Open AccessVisual anticipation of the future path: Predictive gaze and steering
Author(s) -
Samuel Tuhkanen,
Jami Pekkanen,
Richard M. Wilkie,
Otto Lappi
Publication year - 2021
Publication title -
journal of vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.126
H-Index - 113
ISSN - 1534-7362
DOI - 10.1167/jov.21.8.25
Subject(s) - waypoint , anticipation (artificial intelligence) , gaze , computer science , trajectory , path (computing) , simulation , movement (music) , computer vision , artificial intelligence , real time computing , physics , programming language , astronomy , acoustics
Skillful behavior requires the anticipation of future action requirements. This is particularly true during high-speed locomotor steering where solely detecting and correcting current error is insufficient to produce smooth and accurate trajectories. Anticipating future steering requirements could be supported using “model-free” prospective signals from the scene ahead or might rely instead on model-based predictive control solutions. The present study generated conditions whereby the future steering trajectory was specified using a breadcrumb trail of waypoints, placed at regular intervals on the ground to create a predictable course (a repeated series of identical “S-bends”). The steering trajectories and gaze behavior relative to each waypoint were recorded for each participant ( N = 16). To investigate the extent to which drivers predicted the location of future waypoints, “gaps” were included (20% of waypoints) whereby the next waypoint in the sequence did not appear. Gap location was varied relative to the S-bend inflection point to manipulate the chances that the next waypoint indicated a change in direction of the bend. Gaze patterns did indeed change according to gap location, suggesting that participants were sensitive to the underlying structure of the course and were predicting the future waypoint locations. The results demonstrate that gaze and steering both rely upon anticipation of the future path consistent with some form of internal model.