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28 results for “Natural scenes”
Data set to article "Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink"
<p>The file Data_Marxetal2014_AB.csv contains the data to the paper<br> Marx, S., Hansen-Goos, O., Thrun, M., & Einhäuser, W. (2014). Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink. Journal of Vision, 14(14):4, 1-18, http://www.journalofvision.org/content/14/14/4, doi:10.1167/14.14.4.<br> as comma-separated value (csv) file</p> <p>Each row contains the data of one trial, represented by the following columns</p> <p>1 - number of the line<br> 2 - subject ID<br> 3 - experiment number<br> 4 - color condition (1: gray inverted, 2: gray original, 3: color inverted, 4: color original)<br> 5 - number of targets<br> 6 - SOA in ms<br> 7 - serial position of first target (0 if absent)<br> 8 - serial position of second target (0 if absent)<br> 9 - category of first target (1: feline, 2: avian, 3: ungulate, 4: canine)<br> 10 - category of second target (1: feline, 2: avian, 3: ungulate, 4: canine)<br> 11 - response to "How many animals?" (detection)<br> 12 - response to first category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine)<br> 13 - response to second category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine)</p>
Data to "Retinal Blur from Natural Scenes and Eye Shape"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p><strong>Maiello, G</strong>., Harrison, W. J., Vera-Diaz, F. A., & Bex, P. J. (in preparation). Retinal Blur from Natural Scenes and Eye Shape.</p>
Data supplementing the article "Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13."
<p>These data supplement the article Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13.</p> <p>The data can be used freely for academic purposes, provided the aforementioned reference is appropriately cited.</p> <p>The following files are available for experiment 2 of the article:</p> <p>allData.mat</p> <p>Includes the datamatrix allData with the following columns:</p> <p>1) Line used for analysis in the article (0 - no, 1-yes).<br> Possible reasons for exclusion:<br> a. fixation duration smaller than 50ms or larger 1000ms<br> b. fixation adjacent to a blink (preceding or following)<br> c. fixation outside the image</p> <p>2) ID of observer (1-24)</p> <p>3) ID of condition (1:grayscale, 2: reduced luminance, 3: reduced contrast, 4: equalized luminance, 5: equalized contrast, 6: phasenoise)</p> <p>4) ID of image (48 unique numbers between 1 and 135)</p> <p>5) horizontal eye position</p> <p>6) vertical eye position</p> <p>7) fixation duration in ms</p> <p>8) value of empirical map generated from search condition of experiment 1 at fixated location</p> <p>9) value of empirical map generated from preference condition of experiment 1 at fixated location</p> <p>10) value of empirical map generated from memorization condition of experiment 1 at fixated location</p> <p>11) value of empirical map generated from joining memorization and preference condition of experiment 1 at fixated location</p> <p>12) value of empirical map generated from condition 1 at fixated location</p> <p>13) value of empirical map generated from condition 2 at fixated location</p> <p>14) value of empirical map generated from condition 3 at fixated location</p> <p>15) value of empirical map generated from condition 4 at fixated location</p> <p>16) value of empirical map generated from condition 5 at fixated location</p> <p>17) value of empirical map generated from condition 6 at fixated location</p> <p>18) value of empirical map generated from condition 1 at fixated location leaving out the current observer</p> <p>19) value of empirical map generated from condition 2 at fixated location leaving out the current observer</p> <p>20) value of empirical map generated from condition 3 at fixated location leaving out the current observer</p> <p>21) value of empirical map generated from condition 4 at fixated location leaving out the current observer</p> <p>22) value of empirical map generated from condition 5 at fixated location leaving out the current observer</p> <p>23) value of empirical map generated from condition 6 at fixated location leaving out the current observer</p> <p>24) luminance at fixation</p> <p>25) luminance contrast at fixation</p> <p>26) edge density at fixation</p> <p>27) eccentricity of fixation</p> <p> </p> <p>usedData.Rdata</p> <p>- for all lines that are used for analysis (allData(:,1)==1) a field in an R dataframe is created, which contains the following fields (for details, see description of matlab file above):</p> <p>obsNum: the ID of the observer (1-24)</p> <p>condNum: the ID of the condition (1-6)</p> <p>imgNum: the ID of the image (48 unique numbers between 1 and 135)</p> <p>fixDur: fixation duration</p> <p>LUM, LCG, ED, ECC: luminance, contrast, edge density and eccentricity at fixation</p> <p>empMapFromSearch, empMapFromPref, empMapFromMem, empMapFromJoint: values of empirical maps generated from data of experiment 1 (search, preference, memorization task as well as combination of the latter two) at fixation</p> <p>empMapFromC1 through empMapFromC6: value of empirical map generated from condition 1 through 6 at fixated location</p> <p>empMapFromC1loo through empMapFromC6loo - value of empirical map generated from condition 1 through 6 at fixated location leaving out the current observer</p> <p>x,y - coordinates of fixation</p> <p> </p> <p>modelsFigure7.R - computes all models for figure 7 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)</p> <p>modelsFigure8.R - computes all models for figure 8 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)</p> <p> </p>
Data supplementing the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.
