ArticleScience advances2021
Cortical response to naturalistic stimuli is largely predictable with deep neural networks.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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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.
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Who cites it
27 citing papers in PubMed, 52 citations in OpenAlex.
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Developmental changes in similarity between neural representations of mental arithmetic and artificial neural networks.iScience · 2026Article
- Frontal cortex organization supporting audiovisual processing during naturalistic viewing.Nature communications · 2026Article
- Higher visual areas act like domain-general filters with strong selectivity and functional specialization.Nature communications · 2026Article
- A view-engage-predict framework for enhancing brain-behavior mapping with naturalistic movie-watching fMRI.Communications biology · 2026Article
- Pixel-level understanding of a world in motion within a neural encoding framework.Scientific reports · 2026Article
- Pregistered movie-fMRI analyses reveal altered visual feature encoding in autism in pSTS.bioRxiv : the preprint server for biology · 2026Article
- The neural correlates of shared and individual experience.Communications biology · 2026Article
- Rapid cortical mapping with cross-participant encoding models.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Cross-modal deep generative models reveal the cortical representation of dancing.Nature communications · 2025Article
- A hierarchy of processing complexity and timescales for natural sounds in the human auditory cortex.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Stimulus Selection Influences Prediction of Individual Phenotypes in Naturalistic Conditions.Human brain mapping · 2025Article
- Neural processing of naturalistic audiovisual events in space and time.Communications biology · 2025Article
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- The Voxelwise Encoding Model framework: A tutorial introduction to fitting encoding models to fMRI data.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Encoding models for developmental cognitive computational neuroscience: Promise, challenges, and potential.Developmental cognitive neuroscience · 2024Review
- Dissonant music engages early visual processing.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Exploring Types of Photonic Neural Networks for Imaging and Computing-A Review.Nanomaterials (Basel, Switzerland) · 2024Review
- Neural encoding with unsupervised spiking convolutional neural network.Communications biology · 2023Article
- A large-scale fMRI dataset for the visual processing of naturalistic scenes.Scientific data · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict neural responses to arbitrary stimuli can be very useful for studying brain function. However, existing models focus on limited aspects of naturalistic stimuli, ignoring the dynamic interactions of modalities in this inherently context-rich paradigm. Using movie-watching data from the Human Connectome Project, we build group-level models of neural activity that incorporate several inductive biases about neural information processing, including hierarchical processing, temporal assimilation, and auditory-visual interactions. We demonstrate how incorporating these biases leads to remarkable prediction performance across large areas of the cortex, beyond the sensory-specific cortices into multisensory sites and frontal cortex. Furthermore, we illustrate that encoding models learn high-level concepts that generalize to task-bound paradigms. Together, our findings underscore the potential of encoding models as powerful tools for studying brain function in ecologically valid conditions.
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.