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Schema generation in recurrent neural nets for intercepting a moving target.

Andreas G Fleischer¹

¹Department Biology, University Hamburg, Informatikum Vogt-Kölln-Strasse 30, 22527, Hamburg, Germany. fleischer@biokybernetik.uni-hamburg.de

Biological Cybernetics

|April 1, 2010

Summary

Motor schemata help intercept moving targets by predicting trajectories, but require visual feedback for precision. A neural network model simulates this motor control for improved movement strategies.

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Area of Science:

Neuroscience
Robotics
Motor Control

Background:

Grasping moving objects necessitates predictive motor strategies for interception.
Motor schemata, or preprogrammed trajectories, can simplify control during perception-action cycles.

Purpose of the Study:

To investigate the role and precision of motor schemata in intercepting moving targets.
To model the interaction between motor schemata and feedback control using a neural network.

Main Methods:

Participants intercepted a target moving on a circular path using a cursor.
Target visibility and cursor feedback were manipulated to assess prediction capabilities.
A Wilson-type neural network with an excitation-diffusion layer was developed to model the motor schema.

Main Results:

Motor schemata enable aiming ahead and adapting to target trajectories but have limited precision.
Continuous visual feedback is essential for successful interception of moving targets.
The neural network model demonstrated an iterative interaction between motor schemata and feedback control.

Conclusions:

Motor schemata are crucial for forward control in intercepting moving objects.
The developed neural network effectively models the integration of predictive schemata and corrective feedback.
Further research is needed to embed motor schemata within generalized control strategies.