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42 results for “Gaze data”
Eye image data with gaze labels recorded using custom video-oculography hardware at 120Hz
<p>The repository of eye image data with corresponding gaze labels collected from 40 subjects. The preview contains a collage of random image samples, one per subject. </p> <p>All recorded subjects gave informed consent under an experimental protocol approved by the Institutional Research Board of Texas State University (approval code 2018044) and their data were anonymized prior to public release.</p> <p>The data were recorded using the custom video-oculography (VOG) desktop hardware setup at 120Hz. The full description of this eye-tracking system's capabilities is provided at https://doi.org/10.48550/arXiv.1904.07361.</p> <p>This VOG set contains recordings of the random oblique saccades task. It is comprised of 174 on-screen fixation targets that densely cover the range of ±20.51° horizontally and ±16.7° vertically (in degrees of visual angle). More detail on the presented stimuli can be found at https://doi.org/10.1145/3379156.3391370.</p> <p>The data were also used in Dmytro Katrychuk's Ph.D. thesis "Generating Realistic Eye Images to Evaluate Photosensor Oculography Eye-Tracking for Portable Headsets" (https://hdl.handle.net/10877/19437); with the release for public use in the upcoming publication "An appearance-based gaze estimation as a benchmark for eye image data generation methods" accepted to MDPI Journal of Applied Sciences. </p> <p>Each .zip archive represents a recording from one subject, which includes:</p> <ul> <li>Video of the close eye capture in ".avi" format</li> <li>Calibration data in ".xml" format</li> <li>Gaze data in ".tsv" format</li> <li>On-screen target stimulus position in ".tsv" format</li> </ul> <p>The "src.zip" provides a Python script to unpack each ".avi" video recording to a set of ".png" images. The direct playback of ".avi"s may require special codecs and is not supported. </p> <p>Any additional code will be uploaded to https://github.com/dkatrychuk/psog-eval-diss2023</p> <p>The authors can be contacted at their corresponding emails: Dmytro Katrychuk - d_k139@txstate.edu; Oleg Komogortsev - ok@txstate.edu.</p>
Time Series Data of Gaze, Head Pose, Hand Pose, and Object Positions for Object Approaches with a Given Intention
<p>This data set comprises time series data of gaze, head pose, hand pose, and object positions for object approaches with a given intention. The data was captured in the context of the following publication:</p> <ul> <li><em>Michael Fennel, Serge Garbay, Antonio Zea, Uwe D. Hanebeck</em>, <strong>Intention Estimation with Recurrent Neural Networks for Mixed Reality Environments</strong>, Proceedings of the 26th International Conference on Information Fusion (Fusion 2023) <em>(under review)</em></li> </ul> <p>A Microsoft Hololens 2 was used for recording the data at 60 fps under the modalities explained in detail in the above-mentioned paper.</p> <p>The file names are structured as follows:</p> <ul> <li><em>1st/2nd:</em> <ul> <li>The data with "1st" contains approaches to randomly placed objects on a grid, which are rendered in augmented reality. The user is informed about the object to approach using a visual cue. This corresponds to Section IV-A.</li> <li>The data with "2nd" contains approaches to real objects placed statically in a room. The user is informed about the object to approach using a voice command.</li> </ul> </li> <li><em>unfiltered:</em> Contains all approaches, including those where the user disrespects the given commands. Filtering is done as described in the paper.</li> <li><em>train/val/test:</em> The first dataset was split in a 70/20/10 ratio for training, validation, and test.</li> </ul> <p>Each data set contains the following columns. In each approach, 5 objects numbered from i=0 to i=4 are present.</p> <ul> <li>General: <ul> <li><em>time:</em> in seconds</li> <li><em>subject:</em> consecutive subject number</li> <li><em>handedness:</em> left (1), right (0)</li> <li><em>trial:</em> consecutive trial number per subject</li> <li><em>target_label:</em> index of the object to approach (0 to 4)</li> </ul> </li> <li>Data in world coordinates: <ul> <li><em>head_{x,y,z}:</em> head position</li> <li><em>head_quat_{w,x,y,z}:</em> head orientation quaternion</li> <li><em>W_gaze_{x,y,z}:</em> gaze direction</li> <li><em>W_r_hand_{x,y,z}:</em> right hand position</li> <li><em>W_r_hand_quat_{w,x,y,z}:</em> right hand orientation quaternion</li> <li><em>W_l_hand_{x,y,z}:</em> left hand position</li> <li><em>W_l_hand_quat_{w,x,y,z}:</em> left hand orientation quaternion</li> <li><em>W_object_i_{x,y,z}:</em> position of object i</li> <li><em>W_object_i_quat {w,x,y,z}</em>: orientation quaternion of object i</li> </ul> </li> <li>Data in egocentric coordinates (head coordinate system). This data is provided for convenience and can be derived from the other data: <ul> <li><em>gaze_{x,y,z}:</em> gaze direction</li> <li><em>r_hand_{x,y,z}:</em> right hand position</li> <li><em>r_hand_quat_{w,x,y,z}:</em> right hand orientation quaternion</li> <li><em>l_hand_{x,y,z}:</em> left hand position</li> <li><em>l_hand_quat_{w,x,y,z}:</em> left hand orientation quaternion</li> <li><em>object_i_{x,y,z}:</em> position of object i</li> <li><em>object_i_quat {w,x,y,z}</em>: orientation quaternion of object i</li> </ul> </li> </ul> <p><strong>Acknowledgment:</strong></p> <p>This work was supported by the <a href="https://robdekon.de/">ROBDEKON</a> project of the German Federal Ministry of Education and Research.</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>