<p>These data supplement the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.</p> <p>Use is free for academic purposes, provided the aforementioned article is appropriately cited.</p> <p>The directory contains the following files</p> <p>stimuli.tar.gz - stimuli used in this study; note that this is based on the MONS database, but some deviations from the final version of the database do exist.</p> <p>ratings.mat contains the variables<br> arousal - mean arousal rating<br> valence - mean valence rating<br> valence2 - squared mean valence rating (after subtracting midpoint)<br> motivationalValue - mean motivation rating<br> motivaionalValue2 - squared mean motivation rating (after subtracting midpoint)</p> <p>All variables are 104x3, where the first dimension is the stimulus number, and the second dimension the motivation ground truth (aversive, neutral, appetitive)</p> <p><br> Experiment 1</p> <p>fixationsExperiment1.mat contains the variables fixationX, fixationY, fixationDuration, fixaitonOnset, fixationInitial, which contain for each fixation horizontal and vertical coordinate, the duration, the time of the onset relative to the trial onset and whether it is the initial fixation. All variables have dimensions 16x104x3x50, where the first dimension is the observer, the second the scene, the third the condition and the forth a counter of fixations. Whenever there are less than 50 fixations the remainder are filled with NaN.</p> <p><br> boundingBoxesExperiment1.mat contains for each critical object the bounding box coordinates x,y of upper left corner and width and height as variables boundingBoxX, boundingBoxY, boundingBoxW, boundingBoxH respectively. Note that this is relative to the eyetracker coordinates of experiment 1 (full display 1024x768, presentation in the center) and will therefore not match the coordinates of the images in the archive or the bounding box coordinates of experiment 2. Dimensions are 104x3, the dimensions representing scene number and condition, respectively.</p> <p><br> figure2.m uses these data to computes figure 2 of the article from these data</p> <p><br> dataForExperiment1.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of table 1. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object.</p> <p><br> table1.R computes and prints the models for table 1</p> <p> </p> <p>Experiment 2</p> <p>fixationsExperiment2.mat contains fixation data for experiment 2. Variable names as in experiment 1. Dimensions are 18x99x3x3x50, where the first dimension is the observer, the second the image number, the third the visual condition, the third the motivational condition and the fifth the fixation count. Since only one visual condition was shown to each observer per motivational condition, there is an additional variable 'hasData', which is 1 if the image was presented to the observer in this condition and 0 otherwise. Since fixations can be outside the image and will therefore be excluded, there is also an additional variable fixationNumber to keep a correct count of the fixation number in the trial.</p> <p>boundingBoxesExperiment2.mat contains bounding box data for experiment 2 in image (and fixation) coordinates. Notation as for experiment 1, but coordinates refer to image and eyetracking coordinates used for experiment 2 and therefore can differ occasionally.</p> <p><br> figure3and4.m generates figures 3 and 4 of the article from these data files.</p> <p>dataForExperiment2.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of tables 2 amd 3. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. The fields imgMot and imgVis contain the motivational ground truth and the salience manipulation, respectively.</p> <p>table2.R uses the Rdata file to compute the models for table 2 of the article and print summary results</p> <p>table3.R uses the Rdata file to compute the models for table 3 of the article and print summary results. Note that the computation can take substantial time; results might deviate slightly depending on the exact version of R and its libraries used.</p> <p> </p>
Dataset supplementing Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006
<p>These data supplement the publication</p> <p>Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006</p> <p>and be used freely for scientific purposes provided the aforementioned paper is appropriately cited.</p> <p>Note that the image files cannot be provided on this site due to copyright restrictions.</p> <p>The dataset contains the following files:</p> <p>maps_01.mat - maps_72.mat:</p> <p>For each image the 6 maps used in the paper are contained, the maps of experiment 1 are labelled as in the paper (AWS, OOM, nOOM, PVL,UNI), AWS2 is the AWS map for the modified stimuli of experiments 2 and 3.</p> <p>exp?_fixations.mat contains all fixations of the respective experiment.</p> <p>For experiment 1, there are the variables xFix, yFix, durFix, which contain the x position, the y condition, and the fixation duration of each fixation. Dimensions are images x subjects x fixation number, where the first fixation is the 0th (initial) fixation. The variable condition (image x subject) contains the condition in which the respective image was shown to the subject. For the main analysis only the "0" condition was used, refer to the paper's appendix for the other conditions.</p> <p>For experiment 2 and 3, variables are called xFixByImage, yFixByImage, dFixByImage and the dimensions are subject x image x fixation number. In addition tFixByImage contains the start of the fixation relative to trial onset (negative for the 0th fixation).<br> In both cases, empty entries are filled with nans.</p> <p><br> computeROC.m is a helper function called by other functions.</p> <p><br> figure1.m through figure7.m reproduce the figures from the paper to exemplify data usage.</p> <p> </p>
Reconstruction of Natural Visual Scenes from Primary Visual Cortical Neural Activity Using Adversarial Learning
<p>This data set and code goes with "Reconstruction of Natural Visual Scenes from Primary Visual Cortical Neural Activity Using Adversarial Learning"</p>
Efficient coding of natural scenes improves neural system identification
