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190 results for “Eye Movements”
Enhanced brain responses to color during smooth pursuit eye movements
<p>Dataset relative to the following publication:</p> <p>Chen, J., Valsecchi, M. & Gegenfurtner, K.R. (2017). Enhanced brain responses to color during smooth pursuit eye movements. <em>Journal of Neurophysiology, </em>DOI: 10.1152/jn.00208.2017</p> <p>Please refer to "data description.txt" for details about the data. </p>
A quantitative approach to study the adaptation of rhythmic eye movements in larval zebrafish
<p>Optokinetic nystagmus; quick phases; spontaneous saccades; saccadic adaptation; congenital ocular motor apraxia</p>
Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps - data
<p>Data from the article<strong><em> Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps</em></strong> published in Journal of Vision.</p> <p>code: https://zenodo.org/record/5999215<br> data: https://zenodo.org/record/5999046</p> <p><br> Marek A. Pedziwiatr<br> marek.pedziwi@gmail.com<br> February 2022</p>
The impact of music and stretched time on pupillary responses and eye movements in slow-motion film scenes
<p>Dataset of the article "The impact of music and stretched time on pupillary responses and eye movements in slow-motion film scenes" published in Journal of Eye Movement Research.</p>
Evaluating the role of body size and habitat type in movement behavior in human-dominated systems: A frog's eye view
<p>Animal movement is a key process that connects and maintains populations on the landscape, yet for most species we do not understand how intrinsic and extrinsic factors interact to influence individual movement behavior. </p> <p>Land-use/land-cover changes highlight that connectivity among populations will depend upon an individual's ability to traverse habitats, which may vary as a result of habitat permeability, individual condition, or a combination of these factors.</p> <p>We examined the effects of intrinsic (body size) and extrinsic (habitat type) factors on desiccation tolerance, movement, and orientation in three anuran species (American toads, <em>Anaxyrus americanus</em>; northern leopard frogs, <em>Lithobates pipiens</em>; and Blanchard's cricket frogs, <em>Acris blanchardi</em>) using laboratory and field studies to connect the effects of susceptibility to desiccation, size, and movement behavior in single habitat types and at habitat edges.</p> <p>Smaller anurans were more vulnerable to desiccation, particularly for species that metamorphose at relatively small sizes. Habitat type had the strongest effect on movement, while body size had more situational and species-specific effects on movement. We found that individuals moved the farthest in habitat types that, when given the choice, they oriented away from, suggesting that these habitats are less favorable and could represent barriers for movement.</p> <p>Overall, our work demonstrated that differences in habitat type had strong impacts on individual movement behavior and influenced choices at habitat edges. By integrating intrinsic and extrinsic factors into our study, we provided evidence that population connectivity may be influenced not only by the habitat matrix, but the condition of the individuals leaving the habitat patch.</p>
Distribution. Now restricted to the Channel Country of SW Queensland and the Lake Eyre Basin in NE South Australia. Descriptive notes. Head-body 95-120 mm, tail 105-160 mm, ear 23-29 mm, hindfoot 32-37 mm; weight 30-50 g. The Fawn Hopping Mouse has body form typical of hopping mice, with very long hindfeet, long tail with distal brush of longer hairs, very long ears, and large protruberant eyes. Dorsal fur is of variable color, from pale pinkish fawn to gray; ventral fur white. Unlike most other hopping mice, it has no throat pouch, but males have a glandular area of naked skin on the chest. Habitat. Occurs in low shrublands and tussock grasslands on stony ("gibber") plains and claypans. Shows marked habitat segregation from the Dusky Hopping Mouse (N. fuscus), which is closely associated with sandy substrates. Food and Feeding. The Fawn Hopping Mouse is mostly granivorous, but also eats other plant material (stems, leaves) and occasionally invertebrates. It uses succulent, salt-adapted plants around edges of claypans as a source of water. Breeding. Reproduction is probably largely opportunistic and aseasonal, with high reproductive output from near-continuous breeding after periods of high rainfall; reported littersize is 1-5, most commonly three; gestation period 38-43 days for nonlactating