Smooth pursuit eye movements contribute to anticipatory force control during mechanical stopping of moving objects

Abstract

When stopping a closing door or catching an object, humans process the motion of inertial objects and apply reactive limb force over short period to interact with them. One way in which the visual system processes motion is through extraretinal signals associated with smooth pursuit eye movements (SPEM). We conducted three experiments to investigate how SPEM contributes to anticipatory and reactive hand force modulation when interacting with a virtual object moving in the horizontal plane. Participants held a robotic manipulandum and attempted to stop an approaching simulated object by applying a force impulse (area under force-time curve) that matched the objects virtual momentum upon contact. We manipulated the objects momentum by varying either its virtual mass or its speed under free gaze or fixed gaze conditions. We examined gaze variables, timing of hand motor responses, anticipatory force control, and overall task performance. Our results show that extraretinal signals associated with SPEM are important for anticipatory modulation of hand force prior to contact. However, SPEM did not seem to affect the timing of the motor response or the task performance. Together, these results suggest that SPEM are important for anticipatory control of hand force prior to contact and may also play a critical role in anticipatory stabilization of limb posture when humans interact with moving objects.

Predictive posture stabilization before contact with moving objects: equivalence of smooth pursuit tracking and peripheral vision

“…This misperception, termed the Aubert-Fleischl phenomenon, leads to an overestimation of the target's velocity, causing interception movements to be inaccurately aimed ahead of the target (10). Yet, our recent study (4) reveals that subjects instructed to stop a virtually moving object by applying a force pulse will exert less force with a fixed gaze, suggesting an underestimation rather than an overestimation of the object's velocity. Our findings challenge the idea that people overestimate motion when their gaze is fixed and indicate that factors other than motion overestimation might explain why individuals tend to overshoot the target during interception tasks yet decrease the magnitude of their APAs when interacting with moving objects.…”

“…Hand kinematics and kinetics were recorded at 1,000 Hz. The virtual moving stimuli was generated using a Gabor-patch generator (https://www.cogsci.nl/gaborgenerator), with properties identical to those in our previous studies (3,4).…”

“…We implemented Bayesian model averaging for linear regression to compare our predictors (38,39). This approach allowed us to evaluate 2 4 (i.e., 16) models across four predictors by computing all possible parameter combinations. Initially, we set the cumulative likelihood for all models to zero.…”

“…BMA circumvents the binary approach of classical hypothesis testing, where models are either entirely accepted or rejected, and is resilient against errors in model specification. The output of the analysis is a probability value that shows how likely is this variable to be included into the 2 4 (16) combinations of models. A value of 0.5 would suggest that at least 8/16 models should include the predictor variable.…”

“…In previous motor control studies, objects moved across the viewer's field of vision either in the left (11) or the right visual field (10) with respect to the fixed gaze location. In our study (4), we had objects moving downwards in the upper part of the visual field. Additionally, the uneven distribution of cones and rods on the retina influences spatial resolution across the visual field, with a more pronounced decline in vertical than horizontal directions.…”