<p>Dataset for <a href="https://www.biorxiv.org/content/10.1101/2022.01.10.475663v3">Qiu et al., 2022</a>.</p> <blockquote> <p>This work was supported by the German Research Foundation (DFG; SFB 1233, Robust Vision: Inference Principles and Neural Mechanisms, projects 10 and 12, project number 276693517; GRK2381, project number 335549539), the Germany’s Excellence Strategy (EXC 2064/1, project number 390727645), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (agreement No 674901); the Max Planck Society (M.FE.A.KYBE0004); the German Ministry of Education and Research (BMBF; FKZ: 01GQ1002), and the Tübingen AI Center (FKZ: 01IS18039A). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p> </blockquote>
"I was the class teacher at that time. It was a class trip, usually organized near the end of the schoolterm in summer. The pupils went there by bike to have a barbecue at the sandy banks of the river Rhine near Dusseldorf. The landscape around is mostly dominated by agriculture and glasshouse cultures. You find a mixture of former villages nowadays completely suburbanized. The population finds jobs in the nearby urban centers like Dusseldorf, Neuss and other big cities. The reason why Irecorded the scene is simply because Iam interested in collecting sounds in general by doing recordings in different surroundings like nature, cities and everything between. My memories about the event are that it was a relaxing and funny atmosphere, which is not always the case while teaching in a classroom" [Reinhard/reinsamba]15 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I was the class teacher at that time. It was a class trip, usually organized near the end of the schoolterm in summer. The pupils went there by bike to have a barbecue at the sandy banks of the river Rhine near Dusseldorf. The landscape around is mostly dominated by agriculture and glasshouse cultures. You find a mixture of former villages nowadays completely suburbanized. The population finds jobs in the nearby urban centers like Dusseldorf, Neuss and other big cities. The reason why Irecorded the scene is simply because Iam interested in collecting sounds in general by doing recordings in different surroundings like nature, cities and everything between. My memories about the event are that it was a relaxing and funny atmosphere, which is not always the case while teaching in a classroom" [Reinhard/reinsamba]15
Motivational Objects in Natural Scenes (MONS): A database of >800 objects
<p>Dataset for the publication: Schomaker, J., Rau, E.M., Einhäuser, W., & Wittmann, B.C. (2017) Motivational Objects in Natural Scenes (MONS): A database of >800 objects. Front. Psychol. 8:1669. doi: 10.3389/fpsyg.2017.01669</p>
Neural pathways and computations that achieve stable contrast processing tuned to natural scenes
<h1>Gur et al. 2024 database</h1> <p>Source data of the paper Gür et al. 2024, “Neural pathways and computations that achieve stable contrast processing tuned to natural scenes”, Nature Communications. This work contains an analysis of post-receptor luminance gain in the Drosophila visual system, focusing on the circuitry and algorithms for implementation of rapid luminance gain control.</p> <p>All data can be analyzed using the code provided in the Github repository: <a href="https://github.com/silieslab/Gur-etal-2024">https://github.com/silieslab/Gur-etal-2024</a>. Please go to the “Readme” file in the repository for how to use the code.</p> <h2>Raw data</h2> <p>All raw data is located in the folder “raw_data”. "Readme" file located in the code repository will guide you on how to analyze all data.</p> <h2>Processed data</h2> <p>All processed data is located in the folder “processed_data”. "Readme" file located in the code repository will guide you on how to analyze all data.</p> <p>- 2p_imaging_python_pickle: Processed data stored as .pickle files. <br>- Dm12_Figure7_Mat_files: Processed data for Dm12 imaging and optogenetics experiments presented in Figure 7 stored as .mat files.<br>- EM_data: Processed data for EM analysis done in Figure 7.<br>- Figure 6 Tm9 flpSTOP: tdTomato expression data for Figure 6 Tm9 flpSTOP experiments.<br>- Figure S5 Tm1 flpSTOP: tdTomato expression data for FigureS5 Tm1 flpSTOP experiments.</p>
Data from: Peacock spiders prefer image statistics of average natural scenes over those of male ornamentation
<p><span>The origins of preferences that drive the evolution of arbitrary sexual signals have been hotly debated for over 150 years. An emerging but little-tested theory, efficient coding theory, proposes that male visual courtship displays are adapted to pre-existing processing biases shaped by the statistical properties of the natural environment. Natural scenes show strong spatial correlations with average amplitudes of spatial frequencies falling with an average spectral slope of -1, and humans have been shown to prefer </span><span>random amplitude spectrum images that possess similar slopes.</span><span> It has been proposed that other animals may also prefer the statistics of their natural environment and that this preference drives the evolution of sexual signaling displays</span><span>. Here, we measure the spectral slope of the male display pattern of the Australian peacock jumping spider <em>Maratus</em> <em>spicatus</em> and test for a general preference towards that slope. We present spiders (male, female and juvenile) with random images of the male slope of -1.7 compared to a) a shallower slope of -1.0 and b) a steeper slope of -2.3. Spiders spent more time oriented towards the shallower slope than towards the male slope and spent the same amount of time oriented towards the male slope and the steeper slope. Our results indicate that spiders, like humans, prefer the average natural slope of -1, suggesting that this is likely the slope typically found in their natural habitat. Rather than exploiting a potential processing bias, it seems that males have evolved slopes that contrast with the visual background to enhance conspicuousness.</span></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.
