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92 results for “visual perception”
Dataset for the study Multisensory spatial perception in visually impaired infants
<p>Data from the study "Multisensory spatial perception in visually impaired infants". Data are in textual tab-delimited format.</p> <p> </p> <p>Summary</p> <p>Congenitally blind infants are not only deprived of visual input but also of visual influences on the intact senses. The important role that vision plays in the early development of multisensory spatial perception<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib1">1</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib2">2</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib3">3</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib4">4</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib5">5</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib6">6</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib7">7</a> (e.g., in crossmodal calibration<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib8">8</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib9">9</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib10">10</a> and in the formation of multisensory spatial representations of the body and the world<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib1"><sup>1</sup></a><sup>,</sup><a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib2"><sup>2</sup></a>) raises the possibility that impairments in spatial perception are at the heart of the wide range of difficulties that visually impaired infants show across spatial,<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib8">8</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib9">9</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib10">10</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib11">11</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib12">12</a> motor,<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib13">13</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib14">14</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib15">15</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib16">16</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib17">17</a> and social domains.<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib8"><sup>8</sup></a><sup>,</sup><a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib18"><sup>18</sup></a><sup>,</sup><a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib19"><sup>19</sup></a> But investigations of early development are needed to clarify how visually impaired infants’ spatial hearing and touch support their emerging ability to make sense of their body and the outside world. We compared sighted (S) and severely visually impaired (SVI) infants’ responses to auditory and tactile stimuli presented on their hands. No statistically reliable differences in the direction or latency of responses to <a href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/auditory-stimulation">auditory stimuli</a> emerged, but significant group differences emerged in responses to tactile and audiotactile stimuli. The visually impaired infants showed attenuated audiotactile spatial integration and interference, weighted more tactile than auditory cues when the two were presented in conflict, and showed a more limited influence of representations of the external layout of the body on tactile spatial perception.<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib20"><sup>20</sup></a> These findings uncover a distinct phenotype of multisensory spatial perception in early postnatal visual deprivation. Importantly, evidence of audiotactile spatial integration in visually impaired infants, albeit to a lesser degree than in sighted infants, signals the potential of multisensory rehabilitation methods in early development.</p> <p>Orienting responses and reaction times (RT) are reported, based on the scoring of two independent naive raters, for each trial of each subject, group (SVI/S), posture (Uncrossed/Crossed), and sensory condition (Tactile only, Auditory only, Audiotactile congruent, Audiotactile incongruent).</p> <p>Trial is the trial number, condition is the sensory condition, audio and tactile respectively refer to the side of the stimulated hand, response_status reports if the response is defined or undefined, response modality reports if the modality used by subjects to respond/not to respond to stimuli (hand, eye, both hands, no motion), group is if the subject was a sighted (S) or a severely visually impaired (SVI) infant, age_mounth is the age expressed in months, RT_rater1, RT_rater 2 and RT are respectively the RT assigned by the two raters and the merge of the two estimations (for RTs, the mean), the same organization for response_side, and for response_modality (for those variables, when the estimation of the two raters did not agree, the merged classification was set to unknown, that is uncertain/undefined).</p>
Visual and auditory brain areas share a representational structure that supports emotion perception: fMRI data
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Dataset supplementing the article Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129.
