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Binocular rivalry under naturalistic geometry: Evidence from worlds simulated in virtual reality.

Shui'Er HanRandolph BlakeCeline AubuchonDuje Tadin
Published in: PNAS nexus (2024)
Binocular rivalry is a fascinating, widely studied visual phenomenon in which perception alternates between two competing images. This experience, however, is generally restricted to laboratory settings where two irreconcilable images are presented separately to the two eyes, an implausible geometry where two objects occupy the same physical location. Such laboratory experiences are in stark contrast to everyday visual behavior, where rivalry is almost never encountered, casting doubt on whether rivalry is relevant to our understanding of everyday binocular vision. To investigate the external validity of binocular rivalry, we manipulated the geometric plausibility of rival images using a naturalistic, cue-rich, 3D-corridor model created in virtual reality. Rival stimuli were presented in geometrically implausible, semi-plausible, or plausible layouts. Participants tracked rivalry fluctuations in each of these three layouts and for both static and moving rival stimuli. Results revealed significant and canonical binocular rivalry alternations regardless of geometrical plausibility and stimulus type. Rivalry occurred for layouts that mirrored the unnatural geometry used in laboratory studies and for layouts that mimicked real-world occlusion geometry. In a complementary 3D modeling analysis, we show that interocular conflict caused by geometrically plausible occlusion is a common outcome in a visual scene containing multiple objects. Together, our findings demonstrate that binocular rivalry can reliably occur for both geometrically implausible interocular conflicts and conflicts caused by a common form of naturalistic occlusion. Thus, key features of binocular rivalry are not simply laboratory artifacts but generalize to conditions that match the geometry of everyday binocular vision.
Keyphrases
  • virtual reality
  • optical coherence tomography
  • deep learning
  • computed tomography
  • machine learning
  • single cell
  • case control