ArticleAdvances in neural information processing systems2023
Brain encoding models based on multimodal transformers can transfer across language and vision.
Article in Advances in neural information processing systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Hierarchical learning creates invariant schema within plastic neural networks.Journal of computational neuroscience · 2026Article
- Contrastive learning to fine-tune feature extraction models for the visual cortex.PLoS computational biology · 2026Article
- Representations in vision and language converge in a shared, multidimensional space of perceived similarities.Journal of vision · 2026Article
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- Decoding concept representations in aphasia after stroke.bioRxiv : the preprint server for biology · 2026Article
- Disentangling Superpositions: Interpretable Brain Encoding Model with Sparse Concept Atoms.bioRxiv : the preprint server for biology · 2026Article
- Do Language Models Know Who Did What to Whom?Open mind : discoveries in cognitive science · 2026Article
- Transformers for fMRI analysis: from spatiotemporal modeling to brain foundation models.Frontiers in neuroinformatics · 2026Review
- Rapid cortical mapping with cross-participant encoding models.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Multilingual Computational Models Capture a Shared Meaning Component in Brain Responses across 21 Languages.bioRxiv : the preprint server for biology · 2025Article
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- Multi-voxel pattern analysis for developmental cognitive neuroscientists.Developmental cognitive neuroscience · 2025Review
- Semantic language decoding across participants and stimulus modalities.Current biology : CB · 2025Article
- Multisensory naturalistic decoding with high-density diffuse optical tomography.Neurophotonics · 2025Article
- High-level visual representations in the human brain are aligned with large language models.Nature machine intelligence · 2025Article
- A large-scale examination of inductive biases shaping high-level visual representation in brains and machines.Nature communications · 2024Article
- The Multiscale Surface Vision Transformer.ArXiv · 2024Article
- A functional account of stimulation-based aerobic glycolysis and its role in interpreting BOLD signal intensity increases in neuroimaging experiments.Neuroscience and biobehavioral reviews · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Encoding models have been used to assess how the human brain represents concepts in language and vision. While language and vision rely on similar concept representations, current encoding models are typically trained and tested on brain responses to each modality in isolation. Recent advances in multimodal pretraining have produced transformers that can extract aligned representations of concepts in language and vision. In this work, we used representations from multimodal transformers to train encoding models that can transfer across fMRI responses to stories and movies. We found that encoding models trained on brain responses to one modality can successfully predict brain responses to the other modality, particularly in cortical regions that represent conceptual meaning. Further analysis of these encoding models revealed shared semantic dimensions that underlie concept representations in language and vision. Comparing encoding models trained using representations from multimodal and unimodal transformers, we found that multimodal transformers learn more aligned representations of concepts in language and vision. Our results demonstrate how multimodal transformers can provide insights into the brain's capacity for multimodal processing.
Identifiers
39015152PMC11250991What OpenQuestion holds
Registered trials
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