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ShareScore release 0.9.0
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47 results for “visual stimuli”
Dizziness Due to Visual Stimuli in Patients With Concussion and Other Causes of Dizziness: Examination of Balance Behaviour
ClinicalTrials.gov study NCT06893029. IPD Sharing: YES. Countries: 1. Publications: 11.
Investigating Brain Function in People With and Without Visual Snow Syndrome Using Adaptation to Visual Stimuli
ClinicalTrials.gov study NCT06961864. IPD Sharing: YES. Countries: 1. Publications: 3.
An alternative to molluscicides? Toward a push-pull strategy against invasive snails using chemical and visual stimuli
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Data from: Simple visual stimuli are sufficient to drive responses in action observation and execution neurons in the macaque ventral premotor cortex
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Acoustic and visual stimuli combined promote stronger responses to aerial predation in fish
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Data from: Adaptation to visual sparsity enhances responses to isolated stimuli
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Timbre and Visual Forms: a crossmodal study relating acoustic features and the Bouba-Kiki Effect - Auditory Stimuli
<p>Audio fragments from 'Timbre and Visual Forms: a crossmodal study relating acoustic features and the Bouba-Kiki Effect' study. 11 auditory stimuli. Length: 05 seconds. Audio Format: .wav (44.100/16Bits).</p>
GazeMining: A Dataset of Video and Interaction Recordings on Dynamic Web Pages. Labels of Visual Change, Segmentation of Videos into Stimulus Shots, and Discovery of Visual Stimuli.
<p><strong>Recording setup</strong><br> Recordings have been taken place on 12th March 2019. Gaze data has been recorded with a Tobii 4C eye tracker with Pro license at 90 Hz. Resolution of the viewport was set to 1024x768. The display had a size of 24 inches and a resolution of 1680x1050 pixels. We polled the DOM tree every 50 milliseconds for fixed elements. We recorded the Web browsing of four participants, who followed the protocol as stored under "Dataset_visual_change/Instructions.doc".</p> <p><strong>Description of the dataset</strong><br> The dataset consists of following three subsets.</p> <p><em>1. Dataset_visual_change</em><br> The recordings of each participant p1-p4 on twelve Web sites are in the corresponding directories. For each Web site, there are nine to eleven files:</p> <ul> <li><site>.json: datacast</li> <li><site>.webm: video recording</li> <li><site>.features.csv: computer-vision features per observation</li> <li><site>.features_meta.csv: meta information about features</li> <li><site>.labels-l<X>.csv: labels of observations</li> <li><site>_meta.csv: meta information about recording</li> <li><site>_scroll_cache.csv: cache of estimated scrolling</li> <li><site>_scroll_cache_map.csv: mapping of observations to scroll cache entries</li> <li><site>_times.csv: timestamps of frames in the video recording</li> <li><site>_layer_pixels.csv: first row is the pixel count of root layer, second row is pixel count of all fixed elements</li> </ul> <p><em>2. Dataset_stimuli</em><br> Stimulus shots and visual stimuli computed with the framework. Value-based, edge-based, signal-based, and SIFT-based features have been used. The labels of the first participant's session had been used to train a random forest classifier with 100 trees for visual change classification, using the named features. The discovery has been performed on each Web site from the dataset and<br> the results are placed in the respective directories. Inside each directory, there is one directory for the detected shots and one for the discovered stimuli. In the shots directory, there is one overview as <participant>_<site>.csv file. For each shot, there are four further files:</p> <ul> <li><participant>_<site>_<shot>.png: stitched frame of the stimulus shot</li> <li><participant>_<site>_<shot>-blind.csv: frames from animations that are not contributing to the stitched frame</li> <li><participant>_<site>_<shot>-gaze.csv: gaze data (in stitched frame space)</li> <li><participant>_<site>_<shot>-mouse.csv: mouse data (in stitched frame space)</li> </ul> <p>The shots have been merged to stimuli, which are placed in the stimuli directory. The stimuli are grouped per layer (scrollable, fixed elements, etc.) and meta information is available in <layer_index>-<xpath>-meta.csv files. Furthermore, there are directories per layer, storing the discovered stimuli. Each discovered visual stimulus is represented by four files:</p> <ul> <li><stimulus_id>.png: stitched frame of the visual stimulus</li> <li><stimulus_id>-gaze.csv: gaze data (in stitched frame space)</li> <li><stimulus_id>-mouse.csv: mouse data (in stitched frame space)</li> <li><stimulus_id>-shots.csv: contained stimulus shots</li> </ul> <p><em>3. Dataset_evaluation</em><br> We have performed two evaluations of the visual stimuli discovery. One computational estimating the quality of stimuli. One case-study of an expert's task. There are two respective directories with the annotation data.</p> <p><strong>Changelog</strong><br> [1.0.2] Add counts of layer pixels per participant.<br> [1.0.1] Change to CC0 license.<br> [1.0.1] Add labels of third annotator "l3".<br> [1.0.0] Initial release.</p>
Img2brain: Predicting the neural responses to visual stimuli of naturalistic scenes using machine learning
