ArticleeLife2020
What do adversarial images tell us about human vision?
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Artificial Intelligence-Empowered Radiology-Current Status and Critical Review.Diagnostics (Basel, Switzerland) · 2025Review
- Teaching deep networks to see shape: Lessons from a simplified visual world.PLoS computational biology · 2024Article
- Subtle adversarial image manipulations influence both human and machine perception.Nature communications · 2023Article
- When will AI misclassify? Intuiting failures on natural images.Journal of vision · 2023Article
- Remembrance of things perceived: Adding thalamocortical function to artificial neural networks.Frontiers in integrative neuroscience · 2023Review
- Feature blindness: A challenge for understanding and modelling visual object recognition.PLoS computational biology · 2022Article
- Davida's deficits: weak encoding of impoverished stimuli or faulty egocentric representation?Cognitive neuropsychology · 2022Article
- Training for object recognition with increasing spatial frequency: A comparison of deep learning with human vision.Journal of vision · 2021Article
- A comparative biology approach to DNN modeling of vision: A focus on differences, not similarities.Journal of vision · 2021Article
- Five points to check when comparing visual perception in humans and machines.Journal of vision · 2021Article
- Performance vs. competence in human-machine comparisons.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Deep convolutional neural networks (DCNNs) are frequently described as the best current models of human and primate vision. An obvious challenge to this claim is the existence of
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.