Modeling sensorimotor learning with linear dynamical systems
Modeling sensorimotor learning with linear dynamical systems
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DOI：
10.1162/089976606775774651
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发表时间：
2006-04-01
影响因子：
2.9
通讯作者：
Sabes, PN
中科院分区：
文献类型：
--
作者：
Cheng, S;Sabes, PN
最近的研究采用简单的线性动力系统来模拟各种感觉运动学习任务中的逐次试验动力学。在这里，我们探讨了使用一般类别的线性动力系统（LDS）作为感觉运动学习模型时出现的理论和实践考虑。在这个框架中，系统的状态是一组定义当前感觉运动转换的参数——将感觉输入映射到运动输出的函数。 LDS 模型类为任何马尔可夫（状态相关）学习规则提供一阶近似，该规则指定由每次运动的感觉反馈导致的感觉运动转换的变化。我们表明，与从更传统的阻塞暴露实验中得出的适应稳态测量相比，对学习的逐个尝试动态进行建模提供了适应性过程的显着增强的图像。具体来说，这些模型可用于量化感觉和表现偏差、感觉运动转换的学习变化随时间衰减的程度，以及由于学习或表现变异性导致的运动变异性部分。我们表明，之前用线性回归拟合此类模型的尝试通常并未产生一致的参数估计。相反，我们提出了一种将 LDS 模型拟合到实验数据的期望最大化算法，并描述了估计与反馈驱动学习相关的参数所固有的困难。最后，我们演示了这些方法在一个简单的感觉运动学习实验中的应用：适应伸手过程中视觉反馈的变化。
Recent studies have employed simple linear dynamical systems to model trial-by-trial dynamics in various sensorimotor learning tasks. Here we explore the theoretical and practical considerations that arise when employing the general class of linear dynamical systems (LDS) as a model for sensorimotor learning. In this framework, the state of the system is a set of parameters that define the current sensorimotor transformation-the function that maps sensory inputs to motor outputs. The class of LDS models provides a first-order approximation for any Markovian (state-dependent) learning rule that specifies the changes in the sensorimotor transformation that result from sensory feedback on each movement. We show that modeling the trial-by-trial dynamics of learning provides a substantially enhanced picture of the process of adaptation compared to measurements of the steady state of adaptation derived from more traditional blocked-exposure experiments. Specifically, these models can be used to quantify sensory and performance biases, the extent to which learned changes in the sensorimotor transformation decay over time, and the portion of motor variability due to either learning or performance variability. We show that previous attempts to fit such models with linear regression have not generally yielded consistent parameter estimates. Instead, we present an expectation-maximization algorithm for fitting LDS models to experimental data and describe the difficulties inherent in estimating the parameters associated with feedback-driven learning. Finally, we demonstrate the application of these methods in a simple sensorimotor learning experiment: adaptation to shifted visual feedback during reaching.