Data from: Peacock spiders prefer image statistics of average natural scenes over those of male ornamentation
Open the record for dataset details and reuse information.
Data from: A comparison of image statistics of peacock jumping spider colour patterns and natural scenes
Open the record for dataset details and reuse information.
Data from: Measured perceptual nonlinearities show how ON-OFF asymmetric processing improves motion estimation in natural scenes
Animals detect motion using a variety of visual cues that reflect regularities in the natural world. Experiments in animals across phyla have shown that motion percepts incorporate both pairwise and triplet spatiotemporal correlations that could theoretically benefit motion computation. However, it remains unclear how visual systems assemble these cues to build accurate motion estimates. Here we use comprehensive measurements of fruit fly motion perception to show how flies combine local pairwise and triplet correlations to reduce variability in motion estimates across natural scenes. By generating synthetic images with statistics controlled by maximum entropy distributions, we showed that the observed improvement occurs only when light-dark asymmetries mimic natural ones. Thus, fly behavior suggests that asymmetric ON-OFF processing is tuned to the particular statistics of natural scenes. Since all animals encounter the world's light-dark asymmetries, many visual systems are likely to use asymmetric ON-OFF processing to improve motion estimation.
Data from: Zebrafish differentially process colour across visual space to match natural scenes
Animal eyes have evolved to process behaviorally important visual information, but how retinas deal with statistical asymmetries in visual space remains poorly understood. Using hyperspectral imaging in the field, in vivo 2-photon imaging of retinal neurons, and anatomy, here we show that larval zebrafish use a highly anisotropic retina to asymmetrically survey their natural visual world. First, different neurons dominate different parts of the eye and are linked to a systematic shift in inner retinal function: above the animal, there is little color in nature, and retinal circuits are largely achromatic. Conversely, the lower visual field and horizon are color rich and are predominately surveyed by chromatic and color-opponent circuits that are spectrally matched to the dominant chromatic axes in nature. Second, in the horizontal and lower visual field, bipolar cell terminals encoding achromatic and color-opponent visual features are systematically arranged into distinct layers of the inner retina. Third, above the frontal horizon, a high-gain UV system piggybacks onto retinal circuits, likely to support prey capture.
simulation scenes of natural disasters and highway constructions in Unreal Engine 4
<p>This is a zip file which contains two simulation scenes built in UE4. One shows natural disasters' impacts on surrounding objects. Another shows highway constructions' impacts on surrounding objects.</p>
Code and data associated with the paper 'Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes'
<p>Code and data associated with the paper 'Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes'.</p> <p>See Readme.pdf for data and code details.</p>
A Tale of Two Retinal Domains: Near-Optimal Sampling of Achromatic Contrasts in Natural Scenes through Asymmetric Photoreceptor Distribution
<p>Spectral image data of an early spring daytime forest scene recorded using a ‘‘hyperspectral scanner’’. For details, see<a href="https://www.ncbi.nlm.nih.gov/pubmed/24314730"> Baden, Schubert et al. (2013)</a> doi: 10.1016/j.neuron.2013.09.030. For a script to access the data, see <a href="https://github.com/eulerlab/published_data">github.com/eulerlab/published_data</a>.</p>
Nonlinear spatial integration allows the retina to detect the sign of defocus in natural scenes
<p>The dataset comprises three types of data. First, multi-electrode array recordings of mouse retinal ganglion cells performed by Awen Louboutin and Tom Quetu. Second, point spread functions from mouse and human optical eye models, and associated convolved natural images. Third, results from a convolutional neural network model trainings.</p>
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.