ArticleeLife2023
THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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75 citing papers in PubMed.
- Food, mammals, & countries: Stimuli, their pairwise similarities, and MDS dimension values for modeling judgment, categorization, and memory in three real-world domains.Behavior research methods · 2026Article
- Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding.Journal of vision · 2026Article
- Concept2Brain: an AI model for predicting neurophysiological responses to text and pictures.Nature communications · 2026Article
- A flexible behavioral method for measuring human and artificial intelligence alignment using representational similarity analysis.iScience · 2026Article
- Progressive loss of independence in neuronal representations predicts cognitive decline.bioRxiv : the preprint server for biology · 2026Article
- Animate Categories Show Higher Cross-Duration Representational Selectivity in Ventral Occipitotemporal Cortex Under Brief Visual Input.Brain sciences · 2026Article
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- Large Language Models Estimate Fine-Grained Human Color-Concept Associations.Cognitive science · 2026Article
- Fixation duration on natural scenes is explained by memory encoding not processing demand.Nature neuroscience · 2026Article
- Towards a Mathematical Structure of Global Phenomenal Consciousness.Entropy (Basel, Switzerland) · 2026Article
- Cortical knowledge structures guide word concept learning.Nature communications · 2026Article
- Shared representations in brains and models reveal a two-route cortical organization during scene perception.Communications biology · 2026Article
- A large-scale fMRI dataset for vision-language semantic association.Scientific data · 2026Article
- Human-like cognitive generalization for large models via mental representation-guided supervision.Nature communications · 2026Article
- AI-Enhanced Semantic Feature Norms for 786 Concepts.Topics in cognitive science · 2026Article
- Rethinking category-selectivity in human visual cortex.Cognitive neuroscience · 2026Review
- Perceptual and conceptual influences on memory judgments.Communications psychology · 2026Article
- Determinants of visual ambiguity resolution.Communications psychology · 2026Article
- Dynamic Representation of Multidimensional Object Properties in the Human Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- How Robust Are fMRI- and EEG-Based Representational Similarity Analysis?Computational brain & behavior · 2026Article
15 more citing papers are in PubMed but not listed here.
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9 authors.
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Abstract
Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here, we present THINGS-data, a multimodal collection of large-scale neuroimaging and behavioral datasets in humans, comprising densely sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly annotated objects, allowing for testing countless hypotheses at scale while assessing the reproducibility of previous findings. Beyond the unique insights promised by each individual dataset, the multimodality of THINGS-data allows combining datasets for a much broader view into object processing than previously possible. Our analyses demonstrate the high quality of the datasets and provide five examples of hypothesis-driven and data-driven applications. THINGS-data constitutes the core public release of the THINGS initiative (https://things-initiative.org) for bridging the gap between disciplines and the advancement of cognitive neuroscience.
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