Egger Seth W, Keemink Sander W, Goldman Mark S, Britten Kenneth H
Center for Neuroscience, University of California, Davis.
Department of Neurobiology, Physiology and Behavior, University of California, Davis.
bioRxiv. 2024 Jul 29:2024.07.26.605325. doi: 10.1101/2024.07.26.605325.
In natural circumstances, sensory systems operate in a closed loop with motor output, whereby actions shape subsequent sensory experiences. A prime example of this is the sensorimotor processing required to align one's direction of travel, or heading, with one's goal, a behavior we refer to as steering. In steering, motor outputs work to eliminate errors between the direction of heading and the goal, modifying subsequent errors in the process. The closed-loop nature of the behavior makes it challenging to determine how deterministic and nondeterministic processes contribute to behavior. We overcome this by applying a nonparametric, linear kernel-based analysis to behavioral data of monkeys steering through a virtual environment in two experimental contexts. In a given context, the results were consistent with previous work that described the transformation as a second-order linear system. Classically, the parameters of such second-order models are associated with physical properties of the limb such as viscosity and stiffness that are commonly assumed to be approximately constant. By contrast, we found that the fit kernels differed strongly across tasks in these and other parameters, suggesting context-dependent changes in neural and biomechanical processes. We additionally fit residuals to a simple noise model and found that the form of the noise was highly conserved across both contexts and animals. Strikingly, the fitted noise also closely matched that found previously in a human steering task. Altogether, this work presents a kernel-based analysis that characterizes the context-dependence of deterministic and non-deterministic components of a closed-loop sensorimotor task.
在自然环境中，感觉系统与运动输出以闭环方式运作，即行为塑造后续的感觉体验。一个典型的例子是将行进方向或航向与目标对齐所需的感觉运动处理，我们将这种行为称为转向。在转向过程中，运动输出致力于消除航向与目标之间的误差，并在此过程中修正后续误差。这种行为的闭环性质使得确定确定性和非确定性过程如何对行为产生影响具有挑战性。我们通过对猴子在两种实验情境下在虚拟环境中转向的行为数据应用基于线性核的非参数分析来克服这一问题。在给定情境下，结果与之前将该转换描述为二阶线性系统的研究一致。传统上，此类二阶模型的参数与肢体的物理属性相关，如通常假定大致恒定的粘性和刚度。相比之下，我们发现这些参数以及其他参数在不同任务中的拟合核差异很大，这表明神经和生物力学过程存在情境依赖性变化。我们还将残差拟合到一个简单的噪声模型，发现噪声的形式在情境和动物之间都高度保守。引人注目的是，拟合出的噪声也与之前在人类转向任务中发现的噪声非常匹配。总之，这项工作提出了一种基于核的分析方法，该方法表征了闭环感觉运动任务中确定性和非确定性成分的情境依赖性。