<p>This dataset supplements the publication<br> Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129. doi: 10.1016/j.visres.2017.02.001</p> <p>Use is free for scientific purposes, provided the aforementioned reference is appropriately cited.<br> Description of files<br> - conditionsByObserver.csv<br> contains for each of the 16 observers the color and grating direction that had been coupled to either the low-pitch or the high-pitch tone<br> column 1: observer number<br> column 2: color associated with low-pitch tone<br> column 3: color associated with high-pitch tone<br> column 4: drift direction associated with low-pitch tone<br> column 5: drift direction associated with high-pitch tone</p> <p>- conditionsByObserver.mat contains the same information as matlab variables (as four vectors/cell arrays with one entry per observer)</p> <p>- toneByBlockAndTrial.csv<br> contains the conditions for all 18 rivalry trials (6 rivalry blocks with 3 trials each) for each observer<br> column 1: observer number<br> column 2: block number<br> column 3: trial number<br> column 4: tone (low [pitch], high [pitch], none) played in this trial<br> Note that due to a technical error for observer #16, block 6 was presented first, followed by 1,2,3,4,5; for all other observers blocks were used in the order given (1,2,3,4,5,6).</p> <p>- toneByBlockAndTrial.mat contains the same information as a 16x6x3 matrix named toneByBlockAndTrial ; tones are coded numerically (1-low pitch,2-high pitch,3-none)</p> <p>- eyeTraces.mat contains three cell arrays of dimensions 16x6x3 (observer x rivalry block x rivalry trial) called xEye, oknGain, and timeSinceTrialStart;</p> <p>o each entry of xEye contains the horizontal eye position for<br> the respective trial in eye-tracker coordinates (which correspond to screen pixels, except that (1/1) is the upper right rather than the upper left and values increase from right to left due to the setup configuration)</p> <p>o oknGain contains the gain computed from these eye positions.</p> <p>o timeSinceTrialStart contains the time in seconds since onset of the trial</p> <p><br> For all variables, the sampling rate is 500 Hz, in eye-tracker coordinates the speed of the grating is 240 units/ms. Blinks were removed from both eye-data variables, fast-phases were removed from the gain data. Removed data were set to NaN in eye-data variables.</p> <p>- Matlab functions figure1d.m, figure 2.m, figure3.m and figure4.m compute raw versions of the aforementioned paper's figures from the datafiles to exemplify their usage.</p> <p>[Note: In the originally published version of the article, the first two means and their standard errors of section 3.3 were stated incorrectly. All figures and statistical analyses are based on the correct data].</p>
Dataset: Feedback contribution to surface motion perception in the human early visual cortex
<p><strong>Dataset</strong></p> <p>Dataset accompanying the manuscript "Feedback contribution to surface motion perception in the human early visual cortex" (<a href="https://doi.org/10.1101/653626">biorxiv</a>).</p> <p><strong>Description</strong></p> <p>fMRI data are arrange by subject (following BIDS convention). For each subject, there are subfolders for anatomical and functional MRI data.</p> <p>├── sub-01<br> │ ├── anat<br> │ │ └── ...<br> │ ├── func<br> │ │ └── ...<br> │ ├── func_se<br> │ │ └── ...<br> │ └── func_se_op<br> │ └── ...</p> <p>The subfolder 'anat' contains four images from the MP2RAGE sequence (among these, T1 and proton-density weighted images). The subfolder 'func' contains the functional data (GE EPI, T2* weighted) from the main experiment (i.e. the data from which the haemodynamic response was estimated, and on which statistical analysis was performed). The subfolders 'func_se' and 'func_se_op' contain SE EPI images with opposite phase encode polarity that were used for distortion correction. Moreover, for each image/timeseries there is a json file with metadata.</p> <p>Anatomical images have been masked anteriorly (defaced). Functional images are in coronal oblique orientation, covering early visual cortex.</p> <p>The folder 'stimuli' contains information on the stimuli used for retinotopic mapping, including timecourse models used for population receptive field mapping. (These files are included here because of their relatively large file size, which would make distribution via a git repository impractical.) The software used for the presentation of retinotopic mapping stimuli (and for the corresponding analysis) is available on <a href="https://github.com/ingo-m/pyprf">github</a>.</p> <p>For example videos of the main experimental stimuli, see <a href="https://doi.org/10.5281/zenodo.2583017">zenodo.2583017</a>. If you would like to reproduce the experimental stimuli, the respective PsychoPy code can be found on <a href="https://github.com/ingo-m/PacMan/tree/master/stimuli/experiment">github</a>.</p> <p>The exact timing of events during the experiments (rest & stimulus blocks, target events) can be found in FSL-style design matrices ("3 column format") on <a href="https://github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata">github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata</a>.