<p>The data for this project is part of the <a href="https://doi.org/10.1038/s41593-021-00962-x">Natural Scenes Dataset</a> (NSD), a massive dataset of 7T fMRI responses to images of natural scenes coming from the <a href="https://cocodataset.org/#home">COCO dataset</a>. The training dataset consists of brain responses measured at 10.000 brain locations (voxels) to 8857 images (in jpg format) for one subject. The 10.000 voxels are distributed around the visual pathway and may encode perceptual and semantic features in different proportions. The test dataset comprises 984 images (in jpg format), and the goal is to predict the brain responses to these images.</p> <p>The zip file contains the following folders:</p> <p>1. <strong>trainingIMG</strong>: contains the training images (8857) in jpg format. The numbering corresponds to the order of the rows in the brain response matrix.</p> <p>2. <strong>testIMG</strong>: contains test images (984) in jpg format.</p> <p>3. <strong>trainingfMRI</strong>: contains a npy file with the fMRI responses measured at 10000 brain locations (voxels) to the training images. The matrix has 8857 rows (one for each image) and 10000 columns (one for each voxel).</p>
Reduction of Visual and Auditory Stimuli to Reduce Pain During Venipuncture in Premature Infants.
ClinicalTrials.gov study NCT04041635. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study of the Effect of Proprioceptive Stimuli in the Stomatognathic Area on Visual Perception, With and Without Sound
ClinicalTrials.gov study NCT07051460. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Oral Vasopressin Modulates Neural Responses to Looming Visual Stimuli: An Eye-tracking Study
ClinicalTrials.gov study NCT06329063. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Losartan Modulates Neural Responses to Looming Visual Stimuli: An Eye-tracking Study
ClinicalTrials.gov study NCT06329076. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: The effect of static versus dynamic stimuli on visual processing of sexual cues in androphilic women and gynephilic men
Models of sexual response posit that attentional processing of sexual cues is requisite for sexual responding. Despite hypothesized similarities in the underlying processes resulting in sexual response, gender differences in sexual arousal patterns are abundant. One such gender difference relates to the stimulus features (e.g., gender cues, sexual activity cues) that elicit a response in men and women. In the current study, we examined how stimulus modality (static visual images versus dynamic audiovisual films) and stimulus features (gender, sexual activity, and nonsexual contextual cues) influences attentional (i.e., gaze) and elaborative (i.e., self-reported attraction, self-reported arousal) processing of sexual stimuli. Men's initial and controlled attention was consistently gender-specific (i.e., greater attention towards female targets), and this was not influenced by stimulus modality or the presence of sexual activity cues. In contrast, gender-specificity of women's attention patterns differed as a function of attentional stage, stimulus modality, and the features within the stimulus. Degree of specificity was positively predictive of self-reported attraction in both genders; however, it was not significantly predictive of self-reported arousal. These findings are discussed in the context of gendered processing of visual sexual information, including a discussion of implications for research designs.
Data from: Opposite distortions in interval timing perception for visual and auditory stimuli with temporal modulations
When an object is presented visually and moves or flickers, the perception of its duration tends to be overestimated. Such an overestimation is called time dilation. Perceived time can also be distorted when a stimulus is presented aurally as an auditory flutter, but the mechanisms and their relationship to visual processing remains unclear. In the present study, we measured interval timing perception while modulating the temporal characteristics of visual and auditory stimuli, and investigated whether the interval times of visually and aurally presented objects shared a common mechanism. In these experiments, participants compared the durations of flickering or fluttering stimuli to standard stimuli, which were presented continuously. Perceived durations for auditory flutters were underestimated, while perceived durations of visual flickers were overestimated. When auditory flutters and visual flickers were presented simultaneously, these distortion effects were cancelled out. When auditory flutters were presented with a constantly presented visual stimulus, the interval timing perception of the visual stimulus was affected by the auditory flutters. These results indicate that interval timing perception is governed by independent mechanisms for visual and auditory processing, and that there are some interactions between the two processing systems.
Effectiveness of Personal Relevance of Visual Autobiographical Stimuli in Positive Emotions Induction
ClinicalTrials.gov study NCT04251104. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Data from: The effect of static versus dynamic stimuli on visual processing of sexual cues in androphilic women and gynephilic men
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Data from: Neurons in primate visual cortex alternate between responses to multiple stimuli in their receptive field
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Data from: Opposite distortions in interval timing perception for visual and auditory stimuli with temporal modulations
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Impact of Visual Stimuli on Pediatric Oral Hygiene Performance
ClinicalTrials.gov study NCT07150429. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.