We investigated monocular information for the continuous online guidance of reaches-to-grasp and present a dynamical control model thereof. We defined an information variable using optical texture projected from a support surface (i.e. a table) over which the participants reached-to-grasp target objects sitting on the table surface at different distances. Using either binocular or monocular vision in the dark, participants rapidly reached-to-grasp a phosphorescent square target object with visibly phosphorescent thumb and index finger. Targets were one of three sizes. The target either sat flat on the support surface or was suspended a few centimeters above the surface at a slant. The later condition perturbed the visible relation of the target to the support surface. The support surface was either invisible in the dark or covered with a visible phosphorescent checkerboard texture. Reach-to-grasp trajectories were recorded and Maximum Grasp Apertures (MGA), Movement Times (MT), Time of MGA (TMGA), and Time of Peak Velocities (TPV) were analyzed. These measures were selected as most indicative of the participant’s certainty about the relation of hand to target object during the reaches. The findings were that, in general, especially monocular reaches were less certain (slower, earlier TMGA and TPV) than binocular reaches except with the target flat on the visible support surface where performance with monocular and binocular vision was equivalent. The hypothesized information was the difference in image width of optical texture (equivalent to density of optical texture) at the hand versus the target. A control dynamic equation was formulated representing proportional rate control of the reaches-to-grasp (akin to the model using binocular disparity formulated by Anderson and Bingham (Exp Brain Res 205: 291–306, 2010). Simulations were performed and presented using this model. Simulated performance was compared to actual performance and found to replicate it. To our knowledge, this is the first study of monocular information used for continuous online guidance of reaches-to-grasp, complete with a control dynamic model.

我们调查了单眼信息以获取持续不断的在线指导，并提出了一种动态控制模型。我们使用从支撑表面（即桌子）投射的光学纹理定义了一个信息变量，参与者可以通过该纹理到达位于桌子表面上不同距离的抓握目标对象。在黑暗中使用双目或单眼视觉，参与者都可以通过可见的磷光拇指和食指迅速抓到一个磷光方形目标物体。目标是三个大小之一。目标要么平放在支撑表面上，要么斜着悬在表面上方几厘米处。后面的条件扰动了目标与支撑表面的可见关系。支撑表面在黑暗中不可见或被可见的磷光棋盘纹理覆盖。记录到达抓取的轨迹，并分析最大抓取孔径（MGA），移动时间（MT），MGA时间（TMGA）和峰值速度时间（TPV）。选择这些措施是最能说明参与者在伸手过程中手与目标物体之间关系的确定性。研究发现，通常情况下，单眼范围比双眼范围不确定性（慢，TMGA和TPV更低），除了目标平放在可见支撑表面上（单眼和双眼视觉性能相当）之外。假设的信息是手与目标的光学纹理图像宽度（相当于光学纹理的密度）之差。将模拟性能与实际性能进行比较，发现它们可以复制。据我们所知，这是单眼信息的首次研究，该信息用于连续在线指导到达抓握，并带有控制动态模型。将模拟性能与实际性能进行比较，发现它们可以复制。据我们所知，这是单眼信息的首次研究，该信息用于连续在线指导到达抓握，并带有控制动态模型。