4.4 Article

Three timescales in prism adaptation

Journal

JOURNAL OF NEUROPHYSIOLOGY
Volume 113, Issue 1, Pages 328-338

Publisher

AMER PHYSIOLOGICAL SOC
DOI: 10.1152/jn.00803.2013

Keywords

prism adaptation; reaching; motor learning; two-state model; three-state model

Funding

  1. Human Frontiers Science Program Organization
  2. Royal Society (United Kingdom)
  3. [25119002]
  4. Grants-in-Aid for Scientific Research [25119002] Funding Source: KAKEN

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It has been proposed that motor adaptation depends on at least two learning systems, one that learns fast but with poor retention and another that learns slowly but with better retention (Smith MA, Ghazizadeh A, Shadmehr R. PLoS Biol 4: e179, 2006). This two-state model has been shown to account for a range of behavior in the force field adaptation task. In the present study, we examined whether such a two-state model could also account for behavior arising from adaptation to a prismatic displacement of the visual field. We first confirmed that an adaptation rebound, a critical prediction of the two-state model, occurred when visual feedback was deprived after an adaptation-extinction episode. We then examined the speed of decay of the prism aftereffect (without any visual feedback) after repetitions of 30, 150, and 500 trials of prism exposure. The speed of decay decreased with the number of exposure trials, a phenomenon that was best explained by assuming an ultraslow system, in addition to the fast and slow systems. Finally, we compared retention of aftereffects 24 h after 150 or 500 trials of exposure: retention was significantly greater after 500 than 150 trials. This difference in retention could not be explained by the two-state model but was well explained by the three-state model as arising from the difference in the amount of adaptation of the ultraslow process. These results suggest that there are not only fast and slow systems but also an ultraslow learning system in prism adaptation that is activated by prolonged prism exposure of 150-500 trials.

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