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Erschienen in: Experimental Brain Research 2/2008

01.06.2008 | Research Article

Quantitative model of transport-aperture coordination during reach-to-grasp movements

verfasst von: Miya K. Rand, Y. P. Shimansky, Abul B. M. I. Hossain, George E. Stelmach

Erschienen in: Experimental Brain Research | Ausgabe 2/2008

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Abstract

It has been found in our previous studies that the initiation of aperture closure during reach-to-grasp movements occurs when the hand distance to target crosses a threshold that is a function of peak aperture amplitude, hand velocity, and hand acceleration. Thus, a stable relationship between those four movement parameters is observed at the moment of aperture closure initiation. Based on the concept of optimal control of movements (Naslin 1969) and its application for reach-to-grasp movement regulation (Hoff and Arbib 1993), it was hypothesized that the mathematical equation expressing that relationship can be generalized to describe coordination between hand transport and finger aperture during the entire reach-to-grasp movement by adding aperture velocity and acceleration to the above four movement parameters. The present study examines whether this hypothesis is supported by the data obtained in experiments in which young adults performed reach-to-grasp movements in eight combinations of two reach-amplitude conditions and four movement-speed conditions. It was found that linear approximation of the mathematical model described the relationship among the six movement parameters for the entire aperture-closure phase with very high precision for each condition, thus supporting the hypothesis for that phase. Testing whether one mathematical model could approximate the data across all the experimental conditions revealed that it was possible to achieve the same high level of data-fitting precision only by including in the model two additional, condition-encoding parameters and using a nonlinear, artificial neural network-based approximator with two hidden layers comprising three and two neurons, respectively. This result indicates that transport-aperture coordination, as a specific relationship between the parameters of hand transport and finger aperture, significantly depends on the condition-encoding variables. The data from the aperture-opening phase also fit a linear model, whose coefficients were substantially different from those identified for the aperture-closure phase. This result supports the above hypothesis for the aperture-opening phase, and consequently, for the entire reach-to-grasp movement. However, the fitting precision was considerably lower than that for the aperture-closure phase, indicating significant trial-to-trial variability of transport-aperture coordination during the aperture-opening phase. Implications for understanding the neural mechanisms employed by the CNS for controlling reach-to-grasp movements and utilization of the mathematical model of transport-aperture coordination for data analysis are discussed.
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Fußnoten
1
The least complex approximator is the one with the smallest number of coefficients requiring optimization to fit the data. The simplest possible approximator is a linear function, where the optimization of the coefficients is made through linear regression. It corresponds to an artificial neural network consisting of only one neuron that computes a weighed sum of the inputs.
 
2
To someone who is used to thinking about motor control in terms of kinematic parameters as continuous sequences of values within a specific time interval, it might seem that, since, for instance, acceleration as a function of time can be computed as a time derivative of velocity, it must be sufficient to include only one such parameter in equations. In the case of the equation describing transport-aperture coordination, however, instantaneous values of such parameters are involved, and therefore, a different logic applies. Knowledge of hand velocity at a certain time point t in general does not allow one to calculate hand acceleration and vice versa. For this reason, these kinematic variables are viewed in theoretical mechanics as state coordinates independent of each other.
 
3
This was so apparently due to the fact that the number of patterns for ANN training was very large (10,000) compared to the number of unknown coefficients (i.e. synaptic weights), which was not greater than 35 for the set of ANN candidates. Therefore, the probability of overfitting was practically zero.
 
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Metadaten
Titel
Quantitative model of transport-aperture coordination during reach-to-grasp movements
verfasst von
Miya K. Rand
Y. P. Shimansky
Abul B. M. I. Hossain
George E. Stelmach
Publikationsdatum
01.06.2008
Verlag
Springer-Verlag
Erschienen in
Experimental Brain Research / Ausgabe 2/2008
Print ISSN: 0014-4819
Elektronische ISSN: 1432-1106
DOI
https://doi.org/10.1007/s00221-008-1361-5

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