females. Females may mature later than other hopping mice, with reproductive maturity reached at about six months. Activity patterns. Terrestrial and nocturnal. Fawn Hopping Mice shelter during day in burrow systems that are typically simpler and shallower than those of other hopping mice. Movements, Home range and Social organization. Fawn Hopping Mice generally live singly or in small groups; typically uncommon within range, but population density may increase by an order of magnitude following periods of high rainfall. Status and Conservation. Classified as Near Threatened on The IUCN Red List. The Fawn Hopping Mouse has shown marked decline in range (estimated at greater than 50%), and presumably population size, since European settlement of Australia. This is mostlikely due to predation by the introduced house cat and Red Fox (Vulpes vulpes), and to habitat degradation associated with pastoralism. Bibliography. Brazenor (1934), Burbidge et al. (2008), Finlayson (1939), Gould (1853), Jackson & Groves (2015), Murray et al. (1999), Ogilby (1892), Thomas (1921h), Van Dyck & Strahan (2008), Waite (1898), Watts & Aslin (1981), Woinarski et al. (2014), Wood Jones (1925). in Muridae
Distribution. Now restricted to the Channel Country of SW Queensland and the Lake Eyre Basin in NE South Australia. Descriptive notes. Head-body 95-120 mm, tail 105-160 mm, ear 23-29 mm, hindfoot 32-37 mm; weight 30-50 g. The Fawn Hopping Mouse has body form typical of hopping mice, with very long hindfeet, long tail with distal brush of longer hairs, very long ears, and large protruberant eyes. Dorsal fur is of variable color, from pale pinkish fawn to gray; ventral fur white. Unlike most other hopping mice, it has no throat pouch, but males have a glandular area of naked skin on the chest. Habitat. Occurs in low shrublands and tussock grasslands on stony ("gibber") plains and claypans. Shows marked habitat segregation from the Dusky Hopping Mouse (N. fuscus), which is closely associated with sandy substrates. Food and Feeding. The Fawn Hopping Mouse is mostly granivorous, but also eats other plant material (stems, leaves) and occasionally invertebrates. It uses succulent, salt-adapted plants around edges of claypans as a source of water. Breeding. Reproduction is probably largely opportunistic and aseasonal, with high reproductive output from near-continuous breeding after periods of high rainfall; reported littersize is 1-5, most commonly three; gestation period 38-43 days for nonlactating females. Females may mature later than other hopping mice, with reproductive maturity reached at about six months. Activity patterns. Terrestrial and nocturnal. Fawn Hopping Mice shelter during day in burrow systems that are typically simpler and shallower than those of other hopping mice. Movements, Home range and Social organization. Fawn Hopping Mice generally live singly or in small groups; typically uncommon within range, but population density may increase by an order of magnitude following periods of high rainfall. Status and Conservation. Classified as Near Threatened on The IUCN Red List. The Fawn Hopping Mouse has shown marked decline in range (estimated at greater than 50%), and presumably population size, since European settlement of Australia. This is mostlikely due to predation by the introduced house cat and Red Fox (Vulpes vulpes), and to habitat degradation associated with pastoralism. Bibliography. Brazenor (1934), Burbidge et al. (2008), Finlayson (1939), Gould (1853), Jackson & Groves (2015), Murray et al. (1999), Ogilby (1892), Thomas (1921h), Van Dyck & Strahan (2008), Waite (1898), Watts & Aslin (1981), Woinarski et al. (2014), Wood Jones (1925).
Investigating the Eye Movement Characteristics of Basketball Players Executing 3-Point Shot at Varied Intensities and their Correlation with Shot Accuracy
<h2>不同强度三分球运动员眼球运动特征及其与投篮命中率的相关性研究</h2>
The impact of cognitive remediation therapy on lower eye movement and word fluency function: A prospect for improvement of eye movement and cognitive function in schizophrenia
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Pointing movements and eye-tracking data_Facilitated Communication Users
