ArticlePLoS computational biology2020
Natural scene statistics predict how humans pool information across space in surface tilt estimation.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Common Prescription Lens Correction Causes Motion Illusions in the Presbyopic and General Populations.Investigative ophthalmology & visual science · 2026Article
- Monovision-induced motion illusions in presbyopic and non-presbyopic populations.bioRxiv : the preprint server for biology · 2025Article
- Optimal Estimation of Local Motion-in-Depth with Naturalistic Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Continuous psychophysics shows millisecond-scale visual processing delays are faithfully preserved in movement dynamics.Journal of vision · 2024Article
- Shape, perspective, and what is and is not perceived: Comment on Morales, Bax, and Firestone (2020).Psychological review · 2023Article
- Perceptual consequences of interocular differences in the duration of temporal integration.Journal of vision · 2022Article
- Stereo slant discrimination of planar 3D surfaces: Frontoparallel versus planar matching.Journal of vision · 2022Article
- Article
- Contact lenses, the reverse Pulfrich effect, and anti-Pulfrich monovision corrections.Scientific reports · 2020Article
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Authors and funding
2 authors.
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Abstract
Visual systems estimate the three-dimensional (3D) structure of scenes from information in two-dimensional (2D) retinal images. Visual systems use multiple sources of information to improve the accuracy of these estimates, including statistical knowledge of the probable spatial arrangements of natural scenes. Here, we examine how 3D surface tilts are spatially related in real-world scenes, and show that humans pool information across space when estimating surface tilt in accordance with these spatial relationships. We develop a hierarchical model of surface tilt estimation that is grounded in the statistics of tilt in natural scenes and images. The model computes a global tilt estimate by pooling local tilt estimates within an adaptive spatial neighborhood. The spatial neighborhood in which local estimates are pooled changes according to the value of the local estimate at a target location. The hierarchical model provides more accurate estimates of groundtruth tilt in natural scenes and provides a better account of human performance than the local estimates. Taken together, the results imply that the human visual system pools information about surface tilt across space in accordance with natural scene statistics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.