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72 results for “visual attention”
Attention-based frontal-posterior coupling for visual consciousness in the human brain
<ol> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.m</li> <li>DataCode_Fig1_Attentional_Capture_Image_Detectability.mat</li> <li>DataCode_FigS2_Attentional_Capture_Image_Detecability.mat <ul> <li>.m Code (1) using .mat Data (2 and 3) illustrate main behavioral findings in our manuscript. Panel figures shown in Figure.1 and Figure.S2 could be well replicated using these materials.<br><br></li> </ul> </li> <li>Au_Step06_0601_unit_2C.m</li> <li>Au_Step06_0601_unit_mC.m</li> <li>Train_DSVM_xilei.m</li> <li>Classify_DSVM.m</li> <li>svmclassify.m</li> <li>svmtrain_xilei.m <ul> <li>.m Code (4) and .m code (5) using child .m functions (6, 7, 8 and 9) illustrate core codes used to discriminate neural pattern differences on a 2-class issue (image presence versus image absence) or a 3-class issue (animal, object or face), respectively. </li> </ul> </li> <li>Note_Location_activeChannels_distanceTest.m</li> <li>Note_Location_activeChannels_distanceTest.mat</li> <li>Note_Location_activeChannels.mat <ul> <li>.m Code (10) using .mat Data (11 and 12) illustrate our method used to calculate distance between responsive contacts. Based on that, we also made a statistical inference against a chance-level distribution. Panel figure shown in Figure.2F could be well replicated using these materials.<br><br></li> </ul> </li> <li>easy_ImgC.m <ul> <li>.m Code (13) illustrate our method used to calculate imaginary coherence between responsive contacts. A Rayleigh Z correction was also performed and outputed.<br><br></li> </ul> </li> <li>easy_visibility.m <ul> <li>.m Code (14) illustrate our method used to calculate an index of visibility from which measures of interest tied to an invisible image was subtracted from that of a visible image. </li> </ul> </li> </ol> <p> </p>
Dataset from: "Reward expectation facilitates context learning and attentional guidance in visual search"
<p>Dataset for Bergmann N, Koch D, Schubö A (2019). Reward expectation facilitates context learning and attentional guidance in visual search, <em>Journal of Vision</em>, 19(3). <a href="https://doi.org/10.1167/19.3.10">https://doi.org/10.1167/19.3.10</a></p>
Temporal Distortion for Angry Faces: Testing Visual Attention and Action Preparation Accounts
<p>Datasets for Experiment 1 and Experiment 2 of "Temporal Distortion for Angry Faces: Testing Visual Attention and Action Preparation Accounts"</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>
Basic visual functions of children and adolescents with Autism, Attention Deficit Hyperactivity Disorder, and Dyslexia
<p>Data for the manuscript Basic visual functions of children and adolescents with Autism, Attention Deficit Hyperactivity Disorder, and Dyslexia</p> <div> </div>
Mapping User Attention: Filtering and Visualizing Relevant UI Components in Screenshots based on Gaze Fixations
<p>These data correspond to the set of problems used for the evaluation of the proposal <em>What Are You Gazing At? An Approach to Use Eye-tracking for Robotic Process Automation.</em></p> <p>Each problem consists of a set of 10 screenshots with the same <em>look and feel</em> but different data values for those values that can be entered/modify by the user. Each problem has its associated gaze fixation data. In each of the problems there is a <em>key UI element</em> that primarily attracts the attention of the user.</p> <p>The evaluation is based on a set of images which resemble realistic screenshots of activities in the administrative domain. More precisely, 5 different set of screenshots (<em>S</em>) are generated, each of them with a different level of complexity. Complexity is measured in terms of the number of UI elements per screenshot. The sets are:</p> <ul> <li><em>S1 Mockup-based email view</em>. Represents the activity of viewing an email to check if it contains an attachment. In this case, the <em>key UI element</em> that receives the attention is the attachment inside the email.</li> <li><em>S2 Mockup-based CRM user details</em>. Represents a user's detail viewing activity within a Client Relationship Management (CRM) platform. The key UI element is the checkbox that indicates if the user has all his invoices paid.</li> <li><em>S3 Real screenshot email view. </em>Analogous to <em>S1</em> but with real screenshots. It represents the activity of viewing an e-mail to check if it contains an attachment. In this case, the key UI element to which attention is paid is the attachment contained in the e-mail.</li> <li><em>S4 Real screenshot CRM user details. </em>Analogous to <em>S2</em> but with real screenshots. It represents a user's detail viewing activity within a CRM platform. The key UI element is the checkbox indicating whether the user has all their invoices paid.</li> <li><em>S5 Real screenshot CRM user details. </em>Represents the split-screen display of two applications. On the left side a pdf viewer, showing a covid vaccination certificate. And on the right side a human resources management system (basic recreation of real system for privacy reasons). In this one the detail of the employee to whom the certificate of the left side corresponds is visualized. These screenshots, having two applications, have two key UI elements. In the pdf viewer it is the name of the certificate holder and in the human resources management system it is the name of the employee whose detail view is being displayed. The activity being carried out is the verification that the covid certificate received corresponds to that of an employee.