<p>The repository contains all the pre-sorted data used for the analysis described in the paper. Data are divided into three:</p> <ul> <li>in A, we report the movement data of each pointing gesture considered in the analysis.</li> <li>in B we report keystrokes' related data.</li> <li>in C we report the sorted eye-tracking data.</li> </ul> <h3>A. User Correct Movements:</h3> <p>Each participant's data is organized into a 1xN cell array in Matlab, where N represents the number of pointing gestures analyzed. Each cell contains an Nx8 column vector with the following information:</p> <ol> <li> <p><strong>Time Information (column 1)</strong>:</p> <ul> <li>Time associated with the pointing gesture (milliseconds).</li> </ul> </li> <li> <p><strong>Arm Coordinates (columns 2,3 and 4)</strong>:</p> <ul> <li>X-axis coordinates (millimetres).</li> <li>Y-axis coordinates (millimetres).</li> <li>Z-axis coordinates (millimetres).</li> </ul> </li> <li> <p><strong>EMG Deltoid Activation (Facilitator) (columns 5, and 6) </strong>:</p> <ul> <li>Rectified EMG deltoid activation.</li> <li>Envelope EMG deltoid activation.</li> </ul> </li> <li> <p><strong>EMG Deltoid Activation (User) (columns 7 and 8)</strong>:</p> <ul> <li>Rectified EMG deltoid activation.</li> <li>Envelope EMG deltoid activation.</li> </ul> </li> </ol> <h3>B. Users' Keys Pressed with Probability:</h3> <p>Each participant's data is organized into an Nx6 string array, where N represents the number of pointing gestures analyzed. Each array contains the following information:</p> <ol> <li> <p><strong>Key Press Time</strong>:</p> <ul> <li>Absolute time the key is pressed (milliseconds, as recorded by the key-logger).</li> </ul> </li> <li> <p><strong>Time Between Keystrokes</strong>:</p> <ul> <li>Difference in milliseconds between two consecutive keystrokes.</li> </ul> </li> <li> <p><strong>Key Pressed</strong>:</p> <ul> <li>The key that has been pressed.</li> </ul> </li> <li> <p><strong>Pointing Time</strong>:</p> <ul> <li>Time taken by the arm to complete the forward phase of the pointing gesture (seconds).</li> </ul> </li> <li> <p><strong>Character Position</strong>:</p> <ul> <li>Position of the pressed character within the word (spacebar hits are assigned the number 300).</li> </ul> </li> <li> <p><strong>Key Selection Probability</strong>:</p> <ul> <li>Probability (percentage) of the key being selected.</li> </ul> </li> </ol> <h3><strong>C. EyeTracking data sorted</strong></h3> <p>Each participant's data is organized into an N×6 cell array, where N represents the number of pointing gestures analyzed through eye-tracking. The contents of each row are as follows:</p> <ol> <li> <p><strong>Fixation Data (Nx5 vector)</strong>:</p> <ul> <li><strong>N</strong> is the number of fixations related to one pointing gesture.</li> <li>Each vector contains: <ul> <li> <p><strong>The standardized time </strong>is determined by synchronizing the eye fixation with the arm movement. Given the movement duration is scaled from 0 to 10, we identify the moment when the eye-fixation occurs.</p> <p>This standardized time refers to the duration of the fixation relative to the duration of the pointing gesture. In <strong>column 1</strong>, we report the gross time, averaging the beginning and end of the fixation. In<strong> column 4</strong>, we provide the exact standardization at the start, and in <strong>column 5</strong>, the exact standardization at the end of the movement. During analysis, these times are synchronized with the movement duration, and we use the data from column 4.</p> </li> <li> <p><strong>Euclidean distance</strong> between the fixated key and the target key (2nd column).</p> </li> <li><strong>Duration</strong> of each fixation (3rd column).</li> </ul> </li> </ul> </li> <li> <p><strong>Sequence of Fixated Keys</strong>:</p> <ul> <li>Contains the sequence of keys fixated by the user during each pointing gesture.</li> </ul> </li> <li> <p><strong>Arm Movement Information</strong>:</p> <ul> <li>Includes details on the arm movement (same as reported in<strong> file A</strong>) corresponding to the eye-tracking fixation sequence.</li> </ul> </li> <li> <p><strong>Target Key Pressed</strong>:</p> <ul> <li>Indicates the target key pressed by the participant.</li> </ul> </li> <li> <p><strong>Probability of Key Pressed</strong>:</p> <ul> <li>Reports the likelihood of each key being pressed,<strong> as detailed in file B.</strong></li> </ul> </li> <li> <p><strong>Euclidean Distance Between Consecutive Keys</strong>:</p> <ul> <li>Measures the Euclidean distance between two consecutively pressed keys using the keyboard as a reference (refer to the paper text for more details).</li> </ul> </li> </ol> <p> </p> <p> </p>
Reduced latency in manual interception with anticipatory smooth eye movements
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Predicting readers' prototypical eye-movement behavior using MASC, a model of Attention in the Superior Colliculus: Stimulus materials, model code, data, and statistical analyses.
