ArticleScientific data2023
Standardised images of novel objects created with generative adversarial networks.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Preserving Harmonic Structure in FPVS-Oddball: A Two-Dimensional Cluster-Based Permutation Approach.Psychophysiology · 2026Article
- Development and validation of an AI-generated real-world object stimuli set.Behavior research methods · 2026Article
- Generative adversarial networks enable biomimetic topology fusion with balanced mechanical performance and aesthetic quality.Scientific reports · 2026Article
- Synthesis and perceptual scaling of high-resolution naturalistic images using Stable Diffusion.Behavior research methods · 2025Article
- Novel Prototype and Exemplar (NPE) database: A set of 2700 novel 3D images with viewpoint and shape variations.Behavior research methods · 2025Article
- RailFOD23: A dataset for foreign object detection on railroad transmission lines.Scientific data · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
An enduring question in cognitive science is how perceptually novel objects are processed. Addressing this issue has been limited by the absence of a standardised set of object-like stimuli that appear realistic, but cannot possibly have been previously encountered. To this end, we created a dataset, at the core of which are images of 400 perceptually novel objects. These stimuli were created using Generative Adversarial Networks that integrated features of everyday stimuli to produce a set of synthetic objects that appear entirely plausible, yet do not in fact exist. We curated an accompanying dataset of 400 familiar stimuli, which were matched in terms of size, contrast, luminance, and colourfulness. For each object, we quantified their key visual properties (edge density, entropy, symmetry, complexity, and spectral signatures). We also confirmed that adult observers (N = 390) perceive the novel objects to be less familiar, yet similarly engaging, relative to the familiar objects. This dataset serves as an open resource to facilitate future studies on visual perception.
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.