Data + Analyses: "Gaze-dependent Coding of Somatosensory Reach Targets after Effector Movement: Testing the Impact of Online Information, Movement Timing, and Target Distance"
<p>This upload contains the experiment scripts (written in Presentation), data, and analyses (performed with MATLAB and SPSS) underlying the publication<strong> </strong>by Mueller & Fiehler (2017). <em>PloS one</em>. doi:<strong>10.1371/journal.pone.0180782</strong></p>
Data and code for 3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality
<p>This repository contains data and code for</p> <p>Lento B., Segas E., Leconte V., Doat E., Danion F., Péteri R., Benois-Pineau J., de Rugy A. (2024). <strong>3D-</strong><strong>ARM</strong><strong>-Gaze</strong><strong>: a </strong><strong>public </strong><strong>dataset of </strong><strong>3D </strong><strong>A</strong><strong>rm </strong><strong>R</strong><strong>eaching </strong><strong>M</strong><strong>ovements</strong><strong> </strong><strong>with Gaze information</strong><strong> </strong><strong>in </strong><strong>virtual reality</strong><strong>. </strong>doi:</p> <p>It contains a dataset <strong>(DBAS22_DataOnline </strong>folder) of natural arm movements together with visual and gaze information when reaching objects in a wide reachable space from a precisely controlled, comfortably seated posture. More details could be find in the link publication (see Related identifiers section).</p> <p>The <strong>DBAS22_DocOnline</strong> folder contains all the documentation files. The <strong>MainDataExplained </strong>file lists and describes the variables recorded during the experimental phases. In the <strong>SummaryOfFiles </strong>document, you will find descriptions for all the files within the <strong>DBAS22_DataOnline</strong> folder, and at the bottom, there is also a file tree that illustrates the file structure. The <strong>DBAS22FilesWorkflow </strong>document offers an overview of the workflow of experimental file creation during the experiment.</p> <p>The <strong>DBAS22_CodeOnline</strong> folder contains all the scripts to perform data analysis, listed and described in the files <strong>CodeExplanations </strong>and <strong>DependenciesRelations</strong>. The <strong>GuideInstall </strong>file contains information needed to run the Python code files.</p> <p>The <strong>DBAS22_CodeOnline</strong> folder also contains the DataPlayer Unity project. Instructions for running the project are provided in the <strong>DataPlayerGuide </strong>file and SupplementaryVideo2 (see Related identifiers section for more details). The folder <strong>DBAS22_DataPlayer_StandAloneApp </strong>contains the standalone version of the DataPlayer, which doesn't require any software installation.</p> <p>The <strong>DBAS22_VideoOnline</strong> folder contains all the videos. </p>
Data from: Active head movements contribute to spatial updating across gaze shifts
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Publication data of Examining holistic processing strategies in dogs and humans through gaze behavior
<p>This version, compared to the version 1, further includes the figures and tables used in the publication. </p> <p>There is an error in the Table 3 file uploaded. The locations of the column names 'else upper half' and 'else lower half' or the images in the two column cells are switched.</p> <p>Please check the Figure 4 of PloS one version of the paper for correct information. <a href="https://doi.org/10.1371/journal.pone.0317455">https://doi.org/10.1371/journal.pone.0317455</a></p> <p> </p> <p>bioRXiv version: Data of Holistic Processing Strategy in Cross-Species Face Perception between Dogs and Humans </p> <p><a href="https://doi.org/10.1101/2024.06.21.599532">https://doi.org/10.1101/2024.06.21.599532</a></p> <p>&</p> <p>PloS one version: Data of Examining holistic processing strategies in dogs and humans through gaze behavior </p> <p><a href="https://doi.org/10.1371/journal.pone.0317455">https://doi.org/10.1371/journal.pone.0317455</a></p>
Gaze cueing by multiple people - averaged eye tracking data by participant
<p>Data for article Gaze cueing by multiple people.</p> <p>Averaged eye tracking data by participant that was included.</p> <p>Task 1 - time to first fixation and total fixation duration for each Area of Interest </p> <p>Task 2 - time to first fixation for Area of Interest as determined by the task </p>
Neural and behavioral data from: A dynamic sequence of visual processing initiated by gaze shifts
