O'Hare Louise, Hibbard Paul B
NTU Psychology, Nottingham Trent University, Nottingham, UK.
Department of Psychology to Division of Psychology, University of Stirling, Stirling, UK.
Vis Neurosci. 2024 Dec 26;41:E008. doi: 10.1017/S0952523824000051.
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.
稀疏编码理论表明，视觉大脑经过优化，可对自然视觉刺激进行编码，以将代谢成本降至最低。人们认为，与自然图像统计特性不同的图像无法有效编码，并会导致视觉不适。相反，与自然图像相比，艺术品被认为能得到更高效的处理，因此在美学上令人愉悦。本项目结合低层次图像统计分析、数学建模和脑电图测量，研究了令人不适的图像、自然场景和艺术品中的视觉不适。结果表明，模型反应可预测不适判断。此外，包括边缘可预测性在内的低层次图像统计可预测不适判断，而对比度信息则可预测稳态视觉诱发电位反应。总之，本研究表明，对于大量图像的不适判断会受到对比度和边缘信息的影响，并可由我们的低层次视觉模型预测，而当对比度允许变化时，神经反应更多地由基于对比度的指标来定义。
