Electronic Resource
Oxford, UK
:
Blackwell Publishing Ltd
Computational intelligence
3 (1987), S. 0
ISSN:
1467-8640
Source:
Blackwell Publishing Journal Backfiles 1879-2005
Topics:
Computer Science
Notes:
In this paper we present a computational theory of human motor performance and learning. The theory is implemented as a running AI system called MAGGIE. Given a description of a desired movement as input, the system generates simulated motor behavior as output. The theory states mat skills are encoded as motor schemas, which specify the positions and velocities of a limb at selected points in time. Moreover, there exist two natural representations for such knowledge; viewer-centered schemas describe visually perceived behavior, arid joint-centered schemas are used to generate behavior. When the model acts upon these two representational formats, they exhibit quite different behavioral characteristics. MAGGIE performs the desired movement within a feedback control paradigm, monitoring for errors and correcting them when it detects them. Learning involves improving the joint-centered schema over many practice trials; this reduces the need for monitoring. The model accounts for a number of well-documented motor phenomena, including the speed-accuracy trade-off and the gradual improvement in performance with practice. It also makes several testable predictions. We close with a discussion of the theory's strengths and weaknesses, along with directions for future research.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1111/j.1467-8640.1987.tb00220.x
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