June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Beliefs about object motion in the world affect depth perception during self-motion
Author Affiliations & Notes
  • Ranran L French
    University of Rochester Medical Center, Rochester, New York, United States
  • Gregory C DeAngelis
    Brain and Cognitive Sciences, University of Rochester School of Arts and Sciences, Rochester, New York, United States
  • Footnotes
    Commercial Relationships   Ranran French None; Gregory DeAngelis None
  • Footnotes
    Support  NEI F30 Grant EY031183
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 2549 – F0503. doi:
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      Ranran L French, Gregory C DeAngelis; Beliefs about object motion in the world affect depth perception during self-motion. Invest. Ophthalmol. Vis. Sci. 2022;63(7):2549 – F0503.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : It is crucial for animals to accurately judge the depth of moving objects. During observer translation, the relative image motion between stationary objects at different distances, known as motion parallax (MP), provides important depth information. However, when an object also moves relative to the scene, the computation of depth from MP is complicated by the object’s independent motion. Previously, we demonstrated that humans show systematic depth biases during self-motion that depend on object motion in the world. Here, we propose and compare two distinct perceptual mechanisms by which object motion may induce depth biases.

Methods : Naïve human subjects viewed a virtual 3D scene consisting of a ground plane and stationary background objects, while lateral self-motion was simulated by optic flow. A target object, lying above the ground plane, could be either stationary or moving laterally at different velocities. In a dual report task, the subjects were asked to judge the depth of the target object relative to the plane of fixation, as well as whether they thought the object was moving independently relative to the scene.

Results : We constructed Bayesian ideal observer models for two proposed perceptual mechanisms: (incomplete) flow parsing (FP) and causal inference (CI). In the FP model, some fraction of image motion resulting from object motion can be attributed to self-motion, but the model assumes that a moving object is always present. In the CI model, the ideal observer can probabilistically attribute image motion to two separate causes: scene-relative object motion and self-motion. Both models predict systematic depth biases that depend on object motion relative to the scene, but only the CI model captures biases that depend on subjects’ belief about object motion. By the Akaike information criterion, the CI model provides a consistently better fit than the FP model across 6 subjects.

Conclusions : We demonstrate that both incomplete flow parsing and causal inference are feasible explanations for the substantial depth biases that are induced by scene-relative object motion. However, the component of depth bias that depends on a subject’s belief about object motion is consistent with involvement of a causal inference process that adjudicates whether image motion is caused by a combination of object motion and self-motion.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

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