements, the magnitude of error correction by a person on a subsequent movement provides important insight into a person’s motor learning dynamics. Observed differences in trial-by-trial adaptation rates might indicate different relative weighting placed on the various sources of information that inform a movement, e.g. sensory feedback, control predictions, or internal model expectations. Measuring this trial-by-trial adaptation rate is not straightforward, however, since externally observed data are masked by noise from several sources and influenced by inaccessible internal processes. Adaptation to perturbation has been used to measure error adaptation as the introduced external disturbance is sufficiently large to overshadow other noise sources. However, perturbation analysis is difficult to implement in real-world scenarios, requires a large number of movement trials to accommodate infrequent perturbations, and the paradigm itself might affect the movement dynamics being observed. Here we focus on error adaptation during unperturbed and naturalistic movements. With increasing motor noise, the conventional estimation of trial-by-trial adaptation increases, a counterintuitive finding that is the consequence of systematic bias in the estimate due to noise masking the learner’s intention. We present an analytic solution relying on stochastic signal processing to reduce this effect of noise, producing an estimate of motor adaptation with reduced bias. The result is an improved estimate of trial-by-trial adaptation in a human learner compared to conventional methods. We demonstrate the effectiveness of the new method in analyzing simulated and empirical movement data under different noise conditions. The analytic approach is applicable across different types of movements in varied contexts and should replace the regression analysis method in future motor analysis studies.Author SummaryWhen a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction provides insight into how the nervous system is operating, particularly in regard to how much confidence a person places in different sources of information such as sensory feedback or motor command reproducibility. Traditional analysis of movement has required carefully controlled laboratory conditions, limiting the usefulness of motor analysis in clinical and everyday environments. Here we present a new computational method that can be accurately applied to typical movements. Counterintuitive findings of the established approach are corrected by the proposed method. This method will provide a common framework for researchers to analyze movements while extending dynamic motor adaptation analysis capabilities to clinical and non-laboratory settings.

scholarly journals Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass Spectrometry

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Hyunjin Shin ◽  
Miray Mutlu ◽  
John M. Koomen ◽  
Mia K. Markey
Keyword(s):  
Mass Spectrometry ◽  
Signal Processing ◽  
Mass Spectra ◽  
Thermal Noise ◽  
Electrical Noise ◽  
Noise Sources ◽  
Stochastic Signal ◽  
Power Spectral ◽  
Stochastic Signal Processing ◽  
Periodic Components

Noise in mass spectrometry can interfere with identification of the biochemical substances in the sample. For example, the electric motors and circuits inside the mass spectrometer or in nearby equipment generate random noise that may distort the true shape of mass spectra. This paper presents a stochastic signal processing approach to analyzing noise from electrical noise sources (i.e., noise from instrumentation) in MALDI TOF mass spectrometry. Noise from instrumentation was hypothesized to be a mixture of thermal noise, 1/f noise, and electric or magnetic interference in the instrument. Parametric power spectral density estimation was conducted to derive the power distribution of noise from instrumentation with respect to frequencies. As expected, the experimental results show that noise from instrumentation contains 1/f noise and prominent periodic components in addition to thermal noise. These periodic components imply that the mass spectrometers used in this study may not be completely shielded from the internal or external electrical noise sources. However, according to a simulation study of human plasma mass spectra, noise from instrumentation does not seem to affect mass spectra significantly. In conclusion, analysis of noise from instrumentation using stochastic signal processing here provides an intuitive perspective on how to quantify noise in mass spectrometry through spectral modeling.

scholarly journals Sub-optimality in motor planning is not improved by explicit observation of motor uncertainty

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Keiji Ota ◽  
Masahiro Shinya ◽  
Laurence T. Maloney ◽  
Kazutoshi Kudo
Keyword(s):  
Motor Planning ◽  
The Other ◽  
Insufficient Information ◽  
Reaching Task ◽  
Additional Information ◽  
Optimal Decisions ◽  
Decisions Under Risk ◽  
Motor Error ◽  
Better Than ◽  
Decision Tasks

Abstract To make optimal decisions under risk, one must correctly weight potential rewards and penalties by the probabilities of receiving them. In motor decision tasks, the uncertainty in outcome is a consequence of motor uncertainty. When participants perform suboptimally as they often do in such tasks, it could be because they have insufficient information about their motor uncertainty: with more information, their performance could converge to optimal as they learn their own motor uncertainty. Alternatively, their suboptimal performance may reflect an inability to make use of the information they have or even to perform the correct computations. To discriminate between these two possibilities, we performed an experiment spanning two days. On the first day, all participants performed a reaching task with trial-by-trial feedback of motor error. At the end of the day, their aim points were still typically suboptimal. On the second day participants were divided into two groups one of which repeated the task of the first day and the other of which repeated the task but were intermittently given additional information summarizing their motor errors. Participants receiving additional information did not perform significantly better than those who did not.

Motor planning and sparse motor command representation

2007 ◽  
Vol 70 (10-12) ◽  
pp. 1748-1752 ◽  
Author(s):  
Yutaka Sakaguchi ◽  
Shiro Ikeda
Keyword(s):  
Motor Planning ◽  
Motor Command

Stochastic Signal Processing

1988 ◽  
pp. 34-76
Author(s):  
Edward A. Lee ◽  
David G. Messerschmitt
Keyword(s):  
Signal Processing ◽  
Stochastic Signal ◽  
Stochastic Signal Processing

scholarly journals Elimination of the error signal in the superior colliculus impairs saccade motor learning

2018 ◽  
Vol 115 (38) ◽  
pp. E8987-E8995 ◽  
Author(s):  
Yoshiko Kojima ◽  
Robijanto Soetedjo
Keyword(s):  
Superior Colliculus ◽  
Motor Adaptation ◽  
Causal Link ◽  
Current Evidence ◽  
Error Signal ◽  
Drive Motor ◽  
Motor Error ◽  
Saccade Adaptation ◽  
Complex Spikes ◽  
Plastic Change

When movements become dysmetric, the resultant motor error induces a plastic change in the cerebellum to correct the movement, i.e., motor adaptation. Current evidence suggests that the error signal to the cerebellum is delivered by complex spikes originating in the inferior olive (IO). To prove a causal link between the IO error signal and motor adaptation, several studies blocked the IO, which, unfortunately, affected not only the adaptation but also the movement itself. We avoided this confound by inactivating the source of an error signal to the IO. Several studies implicate the superior colliculus (SC) as the source of the error signal to the IO for saccade adaptation. When we inactivated the SC, the metrics of the saccade to be adapted were unchanged, but saccade adaptation was impaired. Thus, an intact rostral SC is necessary for saccade adaptation. Our data provide experimental evidence for the cerebellar learning theory that requires an error signal to drive motor adaptation.

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