Using sensory weighting to model the influence of canal, otolith and visual cues on spatial orientation and eye movements
Using sensory weighting to model the influence of canal, otolith and visual cues on spatial orientation and eye movements
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DOI：
10.1007/s00422-001-0290-1
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发表时间：
2002-03-01
影响因子：
1.9
通讯作者：
Darlot, C
中科院分区：
文献类型：
--
作者：
Zupan, LH;Merfeld, DM;Darlot, C
感觉加权模型是由三个加工层组成的感觉整合的一般模型。首先，每个传感器向中枢神经系统(CNS)提供关于特定物理变量的信息。由于传感器的动态特性，该测量仅在传感器准确的频率范围内可靠。因此，我们假设CNS通过使用来自其他传感器的信息来提高该频率范围之外的单个传感器的可靠性，这一过程被称为“频率补全”。频率补全使用感觉动力学的内部模型。这种“改进的”感觉信号被指定为对物理变量的“感觉估计”。其次，在组合之前，具有不同物理意义的信息首先被转换为共同的表征；感官估计被转换为中间估计。此转换使用身体动力学和物理关系的内部模型。第三，几个感觉系统可能提供关于相同物理变量的信息(例如，半规管和视觉都测量自我旋转)。因此，我们假设一个物理变量的“中心估计”被计算为该物理变量的所有可用中间估计的加权和，这个过程被称为“多重加权平均”。由此产生的中央估计被反馈到前两层。感官加权模型被应用于三维(3D)视觉-前庭交互及其相关的眼睛运动和知觉反应。模型的输入是3D角度和平移刺激。感觉输入是来自半规管、耳石器官和视觉系统的3D感觉信号。视觉运动的角度和平移分量被认为是视觉系统使用视网膜滑动和图像变形测量的单独刺激。此外，还实现了强的(规则的)和相的(不规则的)耳石器传入。虽然强音和相位耳石器传入都不能区分重力和线加速度，但该模型使用强传入来估计重力，使用相传入来估计线加速度。模型的输出是对物理运动变量和3D慢相眼球运动的内部估计。该模型还包括平滑追逐模块。该模型将在黑暗中的各种运动范例(例如，围绕地球垂直轴的中心和偏心偏航旋转、围绕地球-水平轴的偏航旋转)期间测量的眼睛反应和感知效果与视觉提示(例如，稳定的视觉刺激或视动刺激)进行匹配。
The sensory weighting model is a general model of sensory integration that consists of three processing layers. First, each sensor provides the central nervous system (CNS) with information regarding a specific physical variable. Due to sensor dynamics, this measure is only reliable for the frequency range over which the sensor is accurate. Therefore, we hypothesize that the CNS improves on the reliability of the individual sensor outside this frequency range by using information from other sensors, a process referred to as "frequency completion." Frequency completion uses internal models of sensory dynamics. This "improved" sensory signal is designated as the "sensory estimate" of the physical variable. Second, before being combined, information with different physical meanings is first transformed into a common representation; sensory estimates are converted to intermediate estimates. This conversion uses internal models of body dynamics and physical relationships. Third, several sensory systems may provide information about the same physical variable (e.g., semicircular canals and vision both measure self-rotation). Therefore, we hypothesize that the "central estimate" of a physical variable is computed as a weighted sum of all available intermediate estimates of this physical variable, a process referred to as "multicue weighted averaging." The resulting central estimate is fed back to the first two layers. The sensory weighting model is applied to three-dimensional (3D) visual-vestibular interactions and their associated eye movements and perceptual responses. The model inputs are 3D angular and translational stimuli. The sensory inputs are the 3D sensory signals coming from the semicircular canals, otolith organs, and the visual system. The angular and translational components of visual movement are assumed to be available as separate stimuli measured by the visual system using retinal slip and image deformation. In addition, both tonic ("regular") and phasic ("irregular") otolithic afferents are implemented. Whereas neither tonic nor phasic otolithic afferents distinguish gravity from linear acceleration, the model uses tonic afferents to estimate gravity and phasic afferents to estimate linear acceleration. The model outputs are the internal estimates of physical motion variables and 3D slow-phase eye movements. The model also includes a smooth pursuit module. The model matches eye responses and perceptual effects measured during various motion paradigms in darkness (e.g., centered and eccentric yaw rotation about an earthvertical axis, yaw rotation about an earth-horizontal axis) and with visual cues (e.g., stabilized visual stimulation or optokinetic stimulation).