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<p>Large-scale visual neural datasets such as the Natural Scenes Dataset (NSD) are enabling models of the brain with performances beyond what was possible just a decade ago. However, because the stimuli of these datasets typically live within a common naturalistic visual distribution, they make it challenging to implement out-of-distribution (OOD) generalization tests crucial for the development of robust brain models. Here, we address this by releasing NSD-synthetic, a dataset of 7T fMRI responses from the same eight NSD participants for 284 synthetic images. We show that NSD-synthetic’s fMRI responses reliably encode stimulus-related information and are OOD with respect to NSD; that OOD generalization tests on NSD-synthetic reveal differences between brain models that are not detected in-distribution; and that the degree of OOD (quantified as the test data distance from the training data) is predictive of the magnitude of model failures. Together, NSD-synthetic enables OOD generalization tests that facilitate the development of more robust models of visual processing.</p>

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A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月9日

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  • Alessandro T. Gifford,
  • Radoslaw M. Cichy,
  • Thomas Naselaris,
  • Kendrick Kay

摘要

Large-scale visual neural datasets such as the Natural Scenes Dataset (NSD) are enabling models of the brain with performances beyond what was possible just a decade ago. However, because the stimuli of these datasets typically live within a common naturalistic visual distribution, they make it challenging to implement out-of-distribution (OOD) generalization tests crucial for the development of robust brain models. Here, we address this by releasing NSD-synthetic, a dataset of 7T fMRI responses from the same eight NSD participants for 284 synthetic images. We show that NSD-synthetic’s fMRI responses reliably encode stimulus-related information and are OOD with respect to NSD; that OOD generalization tests on NSD-synthetic reveal differences between brain models that are not detected in-distribution; and that the degree of OOD (quantified as the test data distance from the training data) is predictive of the magnitude of model failures. Together, NSD-synthetic enables OOD generalization tests that facilitate the development of more robust models of visual processing.