Laboratory for Computational Motor Control, Dept. of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America.
PLoS Comput Biol. 2021 Jul 6;17(7):e1009176. doi: 10.1371/journal.pcbi.1009176. eCollection 2021 Jul.
As you read this text, your eyes make saccades that guide your fovea from one word to the next. Accuracy of these movements require the brain to monitor and learn from visual errors. A current model suggests that learning is supported by two different adaptive processes, one fast (high error sensitivity, low retention), and the other slow (low error sensitivity, high retention). Here, we searched for signatures of these hypothesized processes and found that following experience of a visual error, there was an adaptive change in the motor commands of the subsequent saccade. Surprisingly, this adaptation was not uniformly expressed throughout the movement. Rather, after experience of a single error, the adaptive response in the subsequent trial was limited to the deceleration period. After repeated exposure to the same error, the acceleration period commands also adapted, and exhibited resistance to forgetting during set-breaks. In contrast, the deceleration period commands adapted more rapidly, but suffered from poor retention during these same breaks. State-space models suggested that acceleration and deceleration periods were supported by a shared adaptive state which re-aimed the saccade, as well as two separate processes which resembled a two-state model: one that learned slowly and contributed primarily via acceleration period commands, and another that learned rapidly but contributed primarily via deceleration period commands.
当你阅读这段文字时，你的眼睛会进行扫视，将你的中央凹从一个单词引导到下一个单词。这些运动的准确性要求大脑进行监测并从视觉错误中学习。目前的模型表明，学习由两种不同的适应过程支持，一种快速（高错误敏感性，低保留），另一种缓慢（低错误敏感性，高保留）。在这里，我们搜索了这些假设过程的特征，并发现，在经历了一次视觉错误后，随后的扫视的运动指令会发生适应性变化。令人惊讶的是，这种适应并不是在整个运动中均匀表达的。相反，在经历了一次错误后，后续试验中的适应性反应仅限于减速阶段。在重复暴露于相同的错误后，加速阶段的命令也会适应，并且在设置中断期间表现出对遗忘的抵抗力。相比之下，减速阶段的命令适应得更快，但在这些相同的休息期间保留效果较差。状态空间模型表明，加速和减速阶段由一个共享的自适应状态支持，该状态重新瞄准扫视，以及两个类似于双状态模型的单独过程：一个缓慢学习，主要通过加速阶段的命令贡献，另一个快速学习，但主要通过减速阶段的命令贡献。
