Authors:
Neri Accornero
and
Marco Capozza
Affiliation:
Sapienza University, Italy
Keyword(s):
Ideomotor Principle, Intentional Movement, Motor Learning, Artificial Neural Network, Simulation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Bio-Inspired and Humanoid Robotics
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neuroinformatics and Bioinformatics
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Although the ideomotor principle (IMP), the notion positing that the nervous system initiates voluntary actions by anticipating their sensory effects, has long been around it still struggles to gain widespread acknowledgement. Supporting this theory, we present an artificial neural network model driving a simulated arm, designed as simply as possible to focus on the essential IMP features, that demonstrates by simulation how the IMP could work in biological intentional movement and motor learning. The simulation model shows that IMP motor learning is fast and effective and shares features with human motor learning. An IMP extension offers new insights into the so-called mirror neuron and canonical neuron systems.