ArticleVisual neuroscience2024
Support for the efficient coding account of visual discomfort.
Article in Visual neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A Cerebral Basis for Visual Discomfort and Visual Stress.Vision (Basel, Switzerland) · 2026Review
- Perirenal fat thickness may be a significant predictor of prognosis and postoperative renal function in renal cancer surgery patients.Scientific reports · 2025Article
- Corneal Nerve Morphology in Painful Diabetic Neuropathy: A Meta-Analysis of In Vivo Confocal Microscopy Studies.Biomedicines · 2025Review
- Prevalence and factors influencing overweight/obesity and poor vision in children and adolescents before and after the COVID-19 pandemic in a city in Sichuan Province.Frontiers in public health · 2025Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Sparse coding theories suggest that the visual brain is optimized to encode natural visual stimuli to minimize metabolic cost. It is thought that images that do not have the same statistical properties as natural images are unable to be coded efficiently and result in visual discomfort. Conversely, artworks are thought to be even more efficiently processed compared to natural images and so are esthetically pleasing. This project investigated visual discomfort in uncomfortable images, natural scenes, and artworks using a combination of low-level image statistical analysis, mathematical modeling, and EEG measures. Results showed that the model response predicted discomfort judgments. Moreover, low-level image statistics including edge predictability predict discomfort judgments, whereas contrast information predicts the steady-state visually evoked potential responses. In conclusion, this study demonstrates that discomfort judgments for a wide set of images can be influenced by contrast and edge information, and can be predicted by our models of low-level vision, whilst neural responses are more defined by contrast-based metrics, when contrast is allowed to vary.
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