Evidence map›Paper›PMID 40793548›Full record

ArticleJournal of the Optical Society of America. A, Optics, image science, and vision2025

Sensitivity to the number of colors in textures defined by luminance and chromatic contrast.

Jesse R Macyczko, Michael A Webster

Abstract read
In one paragraph

Article in Journal of the Optical Society of America. A, Optics, image science, and vision, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jesse R Macyczko
Michael A Webster

Funding

Virtual and Augmented Reality Interaction CoreP30GM145646 · NIGMS · UNIVERSITY OF NEVADA RENO · PI MICHAEL A WEBSTER · 2022 to 2026
$6.8M
SALIENCE, ADAPTATION, AND NATURAL IMAGE STATISTICSR01EY010834 · NEI · UNIVERSITY OF NEVADA RENO · PI WEBSTER, MICHAEL A · 2000 to 2023
$4.6M
NEI NIH HHS R01 EY010834NIGMS NIH HHS P30 GM145646
6 · The paper itself

Abstract

Changes in the mean or variance of a color ensemble can be readily detected, but how sensitive are observers to the actual number of colors defining the array? Discriminating changes in the number of unique hues or saturations in the set becomes difficult when the set reaches four or more levels [J. Opt. Soc. Am. A42, B413 (2025)JOAOD60740-323210.1364/JOSAA.31.545299]. Here we extended these results to examine palettes that vary in both luminance and chromaticity. Stimuli were textures of 11×11 square elements, with the color of each element chosen randomly from a palette. The palettes consisted of colors that were evenly spaced along different planes in cardinal axis color space (LM, S, and achromatic). In the LM versus S plane, elements varied only in hue angle at constant chromatic contrast, while in achromatic versus chromatic planes, the sets varied in both luminance and chromatic contrast. Four textures were displayed on a monitor and observers judged which one contained an additional color. Accuracy degraded as the set size increased beyond three colors. However, when the textures included different luminance levels, the set size differences remained more visible. This advantage persisted over large changes in the maximum luminance contrast and was accentuated when pure black or white elements were present, potentially reflecting sensitivity to pure achromatic features. The results suggest that, while numerosity judgments of color are in general very restricted, different attributes of color (hue, saturation, and brightness) exhibit different properties in texture perception, with enhanced sensitivity to luminance features.

Identifiers

PMID40793548
PMCPMC12344279

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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.