</p> <p><strong>Analysis</strong></p> <p>The analysis pipeline makes use of several MRI software packages (such as SPM and FSL for preprocessing, and CBS tools for cortical depth sampling). In order to facilitate reproducibility, the entire analysis was containerised using docker. Because of licensing issues, the docker images with the third-party software cannot be directly made available. However, the docker files and detailed instructions for the creation of the docker images are available on <a href="https://github.com/ingo-m/PacMan/tree/master/docker">github</a>.</p> <p>If you would like to reproduce the analysis, the first step will be to create the docker images (which provide an exact copy of the system environment that was used to conduct the published analysis). There are two docker images, one for the main analysis (motion correction, distortion correction, GLM fitting; named "dockerimage_pacman_jessie"), and another one for the depth sampling (named "dockerimage_cbs"). Detailed instructions on how to create the docker images can be found <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_PacMan_Image_Jessie.txt">here</a> and <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_CBS_Image.txt">here</a>.</p> <p>Once you set up the docker images, the analysis can be run automatically. For each subject, there is one parent script for the main analysis (e.g. <a href="http://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_01.sh">~/analysis/20180118/metascript_01.sh</a> for subject 20180118) and a separate script for the depth sampling (e.g. <a href="https://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_03.sh">~/analysis/20180118/metascript_03.sh</a>). The only manual adjustments you should have to perform to reproduce the analysis is to change the file paths in the first section of these scripts ('pacman_anly_path' is the parent directory containing the analysis code, i.e. the git repository, and 'pacman_data_path' is the parent directory containing the MRI data). The main analysis (metascript_01.sh) should take about 24 h per subject on a workstation with 12 cores, and the depth sampling (metascript_02.sh) about 2 h. The analysis can be run on consumer-grade hardware, but some parts of the analysis may not run with less than 16 GB of RAM (recommended: 32 GB).</p> <p>Visualisations (e.g. cortical depth profiles and signal timecourses) and group-level statistical tests are implemented in <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">py_depthsampling</a>.</p> <p><strong>Further resources</strong></p> <p>Please refer to the research paper for more details: <a href="https://doi.org/10.1101/653626">https://doi.org/10.1101/653626</a></p> <p>The analysis pipeline can be found on <a href="https://github.com/ingo-m/PacMan">https://github.com/ingo-m/PacMan</a></p> <p>A separate repository contains the code used for visualisation of depth-sampling results: <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">https://github.com/ingo-m/py_depthsampling/tree/PacMan</a></p> <p>Free & open source software package for population receptive field mapping: <a href="https://github.com/ingo-m/pyprf">https://github.com/ingo-m/pyprf</a></p> <p> </p>
Visualization and perception of data gaps in the context of Citizen Science projects: Gradation of Reporting Activity
<p>Online experiment about the influence of different numbers of levels of representation of reporting activity (total number of reports for all birds in the given time span and region) on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Effects of representation with three (3) levels and effects of representation with five (5) levels are investigated. Two groups of members of ornitho.de were tested: experts - persons with access to database (more than 10 reports per month in average) and novices - persons without access to database (less than 10 reports per month in average). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.</p>
Visualization and perception of data gaps in the context of Citizen Science projects: Video tutorial support
<p>Online experiment about the influence of the availability of a video tutorial on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.</p>
Dataset: Information content of ultraviolet-reflecting color patches and visual perception of body coloration in the Tyrrhenian wall lizard Podarcis tiliguerta
<p>These are the data sets and R script corresponding to the scientific publication with the same title and authors.</p> <p>Description of these files is available in the file Note.pdf</p>
The Perception of "Intelligent" Design in Visual Structure
<p>Datasets relative to the following publication:</p> <p>Schmidt, F. (2022). The Perception of “Intelligent” Design in Visual Structure. <em>i-Perception, 13(2)</em>, 1-5. https://doi.org/10.1177/20416695221080184</p> <p>Each excel file contains the data relative to (1) the label task and (2) the two ranking tasks, respectively:</p> <p>(1) LabelData.xlsx contains the average frequencies of verbal labels per category across 3 raters, based on accumululation of participants' free naming responses (for experimental details see publication).</p> <p>(2) RankingData.xlsx contains the individual rankings of all experimental stimuli on the continuum from "animate origin" to "inanimate origin", as well as on the continuum from "Straight lines and right angles" to "No straight lines and no right angles" (for experimental details see publication). </p>
