Abstract | ||
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When humans perform closed loop control tasks like in upright standing or while balancing a stick, their behavior exhibits non-Gaussian fluctuations with long-tailed distributions. The origin of these fluctuations is not known. Here, we investigate if they are caused by self-organized critical noise amplification which emerges in control systems when an unstable dynamics becomes stabilized by an adaptive controller that has finite memory. Starting from this theory, we formulate a realistic model of adaptive closed loop control by including constraints on memory and delays. To test this model, we performed psychophysical experiments where humans balanced an unstable target on a screen. It turned out that the model reproduces the long tails of the distributions together with other characteristic features of the human control dynamics. Fine-tuning the model to match the experimental dynamics identifies parameters characterizing a subject's control system which can be independently tested. Our results suggest that the nervous system involved in closed loop motor control nearly optimally estimates system parameters on-line from very short epochs of past observations. |
Year | DOI | Venue |
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2007 | 10.3389/neuro.10/004.2007 | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE |
Keywords | DocType | Volume |
self-organized criticality,power law,fluctuations,non-gaussianity,multiplicative noise,sensory-motor system,learning,adaptation | Journal | 1 |
ISSN | Citations | PageRank |
1662-5188 | 2 | 0.43 |
References | Authors | |
0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Felix Patzelt | 1 | 2 | 0.43 |
Markus Riegel | 2 | 2 | 0.43 |
Udo Ernst | 3 | 13 | 3.99 |
Klaus Pawelzik | 4 | 509 | 107.71 |