A model of temporal scaling correctly predicts that motor timing improves with speed

Humans can perform complex motor movements at varying speeds. Here, the authors show that a recurrent neural network can be trained to exhibit temporal scaling obeying Weber’s law as well as validate a prediction of the model of improved precision of movements at faster speeds.

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Autores principales: Nicholas F. Hardy, Vishwa Goudar, Juan L. Romero-Sosa, Dean V. Buonomano
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2018
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Acceso en línea:https://doaj.org/article/1735b53621ec48ce80e9bab83c18ff20
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