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Coordinated force production in multi-finger tasks: finger interaction and neural network modeling
V M Zatsiorsky1, Z M Li, M L Latash
1Department of Kinesiology, Pennsylvania State University, University Park 16802, USA. vxzl@psu.edu
Biological Cybernetics
|October 29, 1998
Summary
Finger enslaving, where involuntary force is produced by non-task fingers during maximal voluntary contraction (MVC), is substantial and influenced by neural factors. A neural network model reveals no direct command-force relationship for individual fingers.
Area of Science:
- Neuroscience
- Biomechanics
- Motor Control
Background:
- Maximal voluntary contraction (MVC) involves complex finger interactions: force sharing, force deficit, and involuntary finger activation (enslaving).
- Enslaving effects (EE) quantify involuntary force production by non-task fingers during multi-finger tasks.
Purpose of the Study:
- To investigate the characteristics of involuntary force production (enslaving effects) by individual fingers during maximal voluntary pressing tasks.
- To develop and validate a neural network model explaining the observed finger enslaving phenomena.
Main Methods:
- Isometric force measurements from 10 subjects pressing with one to four fingers in various combinations.
- Analysis of enslaving effects (EE) including magnitude, symmetry, and additivity.
- Development and validation of a three-layer neural network model simulating central neural drive, muscle activation, and finger force output.
Main Results:
- Enslaving effects were significant, with slave fingers producing 10.9%–54.7% of maximal single-finger force.
- EE were nearly symmetrical, larger for neighboring fingers, and non-additive, with 'occlusion' observed where multi-finger EE were smaller than single-finger EE.
- The neural network model successfully accounted for sharing, force deficit, and enslaving, indicating substantial neural contributions beyond anatomical connections.
Conclusions:
- Involuntary finger enslaving is a significant phenomenon in multi-finger force production, driven by both anatomical and substantial neural factors.
- A neural network model demonstrates that finger force output is not directly proportional to the neural command for an individual finger.
- Effective control of individual finger force requires scaling commands based on the activation of other fingers, highlighting the complexity of motor control.