ArticleNature machine intelligence2025
Dimensions underlying the representational alignment of deep neural networks with humans.
Article in Nature machine intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Procrastination as a neurocognitive syndrome: evidence from VBM, RSFC, and Temporal activation patterns in executive and Default-Mode networks.Brain imaging and behavior · 2026Pooled it
- Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges.BMC medical imaging · 2026Pooled it
- Systematic image perturbations reveal persistent gaps between human and machine vision.iScience · 2026Article
- Multidimensional Feature Tuning in Category Selective Areas of Human Visual Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Article
- Representations in vision and language converge in a shared, multidimensional space of perceived similarities.Journal of vision · 2026Article
- Drawings of THINGS: A large-scale drawing dataset of 1854 object concepts.Behavior research methods · 2026Article
- Do Language Models Know Who Did What to Whom?Open mind : discoveries in cognitive science · 2026Article
- BFMGHC: a boosted fuzzy manifold granule hypersurface classifier with local topology preservation.Frontiers in neurorobotics · 2026Article
- Investigating action topography in visual cortex and deep artificial neural networks.Nature communications · 2025Article
- Revealing emergent human-like conceptual representations from language prediction.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Hippocampal transformations occur along dimensions of memory interference.bioRxiv : the preprint server for biology · 2025Article
- Task-relevant representational spaces in human memory traces.PLoS computational biology · 2025Article
- Article
Corrections and comments
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Determining the similarities and differences between humans and artificial intelligence (AI) is an important goal in both computational cognitive neuroscience and machine learning, promising a deeper understanding of human cognition and safer, more reliable AI systems. Much previous work comparing representations in humans and AI has relied on global, scalar measures to quantify their alignment. However, without explicit hypotheses, these measures only inform us about the degree of alignment, not the factors that determine it. To address this challenge, we propose a generic framework to compare human and AI representations, based on identifying latent representational dimensions underlying the same behaviour in both domains. Applying this framework to humans and a deep neural network (DNN) model of natural images revealed a low-dimensional DNN embedding of both visual and semantic dimensions. In contrast to humans, DNNs exhibited a clear dominance of visual over semantic properties, indicating divergent strategies for representing images. Although in silico experiments showed seemingly consistent interpretability of DNN dimensions, a direct comparison between human and DNN representations revealed substantial differences in how they process images. By making representations directly comparable, our results reveal important challenges for representational alignment and offer a means for improving their comparability.
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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.