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

Author:

Hebart M.N.ORCID,Contier O.ORCID,Teichmann L.ORCID,Rockter A.H.ORCID,Zheng C.Y.ORCID,Kidder A.ORCID,Corriveau A.ORCID,Vaziri-Pashkam M.ORCID,Baker C.I.ORCID

Abstract

AbstractUnderstanding 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.

Publisher

Cold Spring Harbor Laboratory

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