<p>Animals move their head and eyes as they explore and sample the visual scene. Previous studies have demonstrated neural correlates of head and eye movements in rodent primary visual cortex (V1), but the sources and computational roles of these signals are unclear. We addressed this by combining measurement of head and eye movements with high density neural recordings in freely moving mice. V1 neurons responded primarily to gaze shifts, where head movements are accompanied by saccadic eye movements, rather than to head movements where compensatory eye movements stabilize gaze. A variety of activity patterns immediately followed gaze shifts, including units with positive, biphasic, or negative responses, and together these responses formed a temporal sequence following the gaze shift. These responses were greatly diminished in the dark for the vast majority of units, replaced by a uniform suppression of activity, and were similar to those evoked by sequentially flashed stimuli in head-fixed conditions, suggesting that gaze shift transients represent the temporal response to the rapid onset of new visual input. Notably, neurons responded in a sequence that matches their spatial frequency preference, from low to high spatial frequency tuning, consistent with coarse-to-fine processing of the visual scene following each gaze shift. Recordings in foveal V1 of freely gazing head-fixed marmosets revealed a similar sequence of temporal response following a saccade, as well as the progression of spatial frequency tuning. Together, our results demonstrate that active vision in both mice and marmosets consists of a dynamic temporal sequence of neural activity associated with visual sampling.</p>
Data from: Orbitofrontal cortex computes gaze-dependent comparisons between attributes rather than integrated values
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Neural and behavioral data from: A dynamic sequence of visual processing initiated by gaze shifts
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Data from: Remote activation of place codes by gaze in a highly visual animal
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Data from: Flexible gaze-following in rhesus monkeys
Humans are characterized by complex social cognitive abilities that emerge early in development. Comparative studies of nonhuman primates can illuminate the evolutionary history of these social capacities. We examined the cognitive skills that rhesus monkeys (Macaca mulatta) use to follow gaze, a foundational skill in human social development. While rhesus monkeys can make inferences about others' gaze when competing, it is unclear how they think about gaze information in other contexts. In study 1, monkeys (n = 64) observed a demonstrator look upwards either in a barrier condition where a box was overhead, so that monkeys could not see the target of her gaze, or a no barrier condition where nothing blocked her view. In study 2, monkeys (n = 59) could approach to observe the target of the demonstrator's gaze when the demonstrator looked behind a barrier on the ground or, in the no barrier condition, behind a window frame in the same location. Monkeys were more likely to directly look up in study 1 if they could initially see the location where the demonstrator was looking, but they did not preferentially reorient their bodies to observe the out-of-view location when they could not see that location. In study 2, monkeys did preferentially reorient, but at low rates. This indicates that rhesus monkeys can use social cognitive processes outside of competitive contexts to model what others can or cannot see, but may not be especially motivated to see what others look at in non-competitive contexts, as they reorient infrequently or in an inconsistent fashion. These similarities and differences between gaze-following in monkeys and children can help to illuminate the evolution of human social cognition.
Data from: Influence of gaze and directness of approach on the escape responses of the Indian rock lizard, Psammophilus dorsalis (Gray, 1831)
Animals often evaluate the degree of risk posed by a predator and respond accordingly. Since many predators orient their eyes towards prey while attacking, predator gaze and directness of approach could serve as conspicuous indicators of risk to prey. The ability to perceive these cues and discriminate between high and low predation risk should benefit prey species through both higher survival and decreased energy expenditure. We experimentally examined whether Indian rock lizards (Psammophilus dorsalis) can perceive these two indicators of predation risk by measuring the variation in their fleeing behaviour in response to type of gaze and approach by a human predator. Overall, we found that the gaze and approach of the predator influenced flight initiation distance, which also varied with attributes of the prey (i.e. size/sex and tail-raise behaviour). Flight initiation distance (FID) was 43% longer during direct approaches with direct gaze compared with tangential approaches with averted gaze. In further, exploratory, analyses, we found that FID was 23% shorter for adult male lizards than for female or young male (FYM) lizards. In addition, FYM lizards that showed a tail-raise display during approach had a 71% longer FID than those that did not. Our results suggest that multiple factors influence the decision to flee in animals. Further studies are needed to test the generality of these factors and to investigate the proximate mechanisms underlying flight decisions.
