Calibration of visually guided reaching is driven by error-corrective learning and internal dynamics
Calibration of visually guided reaching is driven by error-corrective learning and internal dynamics
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DOI：
10.1152/jn.00897.2006
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发表时间：
2007-04-01
影响因子：
2.5
通讯作者：
Sabes, Philip N.
中科院分区：
文献类型：
--
作者：
Cheng, Sen;Sabes, Philip N.
视觉引导到达的感觉运动校准在试验到试验的基础上变化，以响应手的视觉反馈中的随机移位。我们表明，一个简单的线性动力系统是足够的模型动态的自适应过程。在这个模型中，一个内部变量代表感觉运动校准的当前状态。这种状态的变化是由误差反馈信号驱动的，误差反馈信号包括视觉感知的到达误差、视觉反馈中的人工移位或两者。受试者纠正>= 20%的错误观察到的每一个动作，尽管没有意识到的视觉转变。适应状态也是由内部动态驱动的，包括衰减回基线状态和“状态噪声”过程。状态噪声包括任何直接影响适应状态的可变性来源，例如感觉反馈处理的可变性，驱动学习的计算或状态的维持。这种噪声在试验中累积在状态中，在到达误差序列中产生时间相关性。这些相关性使我们能够区分状态噪声和感觉运动性能噪声，后者在每次试验中独立于感觉运动通路中的随机波动而产生。我们发现，这两个噪声源的整体幅度的运动变异性的贡献。最后，随机反馈位移测量的适应动力学推广到恒定反馈位移的情况下，允许我们的结果与更传统的阻断曝光实验的直接比较。
The sensorimotor calibration of visually guided reaching changes on a trial-to-trial basis in response to random shifts in the visual feedback of the hand. We show that a simple linear dynamical system is sufficient to model the dynamics of this adaptive process. In this model, an internal variable represents the current state of sensorimotor calibration. Changes in this state are driven by error feedback signals, which consist of the visually perceived reach error, the artificial shift in visual feedback, or both. Subjects correct for >= 20% of the error observed on each movement, despite being unaware of the visual shift. The state of adaptation is also driven by internal dynamics, consisting of a decay back to a baseline state and a "state noise" process. State noise includes any source of variability that directly affects the state of adaptation, such as variability in sensory feedback processing, the computations that drive learning, or the maintenance of the state. This noise is accumulated in the state across trials, creating temporal correlations in the sequence of reach errors. These correlations allow us to distinguish state noise from sensorimotor performance noise, which arises independently on each trial from random fluctuations in the sensorimotor pathway. We show that these two noise sources contribute comparably to the overall magnitude of movement variability. Finally, the dynamics of adaptation measured with random feedback shifts generalizes to the case of constant feedback shifts, allowing for a direct comparison of our results with more traditional blocked-exposure experiments.