Human cerebellar activity reflecting an acquired internal model of a new tool

Hiroshi Imamizu (), Satoru Miyauchi, Tomoe Tamada, Yuka Sasaki, Ryousuke Takino, Benno Pütz, Toshinori Yoshioka and Mitsuo Kawato
Additional contact information
Hiroshi Imamizu: JST/ERATO Kawato Dynamic Brain Project, 2-2 Hikaridai, Seika-cho, Soraku-gun
Satoru Miyauchi: Communications Research Laboratory, 588-2, Iwaoka, Nishi-ku, Kobe
Tomoe Tamada: JST/ERATO Kawato Dynamic Brain Project, 2-2 Hikaridai, Seika-cho, Soraku-gun
Yuka Sasaki: Communications Research Laboratory, 588-2, Iwaoka, Nishi-ku, Kobe
Ryousuke Takino: Shiraume Gakuen College, 1-830, Ogawa-cho, Kodaira-shi
Benno Pütz: JST/ERATO Kawato Dynamic Brain Project, 2-2 Hikaridai, Seika-cho, Soraku-gun
Toshinori Yoshioka: JST/ERATO Kawato Dynamic Brain Project, 2-2 Hikaridai, Seika-cho, Soraku-gun
Mitsuo Kawato: JST/ERATO Kawato Dynamic Brain Project, 2-2 Hikaridai, Seika-cho, Soraku-gun

Nature, 2000, vol. 403, issue 6766, 192-195

Abstract: Abstract Theories of motor control postulate that the brain uses internal models of the body to control movements accurately. Internal models are neural representations of how, for instance, the arm would respond to a neural command, given its current position and velocity1,2,3,4,5,6. Previous studies have shown that the cerebellar cortex can acquire internal models through motor learning7,8,9,10,11. Because the human cerebellum is involved in higher cognitive function12,13,14,15 as well as in motor control, we propose a coherent computational theory in which the phylogenetically newer part of the cerebellum similarly acquires internal models of objects in the external world. While human subjects learned to use a new tool (a computer mouse with a novel rotational transformation), cerebellar activity was measured by functional magnetic resonance imaging. As predicted by our theory, two types of activity were observed. One was spread over wide areas of the cerebellum and was precisely proportional to the error signal that guides the acquisition of internal models during learning. The other was confined to the area near the posterior superior fissure and remained even after learning, when the error levels had been equalized, thus probably reflecting an acquired internal model of the new tool.

Date: 2000
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DOI: 10.1038/35003194

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