Data Artifact for "Gaze into the Pattern: Characterizing Spatial Patterns with Internal Temporal Correlations for Hardware Prefetching"
<p>This dataset contains the Ligra, PARSEC, GAP, and QMM traces used in our paper "Gaze into the Pattern: Characterizing Spatial Patterns with Internal Temporal Correlations for Hardware Prefetching", which is accepted by HPCA'25. These traces are provided as part of the AE proces.</p>
statistical analysis and data: How personality shapes gaze behavior without compromising subtle emotion recognition.
<pre>CODE:<br>script_gca_X.R: scripts used for the growth curve analysis fits<br>script_fits_brms_no_tb.r: script containing the fits (except those related to the crowth curve analysis).<br>inpact_script_contrasts.r: contrasts of every fits (call inpact_script_plot.r and inpact_script_save.r)<br>inpact_script_plot.r: plots<br>inpact_script_save: save contrasts to csv and xlsx files<br><br>DATA:<br>cluster_df.Rda: personality <br>neutral.Rda: neutral trials<br>sdtg.Rda: signal detection theory parameters<br>resp_emo.Rda : raw data to emotional trials<br>df_et4_propn.Rda: eye tracking data, first exposure phase of the emotional trials (0-1000ms)<br>df_et4_prop.Rda: eye tracking data, second exposure phase of the emotional trials (1000-2000ms)<br>df_n4_propn.Rda: eye tracking data, first exposure phase of the neutral trials (0-1000ms)<br>df_n4_prop.Rda: eye tracking data, second exposure phase of the neutral trials (1000-2000ms)<br><br>gca_e_contrast.Rda: contrasts of the growth curve analysis fit for the eye Area of Interest (AOI)<br>gca_n_contrast.Rda: contrasts of the growth curve analysis fit for the nose AOI<br>gca_m_contrast.Rda: contrasts of the growth curve analysis fit for the mouth AOI<br><br>X.rds: fits<br><br><br>STIMULI:<br>- videos of the neutral and emotional facial expressions<br>- backward masks</pre>
Data from: Influence of gaze and directness of approach on the escape responses of the Indian rock lizard, Psammophilus dorsalis (Gray, 1831)
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Data from: Flexible gaze-following in rhesus monkeys
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Data from: Social and emotional contexts predict the development of gaze following in early infancy
<p>The development of gaze following begins in early infancy and its developmental foundation has been under heavy debate. Using a longitudinal design (<i>N</i> = 118), we demonstrate that attachment quality predicts individual differences in the onset of gaze following, at 6 months of age, and that maternal postpartum depression predicts later gaze following, at 10 months. In addition, we report longitudinal stability in gaze following from 6 to 10 months. A full path model (using attachment, maternal depression, and gaze following at 6) accounted for 21 % of variance in gaze following at 10 months. These results suggest an <i>experience-dependent</i> development of gaze following, driven by the infant's own motivation to interact and engage with others (the <i>social-first</i> perspective).</p>
Data from: Herring gulls respond to human gaze direction
Human-wildlife conflict is one of the greatest threats to species populations worldwide. One species facing national declines in the UK is the herring gull (Larus argentatus), despite an increase in numbers in urban areas. Gulls in urban areas are often considered a nuisance due to behaviours such as food-snatching. Whether urban gull feeding behaviour is influenced by human behavioural cues, such as gaze direction, remains unknown. We therefore measured the approach times of herring gulls to a food source placed in close proximity to an experimenter who either looked directly at the gull or looked away. We found that only 26% of targeted gulls would touch the food, suggesting that food-snatching is likely to be conducted by a minority of individuals. When gulls did touch the food, they took significantly longer to approach when the experimenter's gaze was directed towards them compared to directed away. However, inter-individual behaviour varied greatly, with some gulls approaching similarly quickly in both treatments while others approached much more slowly when the experimenter was looking at them. These results indicate that reducing human-herring gull conflict may be possible through small changes in human behaviour, but will require consideration of behavioural differences between individual gulls.
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International Brain Laboratory public data
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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.