Evidence map›Paper›PMID 39242968›Full record

ArticleCommunications psychology2024

Anchor objects drive realism while diagnostic objects drive categorization in GAN generated scenes.

Aylin Kallmayer, Melissa L-H Võ

Abstract read
In one paragraph

Article in Communications psychology, 2024. 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.

Aylin KallmayerGoethe University Frankfurt, Department of Psychology, Frankfurt am Main, Germany. kallmayer@psych.uni-frankfurt.de.ORCID http://orcid.org/0000-0001-8143-4756
Melissa L-H VõGoethe University Frankfurt, Department of Psychology, Frankfurt am Main, Germany.ORCID http://orcid.org/0000-0003-1145-4473

Funding

Deutsche Forschungsgemeinschaft (German Research Foundation) 222641018-SFB/TRR 135
6 · The paper itself

Abstract

Our visual surroundings are highly complex. Despite this, we understand and navigate them effortlessly. This requires transforming incoming sensory information into representations that not only span low- to high-level visual features (e.g., edges, object parts, objects), but likely also reflect co-occurrence statistics of objects in real-world scenes. Here, so-called anchor objects are defined as being highly predictive of the location and identity of frequently co-occuring (usually smaller) objects, derived from object clustering statistics in real-world scenes, while so-called diagnostic objects are predictive of the larger semantic context (i.e., scene category). Across two studies (N

Identifiers

PMID39242968
PMCPMC11332195

What OpenQuestion holds

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LicenceCC BY
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.