Monocular guidance of reaches-to-grasp using visible support surface texture: data and model

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.