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For the possibility of internships or lab visits, please contact Mario Senden via mario.senden@maastrichtuniversity.nl


Curriculum vitae


Department of Cognitive Neuroscience

Maastricht University

Oxfordlaan 55
6229EV Maastricht





Department of Cognitive Neuroscience

Maastricht University

Oxfordlaan 55
6229EV Maastricht



Neural Correlates of High-Level Visual Saliency Models


Journal article


Alexander Kroner, M. Senden, R. Goebel
bioRxiv, 2023

Semantic Scholar DOI
Cite

Cite

APA   Click to copy
Kroner, A., Senden, M., & Goebel, R. (2023). Neural Correlates of High-Level Visual Saliency Models. BioRxiv.


Chicago/Turabian   Click to copy
Kroner, Alexander, M. Senden, and R. Goebel. “Neural Correlates of High-Level Visual Saliency Models.” bioRxiv (2023).


MLA   Click to copy
Kroner, Alexander, et al. “Neural Correlates of High-Level Visual Saliency Models.” BioRxiv, 2023.


BibTeX   Click to copy

@article{alexander2023a,
  title = {Neural Correlates of High-Level Visual Saliency Models},
  year = {2023},
  journal = {bioRxiv},
  author = {Kroner, Alexander and Senden, M. and Goebel, R.}
}

Abstract

Visual saliency highlights regions in a scene that are most relevant to an observer. The process by which a saliency map is formed has been a crucial subject of investigation in both machine vision and neuroscience. Deep learning-based approaches incorporate high-level information and have achieved accurate predictions of eye movement patterns, the overt behavioral analogue of a saliency map. As such, they may constitute a suitable surrogate of cortical saliency computations. In this study, we leveraged recent advances in computational saliency modeling and the Natural Scenes Dataset (NSD) to examine the relationship between model-based representations and the brain. Our aim was to uncover the neural correlates of high-level saliency and compare them with low-level saliency as well as emergent features from neural networks trained on different tasks. The results identified hV4 as a key region for saliency computations, informed by semantic processing in ventral visual areas. During natural scene viewing, hV4 appears to serve a transformative role linking low- and high-level features to attentional selection. Moreover, we observed spatial biases in ventral and parietal areas for saliency-based receptive fields, shedding light on the interplay between attention and oculomotor behavior.


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