ArticleNature communications2021
Limits to visual representational correspondence between convolutional neural networks and the human brain.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 62 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
62 citing papers in PubMed.
- Article
- Category selectivity observed in the human brain is distinct from category selectivity observed in artificial neural networks.bioRxiv : the preprint server for biology · 2026Article
- Article
- Modeling attention and binding in the brain through bidirectional recurrent gating.Nature communications · 2026Article
- A population vector model of visual working memory for real-world scenes.Journal of experimental psychology. General · 2026Article
- Physical AI goes to the operating room: are we ready for the Surgical Data Factory?Annals of surgical treatment and research · 2026Review
- What makes memories vivid?Journal of experimental psychology. General · 2026Article
- A Population Vector Model of Visual Working Memory for Real-World Scenes.bioRxiv : the preprint server for biology · 2026Article
- Data-driven ANN-based visual decoding enables unsupervised functional alignment.Communications biology · 2026Article
- Using fMRI Representations of Single Objects to Predict Multiple Objects in Working Memory in Human Occipitotemporal and Posterior Parietal Cortices.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Neural mechanisms of visual quality perception and adaptability in the visual pathway.Patterns (New York, N.Y.) · 2025Article
- Synthesizing images to map neural networks to the human brain.bioRxiv : the preprint server for biology · 2025Article
- Wordsworth: A generative word dataset for comparison of speech representations in humans and neural networks.Scientific data · 2025Article
- Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.Medical physics · 2025Article
- Visual search and real-image similarity: An empirical assessment through the lens of deep learning.Psychonomic bulletin & review · 2025Article
- Integrating Humidity-Resistant and Colorimetric COF-on-MOF Sensors with Artificial Intelligence Assisted Data Analysis for Visualization of Volatile Organic Compounds Sensing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Coordinating multiple mental faculties during learning.Scientific reports · 2025Article
- Beyond Letters: Optimal Transport as a Model for Sub-Letter Orthographic Processing.Neurobiology of language (Cambridge, Mass.) · 2025Article
- On the ability of standard and brain-constrained deep neural networks to support cognitive superposition: a position paper.Cognitive neurodynamics · 2024Article
- Neuroscientific insights about computer vision models: a concise review.Biological cybernetics · 2024Review
2 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
Convolutional neural networks (CNNs) are increasingly used to model human vision due to their high object categorization capabilities and general correspondence with human brain responses. Here we evaluate the performance of 14 different CNNs compared with human fMRI responses to natural and artificial images using representational similarity analysis. Despite the presence of some CNN-brain correspondence and CNNs' impressive ability to fully capture lower level visual representation of real-world objects, we show that CNNs do not fully capture higher level visual representations of real-world objects, nor those of artificial objects, either at lower or higher levels of visual representations. The latter is particularly critical, as the processing of both real-world and artificial visual stimuli engages the same neural circuits. We report similar results regardless of differences in CNN architecture, training, or the presence of recurrent processing. This indicates some fundamental differences exist in how the brain and CNNs represent visual information.
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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.