Evidence map›Paper›PMID 29384477›Full record

ArticleeLife2018

The lawful imprecision of human surface tilt estimation in natural scenes.

Seha Kim, Johannes Burge

Abstract read
In one paragraph

Article in eLife, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Optimal Estimation of Local Motion-in-Depth with Naturalistic Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
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  11. Article
  12. Article
  13. Article
  14. Predicting the Partition of Behavioral Variability in Speed Perception with Naturalistic Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020
    Article
  15. Learning to see stuff.Current opinion in behavioral sciences · 2019
    Review
  16. Article
  17. Picture perception reveals mental geometry of 3D scene inferences.Proceedings of the National Academy of Sciences of the United States of America · 2018
    Article
  18. Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Seha KimDepartment of Psychology, University of Pennsylvania, Philadelphia, United States.ORCID 0000-0003-0356-6168
Johannes BurgeDepartment of Psychology, University of Pennsylvania, Philadelphia, United States.ORCID 0000-0002-0311-7875

Funding

Scientific TransparencyP30EY001583 · NEI · UNIVERSITY OF PENNSYLVANIA · PI Geoffrey Karl Aguirre · 1985 to 2026
$19.5M
PERCEPTUAL ORGANIZATION OF TWO DIMENSIONAL PATTERNSR01EY011747 · NEI · UNIVERSITY OF TEXAS AUSTIN · PI GEISLER, WILSON S · 1997 to 2022
$6.4M
Estimation and Discrimination of Motion and Depth in Natural ScenesR01EY028571 · NEI · UNIVERSITY OF PENNSYLVANIA · PI BURGE, JOHANNES D. · 2018 to 2022
$1.9M
NEI NIH HHS EY011747NEI NIH HHS P30 EY001583NEI NIH HHS R01 EY011747NEI NIH HHS R01 EY028571
6 · The paper itself

Abstract

Estimating local surface orientation (slant and tilt) is fundamental to recovering the three-dimensional structure of the environment. It is unknown how well humans perform this task in natural scenes. Here, with a database of natural stereo-images having groundtruth surface orientation at each pixel, we find dramatic differences in human tilt estimation with natural and artificial stimuli. Estimates are precise and unbiased with artificial stimuli and imprecise and strongly biased with natural stimuli. An image-computable Bayes optimal model grounded in natural scene statistics predicts human bias, precision, and trial-by-trial errors without fitting parameters to the human data. The similarities between human and model performance suggest that the complex human performance patterns with natural stimuli are lawful, and that human visual systems have internalized local image and scene statistics to optimally infer the three-dimensional structure of the environment. These results generalize our understanding of vision from the lab to the real world.

Indexed as

OrientationVisual PerceptionBrainComputer SimulationHealthy VolunteersHumansBayesian estimationhumannatural scene statisticsneuroscienceslantsurface orientationtiltvision

Identifiers

PMID29384477
PMCPMC5844693

What OpenQuestion holds

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Registered trials

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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.