A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915785380724736 |
|---|---|
| author | Gifford, Alessandro T. Cichy, Radoslaw M. Naselaris, Thomas Kay, Kendrick |
| author_facet | Gifford, Alessandro T. Cichy, Radoslaw M. Naselaris, Thomas Kay, Kendrick |
| contents | Now published in Nature Communications DOI: https://doi.org/10.1038/s41467-026-69345-9
Large-scale visual neural datasets such as the Natural Scenes Dataset (NSD) are boosting computational neuroscience research by 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 do not allow for strict out-of-distribution (OOD) generalization tests which are crucial for the development of more robust models. Here, we address this limitation by releasing NSD-synthetic, a dataset consisting 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. Furthermore, we provide a proof of principle that OOD generalization tests on NSD-synthetic reveal differences between models of the brain that are not detected with the original NSD data; we demonstrate that the degree of OOD (quantified as the distance between a set of responses and the training data used for modeling) is predictive of the magnitude of model failures; and we show that less strict OOD generalization tests can can be usefully applied even within the domain of naturalistic stimuli. These results showcase how NSD-synthetic enables OOD generalization tests that facilitate the development of more robust models of visual processing and the formulation of more accurate theories of human vision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06286 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision Gifford, Alessandro T. Cichy, Radoslaw M. Naselaris, Thomas Kay, Kendrick Neurons and Cognition Now published in Nature Communications DOI: https://doi.org/10.1038/s41467-026-69345-9 Large-scale visual neural datasets such as the Natural Scenes Dataset (NSD) are boosting computational neuroscience research by 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 do not allow for strict out-of-distribution (OOD) generalization tests which are crucial for the development of more robust models. Here, we address this limitation by releasing NSD-synthetic, a dataset consisting 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. Furthermore, we provide a proof of principle that OOD generalization tests on NSD-synthetic reveal differences between models of the brain that are not detected with the original NSD data; we demonstrate that the degree of OOD (quantified as the distance between a set of responses and the training data used for modeling) is predictive of the magnitude of model failures; and we show that less strict OOD generalization tests can can be usefully applied even within the domain of naturalistic stimuli. These results showcase how NSD-synthetic enables OOD generalization tests that facilitate the development of more robust models of visual processing and the formulation of more accurate theories of human vision. |
| title | A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2503.06286 |