</li> </ul> <p>Two types of filters based on the gaze fixation data are applied to these sets of screenshots: <em>Pre-filtering</em> and <em>Post-filtering</em>, corresponding to applying the filtering before and after detecting UI components in the screenshots, respectively. The structure of the data packages is divided in two folders <em>input </em>and <em>output</em>. The <em>input </em>folder is organized as follows:</p> <p><strong>input/</strong></p> <ul> <li><strong>screenshots/</strong>: corresponds to the screenshots. The sets of screenshots are easily identifiable, they are named following the pattern: <em>SX_screenshot_DDDD.jpeg</em>. Where <em>X </em>indicates to which of the set of screenshots described in the previous list it belongs, and <em>DDDD</em> represents a unique identifier for each screenshot. Each group consists of 10 screenshots, being 50 in total.</li> <li><strong>fixation.json</strong>: It is a JSON file that contains a <em>key </em>associated with each of the screenshots. For each screenshot, it contains a "fixation_points" key where information about the fixations that have occurred on the screenshot is stored. Here's an example: <pre><code> "S5_screenshot_0050.jpeg": { "fixation_points": { "334.25#497.166666666667": { "#events": 6, "start_index": 33224, "ms_start": 553962.1467, "ms_end": 554061.9899, "duration": 99.8432000001194, "imotions_dispersion": 0.300325967868111, "last_index": 33229, "dispersion": 14.044275227531914 }, "1258.80769230769#507.576923076923": { "#events": 13, "start_index": 33234, "ms_start": 554128.5427, "ms_end": 554345.3595, ...</code></pre> </li> </ul> <p>The <em>output </em>folder is organized in three subfolders, the first one containing the information of the <em>non-filtered</em> screenshots (i.e. without having applied to them any filtering or processing), and the next two with the information resulting from <em>pre-filtering</em> and <em>post-filtering</em>.</p> <p><strong>output/</strong></p> <ul> <li><strong>non-filter/</strong> <ul> <li><strong>borders/</strong>: screenshots with highlighted borders of <strong>all </strong>UI components detected in it.</li> <li><strong>components_json/</strong>: a collection of JSON files with the same name as the screenshot, containing the "img_shape" key with a list of the screen resolution and the number of layers the image has: [1080, 1920, 3], and the "compos" key with a list of <strong>all</strong> UI components representing the Screen Object Model.<br> </li> </ul> </li> <li><strong>pre-filter/</strong> and <strong>post-filter/</strong> <ul> <li><strong>borders/</strong>: screenshots with the borders of the <strong>relevant </strong>UI components. In the case of prefiltering, the detection of components is only performed on the parts of the screenshot that have received attention. In postfiltering, the complete screenshot is shown, with only the borders of the relevant UI components highlighted.</li> <li><strong>components_json/</strong>: a collection of JSON files with the same name as the screenshot is included, containing the following keys: <ul> <li>"img_shape": A list representing the screen resolution and the number of layers in the image, e.g., [1080, 1920, 3].</li> <li>"compos": A list of all UI components representing the Screen Object Model (SOM). During post-filtering, each UI component is augmented with an additional property called "relevant." If this property is set to <em>true</em>, it indicates that the respective UI component has received attention.</li> </ul> </li> <li><strong>(pre)/(post)filter_attention_maps/</strong>: represent the attention maps. In the case of prefiltering, any surface of the screen that has not received attention will be shown in black. In the case of postfiltering, the areas of attention will be shown as red circles, and the UI components whose area intersects with the areas of attention by more than 25% will be shown in yellow.</li> </ul> </li> </ul> <p>In conclusion, the described data package consists of sets of screenshots, accompanied by prefiltering and postfiltering filters using gaze fixation data, enabling the identification of relevant UI components. The organized data packages include input and output folders, where the output folder offers processed screenshots, UI component information, and attention maps. This resource provides valuable insights into user attention and interaction with UI elements on different types of scenarios.</p>
Data for: Probing visual sensitivity and attention in mice using reverse correlation
