Evidence map›Paper›PMID 41841665›Full record

ArticleJournal of vision2026

Characterizing surround suppression with dynamic natural scenes.

Merve Kiniklioglu, Daniel Kaiser

Abstract readComparative Study
In one paragraph

Article in Journal of vision, 2026. 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

2 authors.

Merve KinikliogluNeural Computation Group, Department of Mathematics and Computer Science, Physics, Geography, Justus Liebig University Gießen, Gießen, Germany.
Daniel KaiserNeural Computation Group, Department of Mathematics and Computer Science, Physics, Geography, Justus Liebig University Gießen, Gießen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surround suppression refers to the reduction in perceptual sensitivity to a central stimulus caused by its surrounding context. Previous studies have examined this phenomenon mainly with simple stimuli varying only in low-level visual features, leaving it unclear whether the same principles apply to natural scenes. To address this, we investigated surround suppression in complex and dynamic scenes by systematically manipulating categorical similarity and motion congruence between the center and surrounding scenes. Categorical similarity was examined across four levels: identical exemplars, different exemplars of the same basic-level category, different basic-level categories, and different superordinate categories. Motion direction was manipulated by center and surround drifting either in the same or opposite directions. In two experiments, we measured contrast sensitivity for the center scene during a categorization task. We found that the suppression increased as the categorical similarity between the center and the surround decreased, with the strongest suppression observed for different superordinate categories. This finding contrasts with results for simple stimuli, where increasing center-surround similarity increases suppression. Yet, consistent with the findings from simple stimuli, suppression was stronger when the center and surround moved in the same direction. These findings show that contextual modulation in natural vision is governed not only by low-level feature similarity but also by high-level categorical structure. Context-dependent suppression may therefore help the visual system to prioritize coherent, task-relevant information while filtering incongruent input.

Indexed as

Contrast SensitivityMotion PerceptionPattern Recognition, VisualHumansPhotic Stimulation

Identifiers

PMID41841665
PMCPMC13007443

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.