<p>The goal of the present research was to determine the role of rudimentary visuo-motor pathways, from the retina and the primary visual cortex to the superior colliculus (SC), in the guidance of human eye movement during reading. To this end, we used MASC, our model of Attention in the Superior Colliculus (Adeli et al., Journal of Neuroscience 2017), a model that relies on well-established saccade-programming principles in the SC. MASC predicts sequences of fixations over an input image by spatially integrating incoming signals in the space of the SC.</p> <p>Here, MASC computed the distribution of luminance contrast over sentences' images (visual-saliency map), after blurring it proportional to retinal eccentricity (retina transformation). It then projected the visual-saliency map into SC space, using a logarithmic afferent-mapping function (magnification factor). Input signals were averaged over retinotopically organized populations of neurons (point images) of constant size, first in the visual map and then in a spatially-registered motor map. The most active population was identified through a winner-take-all process. After jitter applied to the winning population, the next fixation location was determined using inverse efferent mapping. This sequence of events was then repeated to predict following fixation locations, but inserting after each saccade an inhibitory spatial tag (Inhibition of Saccade Return; ISR -referred to as IOR in the uploaded files). All MASC's parameters, but one, were biologically determined, using electrophysiological data in macaque; the ISR window was the one fit parameter.</p> <p>MASC was tested by comparing its predicted sequences of fixations over sentences from the French-Sentence Corpus (FSC) to the eye-movement behavior of 40 French-native speakers reading the same sentences for comprehension (Albrengues et al., Plos One 2019). Then, MASC was dissected to determine the crucial processing steps enabling prediction of human behavior (10 comparison models -see the general README file). Finally, to address crucial issues in the reading literature, i.e., the role of inter-word spacing and character print size in eye-movement guidance, MASC was additionally tested in four additional display conditions: the same sentences from the FSC, but with blank spaces between words being either filled or removed, or with the screen width angle being multiplied by 2 or 4, such that characters were larger in angular size (0.5° and 1°) than in the original experiment (0.25°). MASC's predicted effects of inter-word spacing and print size were compared to previously published data.</p> <p>All material relevant to the project is reported here, including the FSC materials (bitmap and information text files), the Matlab code for our MASC model, raw simulation data for MASC and all our comparison models, as well as MASC's simulations in the different display conditions, the scripts we developed in R to transform raw simulation data into data matrices for statistical analyses of (word-based) eye-movement behavior, the resulting data matrices for all models as well as the data matrix for FSC readers, the R-scripts for statistical comparison of oculomotor behavior between data sets and conditions, literature-review tables of previously published data (for comparison with MASC's predictions), and the R-scripts generating the figures summarizing our results.</p> <p>Further information can be found in the general README file as well as in the README files attached to each folder. The authors' respective contributions to the project, the licence attached to the included materials and their condition of use are listed in the general README file.</p> <p>A manuscript reporting and discussing these modeling data is in preparation (Vitu, F., Adeli, H. & Zelinsky, G. J.); A reference will be provided here when the manuscript appears in a journal.</p> <p>Other references to be cited:</p> <p>- For the model code: Adeli, H., Vitu, F., & Zelinsky, G. J. (2017). A model of the superior colliculus predicts fixation locations during scene viewing and visual search. Journal of Neuroscience, 37(6), 1453-1467. http://www.jneurosci.org/content/37/6/1453</p> <p>- For FSC materials and data: Albrengues, C., Lavigne, F., Aguilar, C., Castet, E., & Vitu, F. (2019). Linguistic processes do not beat visuo-motor constraints, but they modulate where the eyes move regardless of word boundaries: Evidence against top-down word-based eye-movement control during reading. PLoS ONE 14(7): e0219666. https://doi.org/10.1371/journal.pone.0219666<br> </p>
Knowledge-Driven Perceptual Organization Reshapes Information Sampling Via Eye Movements – data
<p>Data from the article <strong>Knowledge-Driven Perceptual Organization Reshapes Information Sampling Via Eye Movements</strong> published in the Journal of Experimental Psychology: Human Perception and Performance by Marek A. Pedziwiatr, Elisabeth von dem Hagen, and Christoph Teufel</p> <p>Marek A. Pedziwiatr<br> marek.pedziwi@mail.com<br> January 2023</p>
A eye movement data in a virtual-reality based driving experiment
<p>This is a dataset of eye movement data in a virtual reality based driving experiment. Fourteen participants were involved in the experiment. The numbers in the file names represent the order of the participants, while the remaining words indicate the experimental scenarios: 'day' represents daytime, 'night' represents nighttime, 'duskon' represents the beginning of dusk, and 'duskoff' represents the end of dusk. However, this study did not investigate the effects of different scenarios</p>
Eye Movement Desensitization and Reprocessing (EMDR) as a Treatment of Substance Use Disorders
ClinicalTrials.gov study NCT03114423. IPD Sharing: NO. Countries: 1. Publications: 17.
Measurement of Eye Movements While Reading a German Text
ClinicalTrials.gov study NCT04642781. IPD Sharing: NO. Countries: 1. Publications: 2.
Assessment of Eye Movements in Stroke Patients and Closed-loop Intervention
ClinicalTrials.gov study NCT06965673. IPD Sharing: NO. Countries: 1. Publications: 1.
Attention and Eye Movement in Parkinson's Disease
ClinicalTrials.gov study NCT06899022. IPD Sharing: NO. Countries: 1. Publications: 98.
Neurobiological Correlates of Post Traumatic Stress Disorder (PTSD) During Rapid Eye Movement (REM) Sleep
ClinicalTrials.gov study NCT00871650. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Eye-Movement Desensitization and Post-Traumatic Syndroms
ClinicalTrials.gov study NCT03400813. IPD Sharing: YES. Countries: 1. Publications: 1.
Eye Movement Desensitization and Reprocessing (EMDR) in Non-specific Chronic Back Pain
ClinicalTrials.gov study NCT01850875. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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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.
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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.