Variables contributing to the coordination of rapid eye/head gaze shifts.
Variables contributing to the coordination of rapid eye/head gaze shifts.
复制标题
有助于协调眼睛/头部注视快速转移的变量。
DOI：
10.1007/s00422-006-0049-9
复制
发表时间：
2006
影响因子：
1.9
通讯作者：
McCollum,Gin
中科院分区：
文献类型：
--
作者：
Hanes,DouglasA;McCollum,Gin
在这篇文章中，几个已发表的研究结果进行了合成，以解决神经系统的眼睛和头部运动幅度的确定水平眼/头的视线转移与任意的初始头部和眼睛的位置。目标位置、初始头部位置和初始眼睛位置跨越用于计划的眼睛/头部凝视扫视的物理参数的空间。主要的结果是，一个功能性的机制，用于确定组件的眼睛和头部运动的幅度必须使用整个空间的变量。此外，它表明，振幅不能确定相加求和的贡献从单个变量。许多早期的模型计算振幅作为一个或两个变量的函数和/或限制考虑最佳拟合线性公式。我们的分析系统地排除了这样的模型，作为一个系统，可以产生适当的运动，为所有可能的初始条件的候选人。本研究的结果是在响应系统的属性。反应系统的内在组织的某些公理集服从这些性质。我们简要地提供了这样一个公理模型的一个例子。在这篇文章中提出的结果有助于表征实际的神经系统的快速眼/头凝视转移的控制，显示，为了占行为数据，某些物理量必须表示和使用的神经系统。我们的理论分析生成预测并确定数据中的差距。我们建议进行必要的实验。
In this article results of several published studies are synthesized in order to address the neural system for the determination of eye and head movement amplitudes of horizontal eye/head gaze shifts with arbitrary initial head and eye positions. Target position, initial head position, and initial eye position span the space of physical parameters for a planned eye/head gaze saccade. The principal result is that a functional mechanism for determining the amplitudes of the component eye and head movements must use the entire space of variables. Moreover, it is shown that amplitudes cannot be determined additively by summing contributions from single variables. Many earlier models calculate amplitudes as a function of one or two variables and/or restrict consideration to best-fit linear formulae. Our analysis systematically eliminates such models as candidates for a system that can generate appropriate movements for all possible initial conditions. The results of this study are stated in terms of properties of the response system. Certain axiom sets for the intrinsic organization of the response system obey these properties. We briefly provide one example of such an axiomatic model. The results presented in this article help to characterize the actual neural system for the control of rapid eye/head gaze shifts by showing that, in order to account for behavioral data, certain physical quantities must be represented in and used by the neural system. Our theoretical analysis generates predictions and identifies gaps in the data. We suggest needed experiments.