Abstract
A complex array of competing task demands, such as locating a landmark, avoiding obstacles, and regulating foot placement on challenging terrain, necessitate appropriate spatial-temporal gaze behavior and proper gaze-foot coordination when walking. Because of our imperfect knowledge of the world, many environmental features critical to this action are uncertain. For example, we may encounter terrain in which we do not know its characteristics. Here, we determined whether gaze is sensitive to uncertainty to control foot placement. In this experiment, participants (n = 8) walked and stepped on three irregularly spaced targets projected on the floor. To create three different levels of environmental (i.e., target) uncertainty (no, low, and high), we manipulated the standard deviation of two-dimensional Gaussian luminance blobs. We instructed participants to either step onto the centre of these blob targets (High-Accuracy task) or anywhere onto the targets (Low-Accuracy task) in different blocks of trials. We used a motion capture system to track foot placement and a high-speed, head-mounted mobile eye tracker to measure gaze. Our data show that participants spent more time fixating the targets in the High-Accuracy task. More importantly, total fixation time on the targets increased as their uncertainty increased in the High-Accuracy, but not Low-Accuracy task. We also found increased foot-placement error in the Low-Accuracy task overall, and with greater target uncertainty. In contrast, we found similar error among the three uncertainty conditions in the High-Accuracy task. Taken together, our results suggest that people choose to prolong fixation time on specific ground locations to control foot placement while walking. In line with past research (e.g., Gottlieb et al. 2014), we further suggest that this adaptive gaze strategy is due to an intrinsic motivation to reduce uncertainty and increase the expected reward of a future action; in this case, to ensure safe, accurate foot placement.
Meeting abstract presented at VSS 2017