Updating target distance across eye movements in depth Running Title: Depth coding for reaching movements

We tested between two coding mechanisms that the brain may use to retain distance information about a target for a reaching movement across vergence eye movements. If the brain were to encode a retinal disparity representation (retinal model), i.e., target depth relative to the plane of fixation, each vergence eye movement would require an active update of this representation to preserve depth constancy. Alternatively, if the brain were to store an egocentric distance representation of the target by integrating retinal disparity and vergence signals at the moment of target presentation, this representation should remain stable across subsequent vergence shifts (nonretinal model). We tested between these schemes by measuring errors of human reaching movements (n=14 subjects) to remembered targets, briefly presented before a vergence eye movement. For comparison, we also tested their directional accuracy across version eye movements. With intervening vergence shifts, the memory-guided reaches showed an error pattern that was based on the new eye position and on the depth of the remembered target relative to that position. This suggests that target depth is recomputed after the gaze shift, supporting the retinal model. Our results also confirm earlier literature showing retinal updating of target direction. Furthermore, regression analyses revealed updating gains close to one for both target depth and direction, suggesting that the errors arise after the updating stage, during the subsequent reference frame transformations that are involved in reaching.