Evidence map›Paper›PMID 37747401›Full record

ArticleJournal of vision2023

Phase-dependent asymmetry of pattern masking in natural images explained by intrinsic position uncertainty.

Anqi Zhang, Eric S Seemiller, Wilson S Geisler

Abstract read
In one paragraph

Article in Journal of vision, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Anqi ZhangCenter for Perceptual Systems, University of Texas at Austin, Austin, TX, USA.
Eric S Seemiller711th Human Performance Wing, Air Force Research Labs, Wright-Patterson AFB, OH, USA.
Wilson S GeislerCenter for Perceptual Systems, University of Texas at Austin, Austin, TX, USA.

Funding

PERCEPTUAL ORGANIZATION OF TWO DIMENSIONAL PATTERNSR01EY011747 · NEI · UNIVERSITY OF TEXAS AUSTIN · PI GEISLER, WILSON S · 1997 to 2022
$6.4M
Visual Search and Detection in Natural ScenesR01EY024662 · NEI · UNIVERSITY OF TEXAS AT AUSTIN · PI GEISLER, WILSON S · 2015 to 2022
$2.9M
NEI NIH HHS R01 EY011747NEI NIH HHS R01 EY024662
6 · The paper itself

Abstract

A number of recent studies have been directed at measuring and modeling detection of targets at specific locations in natural backgrounds, a key subtask of visual search in natural environments. A useful approach is to bin natural background patches into joint histograms with bins along specific background dimensions. By measuring psychometric functions in a sparse subset of these bins, it is possible to estimate how the included dimensions jointly affect detectability over the whole space of natural backgrounds. In previous studies, we found that threshold is proportional to the product of the background luminance, contrast, and similarity; a result predicted by a simple template-matching observer with divisive normalization along each of the dimensions. The measure of similarity was the cosine similarity of the amplitude spectra of the target and background (SA)-a phase-invariant measure. Here, we investigated the effect of the cosine similarity of the target and background images (SI|A)-a phase-dependent measure. We found that threshold decreases monotonically with SI|A in agreement with a recent study (Rideaux et al., 2022). In contrast, the template-matching observer predicts threshold to be a U-shaped function of SI|A reaching a minimum when the target and background are orthogonal (SI|A = 0). Surprisingly, when the template-matching observer includes a small amount of intrinsic position uncertainty (measured in a separate experiment) the pattern of thresholds is explained.

Indexed as

EnvironmentHumansPsychometricsUncertainty

Identifiers

PMID37747401
PMCPMC10540874

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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.