Abstract
Subjects lack perfect knowledge about the world, so to understand behavior, it is necessary to take subjects' uncertainty into account. Sprague et al (2007) suggested that gaze is directed to locations in order to resolve uncertainty affecting task performance, with uncertainty about unattended parts of the scene increasing over time. This uncertainty can be due to intrinsic factors (like motor noise and imperfect memory) and extrinsic factors (a dynamic world state). We evaluated the consequences of intrinsic and extrinsic uncertainty, revealed in the behavioral choices subjects made while walking through a cluttered scene. Subjects followed a path in a virtual environment while intercepting targets and avoiding obstacles. Extrinsic uncertainty was manipulated by adding random motion to targets and obstacles. We examined gaze and walking behavior of 12 subjects while they avoided obstacles. We plotted a subject's path from the time that fixation on an obstacle ended, and measured the minimal distance at which the subject subsequently passed the obstacle. When obstacles are stationary subjects give wider clearance when fixation had ended at a greater distance. Modal clearance was 0.42m for fixations ending with obstacles about 0.4m away, increasing to 0.55m for fixations that ended when the subject was further than 2.25m. This suggests that subjects take into account their intrinsic uncertainty, which accumulates over the greater distance walked after fixation ended. When the obstacles were moving, the distance of the last fixation had a greater effect (increasing to 0.64m clearance at large distance), suggesting that the extrinsic uncertainty about scene state is also taken into account. Together, this suggests that both kinds of uncertainty are taken into account when planning path trajectories (consistent with the decision-theoretic account described by Wolpert and Landy, 2012), and also that the position of the obstacle is only imperfectly updated when it is not fixated.
Meeting abstract presented at VSS 2016