ArticleJournal of vision2011
Detection of changes in luminance distributions.
Article in Journal of vision, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Computational-observer analysis of illumination discrimination.Journal of vision · 2019Trial
- Human vision is attuned to the diffuseness of natural light.Journal of vision · 2014Article
- A Bayesian model of lightness perception that incorporates spatial variation in the illumination.Journal of vision · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
How well can observers detect the presence of a change in luminance distributions? Performance was measured in three experiments. Observers viewed pairs of grayscale images on a calibrated CRT display. Each image was a checkerboard. All luminances in one image of each pair consisted of random draws from a single probability distribution. For the other image, some patch luminances consisted of random draws from that same distribution, while the rest of the patch luminances (test patches) consisted of random draws from a second distribution. The observers' task was to pick the image with luminances drawn from two distributions. The parameters of the second distribution that led to 75% correct performance were determined across manipulations of (1) the number of test patches, (2) the observers' certainty about test patch location, and (3) the geometric structure of the images. Performance improved with number of test patches and location certainty. The geometric manipulations did not affect performance. An ideal observer model with high efficiency fit the data well and a classification image analysis showed a similar use of information by the ideal and human observers, indicating that observers can make effective use of photometric information in our distribution discrimination task.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.