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This Article Full Text Full Text (PDF) All Versions of this Article: 99/5/2577    most recent 00015.2008v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Ethier, V. Articles by Shadmehr, R. PubMed PubMed Citation Articles by Ethier, V. Articles by Shadmehr, R.

Spontaneous Recovery of Motor Memory During Saccade Adaptation

¹Department of Biomedical Engineering and ²Department of Neurology and Neuroscience, The Johns Hopkins School of Medicine, Baltimore, Maryland

Submitted 6 January 2008; accepted in final form 16 March 2008

It is possible that motor adaptation in timescales of minutes is supported by two distinct processes: one process that learns slowly from error but has strong retention, and another that learns rapidly from error but has poor retention. This two-state model makes the prediction that if a period of adaptation is followed by a period of reverse-adaptation, then in the subsequent period in which errors are clamped to zero (error-clamp trials) there will be a spontaneous recovery, i.e., a rebound of behavior toward the initial level of adaptation. Here we tested and confirmed this prediction during double-step, on-axis, saccade adaptation. When people adapted their saccadic gain to a magnitude other than one (adaptation) and then the gain was rapidly reversed back to one (reverse-adaptation), in the subsequent error-clamp trials (visual target placed on the fovea after the saccade) the gain reverted toward the initially adapted value and then gradually reverted toward normal. We estimated that the fast system was about 20-fold more sensitive to error than the slow system, but had a time constant of 28 s, whereas the slow system had a time constant of nearly 8 min. Therefore short-term adaptive mechanisms that maintain accuracy of saccades rely on a memory system that has characteristics of a multistate process with a logarithmic distribution of timescales.