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Monocular guidance of reaches-to-grasp using visible support surface texture: data and model
Rachel A Herth1, Xiaoye Michael Wang1,2, Olivia Cherry1
1Department of Psychological and Brain Sciences, Indiana University, 1101 E 10th Street, Bloomington, IN, 47405, USA.
Experimental Brain Research
|January 3, 2021
Summary
This study shows that monocular vision can effectively guide reaches-to-grasp, similar to binocular vision, when visual cues from the environment are clear. A new dynamical control model replicates these findings.
Area of Science:
- Visual perception
- Motor control
- Robotics
Background:
- Humans use visual information for precise movements like reaching and grasping.
- Monocular vision's role in continuous reach-to-grasp guidance is less understood than binocular vision.
Purpose of the Study:
- To investigate the use of monocular visual information for online reach-to-grasp guidance.
- To develop and validate a dynamical control model for monocular reaches-to-grasp.
Main Methods:
- Participants performed reaches-to-grasp in darkness with monocular or binocular vision.
- Optical texture on a support surface provided visual information about target distance.
- Reach trajectories were analyzed for kinematic variables like Maximum Grasp Aperture (MGA) and Movement Time (MT).
Main Results:
- Monocular reaches were generally less certain (slower, earlier MGA/peak velocity) than binocular reaches.
- Performance was equivalent between monocular and binocular vision when the target was flat on a visible surface.
- A dynamical control model accurately simulated observed reach-to-grasp performance.
Conclusions:
- Monocular vision provides sufficient information for online reach-to-grasp guidance, especially with clear environmental cues.
- The developed dynamical control model effectively explains reach-to-grasp behavior using monocular information.
