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8 results for “visual discomfort”
A mechanistic account of visual discomfort
<p>Much of the neural machinery of the early visual cortex, from the extraction of local orientations to contextual modulations through lateral interactions, is thought to have developed to provide a sparse encoding of contour in natural scenes, allowing the brain to process efficiently most of the visual scenes we are exposed to. Certain visual stimuli, however, cause visual stress, a set of adverse effects ranging from simple discomfort to migraine attacks, and epileptic seizures in the extreme, all phenomena linked with an excessive metabolic demand. The theory of efficient coding suggests a link between excessive metabolic demand and images that deviate from natural statistics. Yet, the mechanisms linking energy demand and image spatial content in discomfort remain elusive. Here, we used theories of visual coding that link image spatial structure and brain activation to characterize the response to images observers reported as uncomfortable. Our biologically based neurodynamic model of the early visual cortex included excitatory and inhibitory layers to implement contextual influences. We found three clear markers of aversive images: a larger overall activation in the model, a less sparse response, and a more unbalanced distribution of activity across spatial orientations. When the ratio of excitation over inhibition was increased in the model, a phenomenon hypothesised to underlie interindividual differences in susceptibility to visual discomfort, the three markers of discomfort progressively shifted towards values typical of the response to uncomfortable stimuli. Overall, these findings propose a unifying mechanistic explanation for why there are differences between images and between observers, suggesting how visual input and idiosyncratic hyperexcitability give rise to abnormal brain responses that result in visual stress.</p>
Visual discomfort and variations in chromaticity in art and nature
<p>Visual discomfort is related to the statistical regularity of visual images. The contribution of luminance contrast to visual discomfort is well understood and can be framed in terms of a theory of efficient coding of natural stimuli, and linked to metabolic demand. While colour is important in our interaction with nature, the effect of colour on visual discomfort has received less attention. In this study, we build on the established association between visual discomfort and differences in chromaticity across space. We average the local differences in chromaticity in an image and show that this average is a good predictor of visual discomfort from the image. It accounts for part of the variance left unexplained by variations in luminance. We show that the local chromaticity difference in uncomfortable stimuli is high compared to that typical in natural scenes, except in particular infrequent conditions such as the arrangement of colourful fruits against foliage. Overall, our study discloses a new link between visual ecology and discomfort whereby discomfort arises when adaptive perceptual mechanisms are overstimulated by specific classes of stimuli rarely found in nature.</p>
Visual discomfort and variations in chromaticity in art and nature
Open the record for dataset details and reuse information.
A mechanistic account of visual discomfort
Open the record for dataset details and reuse information.
Visual Discomfort and Reading
ClinicalTrials.gov study NCT00402155. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Training and testing labelled dataset of human faces for different indoor visual comfort and glare visual discomfort situations - images
<p>The image dataset is generated from experimental tests on 23 different participants and includes a certain variety in characteristics: age, gender, face feature, glasses, etc. The original dataset was generated from experimental tests on 24 different participants but all the data of one of the participants was entirely excluded as this participant did not want his/her data to be publically available.</p> <p>After synchronization of the video footages with the answers of the participants regarding their visual discomfort at the different phases of the experiment to label the video dataset, the video footages are resampled at a 0.5 FPS rate, and the YOLOFace extraction algorithm is used to crop the image down to the face of the occupants only and generate the labelled image dataset. </p>
Training and testing labelled dataset of human faces for different indoor visual comfort and glare visual discomfort situations - videos
<p>The video dataset is generated from experimental tests on 23 different participants and includes a certain variety in characteristics: age, gender, face feature, glasses, etc. The original dataset was generated from experimental tests on 24 different participants but all the data of one of the participants was entirely excluded as this participant did not want his/her data to be publically available.</p> <p>After synchronization of the video footages with the answers of the participants regarding their visual discomfort at the different phases of the experiment to label the video dataset, the video footages are resampled at a 0.5 FPS rate, and the YOLOFace extraction algorithm is used to crop the image down to the face of the occupants only and generate the labelled image dataset. </p>
Training and testing labelled dataset of human faces for different indoor visual comfort and glare visual discomfort situations - videos
<p>The video dataset is generated from experimental tests on 23 different participants and includes a certain variety in characteristics: age, gender, face feature, glasses, etc. The original dataset was generated from experimental tests on 24 different participants but all the data of one of the participants was entirely excluded as this participant did not want his/her data to be publically available.</p> <p>After synchronization of the video footages with the answers of the participants regarding their visual discomfort at the different phases of the experiment to label the video dataset, the video footages are resampled at a 0.5 FPS rate, and the YOLOFace extraction algorithm is used to crop the image down to the face of the occupants only and generate the labelled image dataset. </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.