<p>Visual attention allows the brain to evoke behaviors based on the most important visual features. Mouse models offer immense potential to gain a circuit-level understanding of this phenomenon, yet, how mice distribute attention across features and locations is not well understood. Here, we describe a new approach to address this limitation, by training mice to detect weak vertical bars in a background of checkerboard noise while spatial cues manipulated their attention. By adapting a reverse correlation method from human studies, we linked behavioral decisions to stimulus features and locations. We show that mice voluntarily deploy attention to a small rostral region of the visual field. Within this region, mice attended to multiple features (orientation, spatial frequency, contrast) that indicated the presence of weak vertical bars. This attentional tuning grew with training, multiplicatively scaled behavioral sensitivity, approached that of an ideal observer, and resembled the effects of attention in humans. Taken together, we demonstrate that mice can simultaneously attend to multiple features and locations of a visual stimulus.</p>
Data for: Probing visual sensitivity and attention in mice using reverse correlation
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Investigating the Impact of Visual Stimuli on the Auditory Selective Attention in a VR Classroom
<h2>General</h2> <p>The audio-visual Auditory Selective Attention - visual Priming (avASAvisPrim) project serves to investigate the impact of visual stimuli on auditory selective attention switch in a close-to-real-life classroom setting. This dataset consists of a Unity project and Matlab code used to collect data as well as the collected data and R scripts used for evaluation.</p> <p><strong>The dataset contains:</strong></p> <p> Unity project for audiovisual display and the experiment structure<br> Matlab code for experiment preparation and HpFT measurement<br> Data collected in the experiment (experiment performance)<br> R code for data evaluation</p> <h2>Experiment preparation using Matlab</h2> <p>The code and software used to prepare the experiment is provided in the folder "matlab_avASAvisPrim".</p> <p>The Matlab code used to prepare the trials for each participant as well as to measure the HpTFs. For the HpTF measurements, the ITA Toolbox for Matlab was used and is provided (https://git.rwth-aachen.de/ita/toolbox). A developmental version of Virtual acoustics (VA) 2021a (https://www.virtualacoustics.org/VA/overview/) is provided.</p> <p><strong>Software requirements:</strong></p> <p> Matlab 2020a or higher<br> ITA Toolbox for Matlab installed</p> <h2><br>Experiment conduction in Unity</h2> <p>The Unity project is provided in the folder "unity_avASAvisPrim".</p> <p>Therefore, a virtual classroom with some basic furniture is provided. The used models, prefabs and plugins can be found in the Assets folder.</p> <p>The acoustic stimuli for the task are taken from Loh and Fels 2023 "ChildASA dataset: Speech and Noise Material fpr Child-appropriate Paradigms on Auditory Selective Attention" https://doi.org/10.18154/RWTH-2023-00740. </p> <p>This Unity project was intended for the use in virtual reality using an HMD and respective controllers for input. However, it can also be used on a desktop pc. The mode can be changed using the "VRMode" toggle as described below.<br>The audio reproduction is realized using the Unity plugin for Virtual Acoustics (VA, http://www.virtualacoustics.org/ and https://git.rwth-aachen.de/ita/vaunity_package).</p> <p><strong>Software requirements:</strong></p> <p> Unity 2019.4.21.f1.<br> SteamVR 1.19.7<br> Virtual Acoustics v2021a, VAUnity: https://git.rwth-aachen.de/ita/VAUnity</p> <h2><br>Data evaluation</h2> <p>The collected data is provided in the folder "dataEvaluation_avASAvisPrim". This folder contains three types of data: the raw data collected in the experiment (reaction times and error rates). R code for the evaluation of the head tracking data and the questionnaires is provided.</p>
Data - "Suppressing feedback signals to visual cortex abolishes attentional modulation"
<p>Data associated with article "Suppressing feedback signals to visual cortex abolishes attentional modulation"</p>
The symmetry-induced numerosity illusion depends on visual attention
<p>For the ST, ST-WD: each file contains a matrix called “a”. Each row of the matrix “a” is a trial. </p> <p>The columns contain the following information:</p> <ul> <li>1st: Numerosity of test stimulus</li> <li>2nd: Log10 of numerosity of test stimulus divided by the standard numerosity</li> <li>3rd: Participant response (0 = standard stimulus; 1 = test stimulus)</li> <li>4th: 200</li> <li>5th: Standard numerosity </li> <li>6th: Dots size in pixels</li> <li>7th: 10</li> <li>8th: Condition (0= symmetry condition; 1= random condition)</li> <li>9th: Standard-Test order (1 = standard first; 2 = standard second)</li> <li>10th: Response time</li> <li>11th: -99</li> <li>12th: -99</li> <li>13th: -99</li> <li>14th: 0</li> </ul> <p>For the DT: each file contains a matrix called “a”. Each row of the matrix “a” is a trial. </p> <p>The columns contain the following information:</p> <ul> <li>1st: Numerosity of test stimulus</li> <li>2nd: Log10 of numerosity of test stimulus divided by the standard numerosity</li> <li>3rd: Participant response (0 = standard stimulus; 1 = test stimulus)</li> <li>4th: 200</li> <li>5th: Standard numerosity </li> <li>6th: Dots size in pixels</li> <li>7th: 10</li> <li>8th: Condition (0= symmetry condition; 1= random condition)</li> <li>9th: Standard-Test order (1 = standard first; 2 = standard second)</li> <li>10th: Response time to the numerosity task</li> <li>11th: Response to the color-conjunction task (1: target; 0: no-target)</li> <li>12th: code of color-conjuction stimulus presented onscreen</li> <li>13th: Accuracy to the color-conjuction task (1:correct; 0:wrong)</li> <li>14th: 1</li> </ul>