Visual Perception of Shape-Transforming Processes: 'Shape Scission'
<p>Dataset relative to the following publication:</p> <p>Schmidt, F., Phillips, F., & Fleming, R. W. (in press). Visual Perception of Shape-Transforming Processes: ‘Shape Scission’. <em>Cognition, 189</em>, 167-180. https://doi.org/10.1016/j.cognition.2019.04.006</p> <p>Each experiment folder contains the data relative to one experiment and a text file with comments. The stimuli folder contains image files of the experimental stimuli.</p>
Photogrammetry-based model of the Copacabana for auralization and audio-visual perception studies in virtual reality
<p>This dataset contains photogrammetry-based audio-visual models from Copacabana. They are used for urban sound auralization as well as for audio-visual perception studies in virtual reality.</p> <p>Photogrammetry model available in the following formats: 3ds, dae, dxf, fbx, obj, stl</p> <p>Simplified CAD model for acoustic simulations available in format: dae</p>
A Serious Game to Anticipate Handwriting Difficulties Screening Through Visual Perception Assessment - DATASET
<p>Each row in the dataset represents a subject. It contains:</p> <ul> <li>The answers to a characterization questionnaire</li> <li>The performance in the game described in the article</li> </ul>
Mechanisms of individualized fMRI neuromodulation for visual perception and visual imagery
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Visual Perception of Complex Shape-Transforming Processes
<p>Dataset relative to the following publication:</p> <p>Schmidt, F., & Fleming, R. W. (2016). Visual Perception of Complex Shape-Transforming Processes. <em>Cognitive Psychology, 90</em>, 48-70. <a href="http://dx.doi.org/10.1016/j.cogpsych.2016.08.002"> http://dx.doi.org/10.1016/j.cogpsych.2016.08.002</a></p> <p>Each folder contains the stimuli and data relative to one experiment and a text file with comments.</p>
Visual perception of shape altered by inferred causal history
<p>Dataset and stimuli relative to the following publication:</p> <p>Spröte, P., Schmidt, F., & Fleming, R. W. (2016). Visual perception of shape altered by inferred causal history. <em>Scientific Reports, 6</em>, 36245. <a href="http://dx.doi.org/10.1038/srep36245"> http://dx.doi.org/10.1038/srep36245</a></p> <p>Each folder contains the data and stimuli relative to one experiment and a text file with comments.</p> <p> </p>
Visual features in the perception of liquids
<p>Dataset relative to the following publication:</p> <p>van Assen, J.J.R., Pascal Barla & Fleming, R. W. (2018). Visual features in the perception of liquids. <em>Current Biology.</em></p> <p>One zip file contains the datasets of the various experiments and code to run the analysis.</p> <p>The second zip file contains the liquid stimuli used in this study, note: this is a large file (~12Gb).</p>
Studying the Effects of Natural Visual Scene Changes on Typical Adult Visual Perception
ClinicalTrials.gov study NCT05004649. IPD Sharing: Not stated. Countries: 1. Publications: 13.
Dynamic visual effects enhance flower conspicuousness but compromise colour perception
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Environmental impacts on visual perception modulate behavioral responses of schooling fish to looming predators
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Alpha-band oscillations and visual temporal resolution: An expansion and partial replication of Samaha & Postle's 2015 study: "The Speed of Alpha-Band Oscillations Predicts the Temporal Resolution of Visual Perception"
<p>Data for the study "Alpha-band oscillations and visual temporal resolution: An expansion and partial replication of Samaha & Postle’s 2015 study: “The Speed of Alpha-Band Oscillations Predicts the Temporal Resolution of Visual Perception”"</p> <p>Contents consist of curated EEG data for statistical analysis, statistical analysis workflow and code and Matlab code for the behavioural flash fusion task.</p>
Data from: Visual attention and perception of road safety advertisements with different level of explicitness in Peruvian millennials and centennials
<p>In the face of the massiveness of content, social advertisements must explore the potential of other dimensions to improve their designs. This paper examines the effect of different levels of graphic explicitness on the attention and perception of road safety ads. An eye-tracking experiment was conducted to assess the visual attention of 60 young millennials and centennials on three ads with images of different levels of explicitness. Participants were agrouped into two groups where they watched two road safety ads with negative valence, but with different levels of explicitness in the second ad seen. For the image and text of each ad, metrics of number of fixations, time to first fixation and total fixation time spent were compared. A survey provided qualitative information about the perception and effectiveness of the ads. Significant effects were found when using negative emotional appeals through explicitness to enhance visual attention and audience perception. But, although the image has prominence, textual design should not be neglected to focus the emotional appeal of the ad.</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.