ArticleNature neuroscience2022
A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence.
Article in Nature neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 6 registered trials, which are not on this map. Cited by 260 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.
Probing the Role of Feature Dimension Maps in Visual Cognition: Expt 1.1
Probing the Role of Feature Dimension Maps in Visual Cognition: Expt 2.1
Probing the Role of Feature Dimension Maps in Visual Cognition: Expt 2.3
Probing the Role of Feature Dimension Maps in Visual Cognition: Expt 3.1 (Pilot)
Probing the Role of Feature Dimension Maps in Visual Cognition: Expt 1.1 (Behavioral)
Probing the Role of Feature Dimension Maps in Visual Cognition: Expt 2.2
Who cites it
260 citing papers in PubMed.
- Investigating effects of day-to-day variations in environmental exposure on the human brain: study protocol for the Day2Day Environment project.BMC neuroscience · 2026Article
- Retinotopic coding organizes the interaction between internally and externally oriented brain networks.eLife · 2026Article
- Frequency-filtered attention for cross-species fMRI time series prediction under small-sample learning.iScience · 2026Article
- Precision fMRI reveals densely interdigitated network patches with conserved motifs in the lateral prefrontal cortex.Neuron · 2026Article
- Model-based estimation of the population contrast response function in human visual cortex.Journal of vision · 2026Article
- Self-supervised learning yields representational signatures of category-selective cortex.Communications biology · 2026Article
- Eccentricity-Constrained CNN Training Reveals Adaptive Information Coding Around the Visual Field.ArXiv · 2026Article
- A single computational objective can produce specialization of streams in visual cortex.Nature communications · 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
- Contrastive learning to fine-tune feature extraction models for the visual cortex.PLoS computational biology · 2026Article
- Triple-N dataset: large-scale fMRI-guided dense recordings of nonhuman primate neural responses to natural scenes.Nature neuroscience · 2026Article
- Integrating neuroscience across species and scales.Nature neuroscience · 2026Article
- A 5.0 T Ultra-High-Field fMRI Dataset for Naturalistic Visual Scene Processing.Scientific data · 2026Article
- NeuroFusion: A Unified Framework for Generalized Visual Stimulus Decoding from fMRI Across Datasets and Subjects.Neuroinformatics · 2026Article
- Excessive Censoring Degrades Individual-Specific Cortical Parcellations and Personalized TMS Targets.bioRxiv : the preprint server for biology · 2026Article
- Talking to the Brain: Using Large Language Models as Proxies to Model Brain Semantic Features.Human brain mapping · 2026Article
- Probabilistic Forecasting and Information-Theoretic Analysis of Multivariate fMRI Dynamics.Entropy (Basel, Switzerland) · 2026Article
- Article
- Higher visual areas act like domain-general filters with strong selectivity and functional specialization.Nature communications · 2026Article
- Category selectivity observed in the human brain is distinct from category selectivity observed in artificial neural networks.bioRxiv : the preprint server for biology · 2026Article
200 more citing papers are in PubMed but not listed here.
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Authors and funding
13 authors.
Funding
Abstract
Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural scenes were measured while participants performed a continuous recognition task. To optimize data quality, we developed and applied novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we used NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality and breadth, NSD opens new avenues of inquiry in cognitive neuroscience and artificial intelligence.
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Identifiers
34916659What 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.