ArticleNature neuroscience2023
Model metamers reveal divergent invariances between biological and artificial neural networks.
Article in Nature neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Identification of letters distorted by physiologically-inspired spatial scrambling.PLoS computational biology · 2026Article
- Perceptual misalignment of texture representations in convolutional neural networks.Journal of vision · 2026Article
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- MAME: Multidimensional adaptive metamer exploration with human perceptual feedback.Journal of vision · 2026Article
- Optimized feature gains explain and predict successes and failures of human selective listening.Nature human behaviour · 2026Article
- Modulation statistics allow robust prediction of speech recognition accuracy across many words, voices, and natural background sounds.bioRxiv : the preprint server for biology · 2026Article
- Convolutional neural network models describe the encoding subspace of local circuits in auditory cortex.Nature neuroscience · 2026Article
- Quantifying task-relevant representational similarity using decision variable correlation.ArXiv · 2026Article
- From sound to source: Human and model recognition of environmental sounds.bioRxiv : the preprint server for biology · 2026Article
- Deep Sound Synthesis Matched to Brain Activity Recapitulates Preferential Responses to Speech and Music.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Quantifying Task-relevant Similarities in Representations Using Decision Variable Correlations.Advances in neural information processing systems · 2025Article
- Dissociating the neural codes for multiple pitch perception in humans.bioRxiv : the preprint server for biology · 2025Article
- Deep learning-driven characterization of single cell tuning in primate visual area V4 supports topological organization.bioRxiv : the preprint server for biology · 2025Article
- A deep learning framework for understanding cochlear implants.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
- Interference of Mid-level Speech and Noise Statistics Underlies Human Speech Recognition Sensitivity in Natural Environmental Noise.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Natural sounds can be reconstructed from human neuroimaging data using deep neural network representation.PLoS biology · 2025Article
- The collapse of the wave function as the mediator of free will in prime neurons.Frontiers in neuroscience · 2025Article
- Universality of representation in biological and artificial neural networks.bioRxiv : the preprint server for biology · 2024Article
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4 authors.
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Abstract
Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated 'model metamers', stimuli whose activations within a model stage are matched to those of a natural stimulus. Metamers for state-of-the-art supervised and unsupervised neural network models of vision and audition were often completely unrecognizable to humans when generated from late model stages, suggesting differences between model and human invariances. Targeted model changes improved human recognizability of model metamers but did not eliminate the overall human-model discrepancy. The human recognizability of a model's metamers was well predicted by their recognizability by other models, suggesting that models contain idiosyncratic invariances in addition to those required by the task. Metamer recognizability dissociated from both traditional brain-based benchmarks and adversarial vulnerability, revealing a distinct failure mode of existing sensory models and providing a complementary benchmark for model assessment.
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