Detecting an Early Response to Donepezil With Measures of Visual Attention
ClinicalTrials.gov study NCT03073876. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Encoding of cerebellar dentate neuron activity during visual attention in rhesus macaques
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Data from: Attentional modulation of secondary somatosensory and visual thalamus of mice
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Distinct attentional characteristics of neurons with visual feature coding in the primate brain
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Attention and distraction in the modular visual system of a jumping spider
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Dataset from: "Action relevance induces an attentional weighting of representations in visual working memory"
<p>Dataset from the following publication:</p> <p>Heuer, A., Crawford, J.D., & Schubö, A. (2016). Action relevance induces an attentional weighting of representations in visual working memory. Memory & Cognition. doi:10.3758/s13421-016-0670-3</p>
Dataset from "Rewarded visual items capture attention only in heterogeneous contexts"
<p>Dataset from the following publication: Feldmann-Wüstefeld, T., Brandhofer, R. & Schubö, A. (2016). Rewarded visual items capture attention only in heterogeneous contexts. Psychophysiology, 53, 1063-1073. DOI: 10.1111/psyp.12641</p>
Data from: Laminar microcircuitry of visual cortex producing attention-associated electric fields
<p>Cognitive operations are widely studied by measuring electric fields through EEG and ECoG. However, despite their widespread use, the neural circuitry giving rise to these signals remains unknown because the functional architecture of cortical columns producing attention-associated electric fields has not been explored. Here, we detail the laminar cortical circuitry underlying an attention-associated electric field measured over posterior regions of the brain in humans and monkeys. First, we identified visual cortical area V4 as one plausible contributor to this attention-associated electric field through inverse modeling of cranial EEG in macaque monkeys performing a visual attention task. Next, we performed laminar neurophysiological recordings on the prelunate gyrus and identified the electric-field-producing dipoles as synaptic activity in distinct cortical layers of area V4. Specifically, activation in the extragranular layers of cortex resulted in the generation of the attention-associated dipole. Feature selectivity of a given cortical column determined the overall contribution to this electric field. Columns selective for the attended feature contributed more to the electric field than columns selective for a different feature. Last, the laminar profile of synaptic activity generated by V4 was sufficient to produce an attention-associated signal measurable outside of the column. These findings suggest that the top-down recipient cortical layers produce an attention-associated electric field that can be measured extracortically with the relative contribution of each column depending upon the underlying functional architecture.</p>
Pupil dilation during orienting of attention and conscious detection of visual targets in patients with left spatial neglect
<p>Right Brain-Damaged patients (RBD) with left spatial neglect (N+), are characterised by deficits in orienting and re-orienting attention to stimuli in the contralesional left side of space. In a recent ERPs study with visual stimuli (Lasaponara et al., 2018) we have pointed out that the pathological attentional bias of N+ is matched with exaggerated novelty reaction and contextual updating of targets in the right ipsilesional space and reduced novelty reaction and contextual updating of targets in the left contralesional space. To characterise further the attentional performance of N+, here we measured Pupil Dilation (PDil), which is a reliable marker of noradrenergic-locus coeruleus activity and response to unexpected events/rewards. Compared to Neutral and Valid targets, N+ patients displayed a pathological reduction of PDil in response to infrequent Invalid targets in the left side of space, while in Healthy Controls (HC) and RBD without neglect (N-) the same targets enhanced PDil with respect to Neutral and frequent Valid targets. Invalid targets in the right side of space enhanced PDil in all experimental groups. Interestingly, both N- and N+ showed a consistent number of target omissions both in the left and right side of space. With respect to seen targets, N- showed reduced PDil in response to unseen targets both in the left and right side of space. In contrast, N+ had reduced PDil in response to unseen targets in the left side of space though not in the right side, where seen and unseen targets evoked comparable levels of PDil. These results disclose, for the first time, the PDil correlates of spatial attention in left spatial neglect and suggest that the pathological attentional bias suffered by N+ might enhance the autonomic responses reflected in PDil to unseen ipsilesional stimuli. This enhancement can contribute to biasing contextual updating and predictive coding of stimuli in the ipsilesional space, thus worsening the pathological attentional bias of N+.</p>
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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.