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pubmed:abstractText |
Can memories be unlearned, or is unlearning a form of acquiring a new memory that competes with the old, effectively masking it? We considered motor memories that were acquired when people learned to use a novel tool. We trained people to reach with tool A and quantified recall in error-clamp trials, i.e., trials in which the memory was reactivated but error-dependent learning was minimized. We measured both the magnitude of the memory and its resistance to change. With passage of time between acquisition and reactivation (up to 24 h), memory of A slowly declined, but with reactivation remained resistant to change. After learning of tool A, brief exposure to tool B brought performance back to baseline, i.e., apparent extinction. Yet, for up to a few minutes after A+B training, output in error-clamp trials increased from baseline to match those who had trained only in A. This spontaneous recovery and convergence demonstrated that B did not produce any unlearning of A. Rather, it masked A with a new memory that was very fragile. We tracked the memory of B as a function of time and found that within minutes it was transformed from a fragile to a more stable state. Therefore, a sudden performance error in a well-learned motor task does not produce unlearning, but rather installs a competing but fragile memory that with passage of time acquires stability. Learning not only engages processes that adapt at multiple timescales, but once practice ends, the fast states are partially transformed into slower states.
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pubmed:affiliation |
Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, USA. sarah.hemminger@jhu.edu
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