Consolidation patterns of human motor memory
Consolidation patterns of human motor memory
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DOI：
10.1523/jneurosci.3071-08.2008
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发表时间：
2008-09-24
影响因子：
5.3
通讯作者：
Shadmehr, Reza
中科院分区：
文献类型：
--
作者：
Criscimagna-Hemminger, Sarah E.;Shadmehr, Reza
记忆是可以被遗忘的，还是遗忘是一种获取新记忆的形式，与旧记忆竞争，有效地掩盖了旧记忆？我们考虑了人们在学习使用新工具时获得的运动记忆。我们训练人们在错误夹紧试验中使用工具A和量化回忆，即记忆被重新激活但错误依赖学习被最小化的试验。我们测量了记忆的大小和它对变化的抵抗力。从习得到再激活的时间间隔长达24小时，A的记忆缓慢下降，但再激活后仍保持不变。在学习了工具A之后，短暂地接触工具B使性能恢复到基线，即明显消失。然而，在a + B训练后的几分钟内，错误钳试验的输出从基线增加到与只接受a训练的人相当。这种自发的恢复和收敛表明，B没有产生对a的任何遗忘，相反，它用一种非常脆弱的新记忆掩盖了a。我们追踪了B的记忆作为时间的函数，发现在几分钟内它就从一个脆弱的状态变成了一个更稳定的状态。因此，在学习良好的运动任务中，突然出现的表现错误不会产生遗忘，而是会建立一种竞争但脆弱的记忆，随着时间的推移，这种记忆会变得稳定。学习不仅涉及适应多个时间尺度的过程，而且一旦练习结束，快速状态部分转化为较慢状态。
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.