THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

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 den...

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Vydané v:eLife Ročník 12
Hlavní autori: Hebart, Martin N, Contier, Oliver, Teichmann, Lina, Rockter, Adam H, Zheng, Charles Y, Kidder, Alexis, Corriveau, Anna, Vaziri-Pashkam, Maryam, Baker, Chris I
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England eLife Science Publications, Ltd 27.02.2023
eLife Sciences Publications, Ltd
eLife Sciences Publications Ltd
Predmet:
behavior
Brain
Brain - diagnostic imaging
Brain Mapping - methods
fMRI
Human acts
Human behavior
Humans
Investigations
Magnetic Resonance Imaging - methods
Magnetoencephalography - methods
MEG
Neurophysiology
Neuroscience
objects
Pattern Recognition, Visual - physiology
Reproducibility of Results
research data
Tools and Resources
vision
fMRI
vision
research data
neuroscience
objects
behavior
human
MEG
ISSN:2050-084X, 2050-084X
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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.
AbstractList 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.
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.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.
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.
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 (
Audience Academic
Author Rockter, Adam H
Zheng, Charles Y
Corriveau, Anna
Teichmann, Lina
Vaziri-Pashkam, Maryam
Hebart, Martin N
Kidder, Alexis
Contier, Oliver
Baker, Chris I
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  orcidid: 0000-0001-6861-8964
  surname: Baker
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/36847339$$D View this record in MEDLINE/PubMed
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Keywords fMRI
vision
research data
neuroscience
objects
behavior
human
MEG
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Snippet Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and...
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SubjectTerms behavior
Brain
Brain - diagnostic imaging
Brain Mapping - methods
fMRI
Human acts
Human behavior
Humans
Investigations
Magnetic Resonance Imaging - methods
Magnetoencephalography - methods
MEG
Neurophysiology
Neuroscience
objects
Pattern Recognition, Visual - physiology
Reproducibility of Results
research data
Tools and Resources
vision
Title THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior
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