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

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

doi:10.1101/2022.07.22.501123

bioRxiv · 10.1101/2022.07.22.501123

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

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

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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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Hebart, M. N., Contier, O., Teichmann, L., Rockter, A., Zheng, C. Y., Kidder, A., Corriveau, A., Vaziri-Pashkam, M., Baker, C. I.. 2022-07-23. THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in brain and behavior. https://doi.org/10.1101/2022.07.22.501123

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