ArticleJournal of vision2021
Five points to check when comparing visual perception in humans and machines.
Article in Journal of vision, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 66 citations in OpenAlex.
- PIP-PACA: An Interpretable Image Classification Framework via Prototype-Aware Clustering Attention.Journal of imaging · 2026Article
- Behavioral differences between humans and machines arise early in visual processing.Journal of vision · 2026Article
- Distinct visual biases affect humans and artificial intelligence in medical imaging diagnoses.NPJ digital medicine · 2025Review
- Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning.Nature communications · 2025Article
- Machine Learning Techniques for Simulating Human Psychophysical Testing of Low-Resolution Phosphene Face Images in Artificial Vision.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition.PLoS computational biology · 2025Article
- Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A brain-inspired object-based attention network for multiobject recognition and visual reasoning.Journal of vision · 2023Article
- When will AI misclassify? Intuiting failures on natural images.Journal of vision · 2023Article
- GAMR: A Guided Attention Model for (visual) Reasoning.... International Conference on Learning Representations · 2023Article
- Article
- Perception without preconception: comparison between the human and machine learner in recognition of tissues from histological sections.Scientific reports · 2022Article
- Can deep convolutional neural networks support relational reasoning in the same-different task?Journal of vision · 2022Article
- On the synthesis of visual illusions using deep generative models.Journal of vision · 2022Article
- Could simplified stimuli change how the brain performs visual search tasks? A deep neural network study.Journal of vision · 2022Article
- Differences between human and machine perception in medical diagnosis.Scientific reports · 2022Article
- Motion illusion-like patterns extracted from photo and art images using predictive deep neural networks.Scientific reports · 2022Article
- Guiding visual attention in deep convolutional neural networks based on human eye movements.Frontiers in neuroscience · 2022Article
- Noise increases the correspondence between artificial and human vision.PLoS biology · 2021Article
- Grounding deep neural network predictions of human categorization behavior in understandable functional features: The case of face identity.Patterns (New York, N.Y.) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rise of machines to human-level performance in complex recognition tasks, a growing amount of work is directed toward comparing information processing in humans and machines. These studies are an exciting chance to learn about one system by studying the other. Here, we propose ideas on how to design, conduct, and interpret experiments such that they adequately support the investigation of mechanisms when comparing human and machine perception. We demonstrate and apply these ideas through three case studies. The first case study shows how human bias can affect the interpretation of results and that several analytic tools can help to overcome this human reference point. In the second case study, we highlight the difference between necessary and sufficient mechanisms in visual reasoning tasks. Thereby, we show that contrary to previous suggestions, feedback mechanisms might not be necessary for the tasks in question. The third case study highlights the importance of aligning experimental conditions. We find that a previously observed difference in object recognition does not hold when adapting the experiment to make conditions more equitable between humans and machines. In presenting a checklist for comparative studies of visual reasoning in humans and machines, we hope to highlight how to overcome potential pitfalls in design and inference.
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.