Nathaniel Goldstein¹ (), Laurie M. Wilcox¹, Erez Freud¹; ¹York University, Toronto, Canada

We typically rely on binocular vision to understand the spatial layout and physical properties of objects in the visual world. This information is essential both for perception and action-based behaviours and there is evidence that when adults have access to binocular depth cues, they make faster and more accurate grasps towards objects. However, most studies examining this topic have used tasks in which participants reach for objects at a distance and place them closer towards them. Conversely, real-world movements occur in different directions and distances relative to the observer. Here we evaluate the contribution of binocular depth information (e.g., disparity, vergence) to a range of grasp characteristics for bidirectional movements (i.e., towards vs. away from the body). In a within-subjects design, participants grasped 3D discs of varying sizes (3.5 - 5.5 cm diameter) at two distances (18 and 36 cm from observer). Viewing was binocular or monocular with the non-dominant eye patched. On each trial, participants grasped an object and placed it on a peg positioned closer or further away from their body as quickly and accurately as possible. Our results show that binocular depth information improves both components of the task (grasping the object and positioning the object at the new location). In particular, under the binocular condition the velocity of the grasping movement was higher and the time to reposition the objects shorter. These effects were consistent for both movement directions. We observed minimal interactions between depth, distance, and size, suggesting mostly independent effects of these variables on visuomotor behaviour. Additional analyses of the grasp trajectories, including machine learning approaches, will provide insight into the contribution of 3D cues to multi-dimensional visually guided movements.

Acknowledgements: Natural Sciences and Engineering Research Council of Canada (NSERC); CFREF Vision: Science to Applications (VISTA)