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14 results for “Smooth Pursuit”
Data from Churan et al. 2018 Comparison of the precision of smooth pursuit in humans and head unrestrained monkeys
<p>Experiments were performed in two rhesus monkeys, B and E. Each monkey made a combination of slow and fast eye-movements following a visual target. The target was stationary at first and then either abruptly started moving (at a speed of 10°/s) in a certain direction (Ramp paradigm) or made a Step before starting the motion (Step-Ramp paradigm). By using a specific Step size and Step direction the initial saccade was eliminated in the Step-Ramp paradigm. The direction of the stimulus motion was predominantly horizontal with a smaller vertical component that was systematically varied between 0° and +-20°. We investigated how precisely the monkeys can follow this vertical component of the stimulus motion.</p> <p><strong>The files:</strong></p> <p>There are separate files for each monkey (B and E) and paradigm (Ramp and Step_Ramp). The files are MATLAB data files.</p> <p>Ramp:</p> <p>Each file consists of three variables – ‘alldatx’, ‘alldaty’, and ‘init’.</p> <p>‘alldatx’ and ‘alldaty’ are cell arrays in which each cell represents one vertical component of the stimulus: 1=20° up, 2=10° up, 3=5° up, 4=2° up, 5=0° , 6=2° down, 7=5° down, 8=10° down, 9=20° down. Each cell contains a matrix of n x 2001 elements. Each row represents the eye velocities during one individual trial between 1000 ms before and 1000 ms after the start of stimulus motion with a sampling rate of 1000 Hz.</p> <p>‘init’ is a cell array in which each cell represents one vertical component of the stimulus (s. above). Each cell contains a structure array which shows the approximate properties of the initial saccade in each trial:</p> <p>‘init.t’: Start end end time of the saccade (in ms) after the start of stimulus motion.</p> <p>‘init.amp’: Amplitude of the initial saccade in deg</p> <p>‘init.startpos’, ‘init.endpos’: start- and end-position (x, y) of the saccade</p> <p>Step_Ramp:</p> <p>Each file consists of two variables – ‘alldatx’, ‘alldaty’. Description is the same as for Ramp.</p>
LRP predicts smooth pursuit eye movement onset during the ocular tracking of self-generated movements
<p>Dataset relative to the following publication:</p> <p>Chen, J., Valsecchi, M. & Gegenfurtner, K.R. (2016). LRP predicts smooth pursuit eye movement onset during the ocular tracking of self-generated movements. <em>Journal of Neurophysiology, </em>in press</p> <p>Each folder contains the data relative to one experiment and the script that was used to generate them. Please refer to "Description on data format.txt" for the usage of the data.</p> <p>Additional information can be deducted from the experimental scripts.</p>
Visual sensitivity for luminance and chromatic stimuli during the execution of smooth pursuit and saccadic eye movements
<p>Dataset relative to the following publication:</p> <p>Braun, D. I., Schütz, A. C., & Gegenfurtner, K. R. (2017). Visual sensitivity for luminance and chromatic stimuli during the execution of smooth pursuit and saccadic eye movements. Vision Research</p>
Discrimination of curvature from motion during smooth pursuit eye movements and fixation
<p>One folder contains all the data for the main experiment. In the folder you can find a file dataREADME, which explains the format and the content of the individual files. A second folder contains the data files for the additional control experiment with shorter presentation durations. The structure of the data is the same. It includes two folders. One which constains the perceptual data for the shorter presentation durations and one with eye traces of the same participants for the oculometric thresholds. The name of the folder indicates also the length of the presentation duration, either 150 or 300 ms.</p>
Dynamic integration of information about salience and value for smooth pursuit eye movements
<p>Dataset from the following publication:</p> <p>Schütz, A. C., Lossin, F., & Gegenfurtner, K. R. (2015). Dynamic integration of information about salience and value for smooth pursuit eye movements. <em>Vision Research, 113</em>, 169-178. <a>doi:10.1016/j.visres.2014.08.009 <span></span></a><a></a> .</p>
Analysis of Smooth Pursuit Eye Movements in Clinical Context by Tracking the Target and Eyes
<p>Eyemove dataset obtained at Teikyo University.</p> <p>If you use the dataset, please state clearly that you have used our data.</p> <p>The mp4 files are the video of the examination.<br> Excel files are the position of the optic disc analyzed by SSD and the ocular position data analyzed by VOG.</p>
Saccades Exert Spatial Control of Motion Processing for Smooth Pursuit Eye Movements
<p>Saccades modulate the relationship between visual motion and smooth eye movement. Before a saccade, pursuit eye movements reflect a vector average of motion across the visual field. After a saccade, pursuit primarily reflects the motion of the target closest to the endpoint of the saccade. We tested the hypothesis that the saccade produces a spatial weighting of motion around the endpoint of the saccade. Using a moving pursuit stimulus that stepped to a new spatial location just before a targeting saccade, we controlled the distance between the endpoint of the saccade and the position of the moving target. We demonstrate that the smooth eye velocity following the targeting saccade weights the presaccadic visual motion inputs by the distance from their location in space to the endpoint of the saccade, defining the extent of a spatiotemporal filter for driving the eyes. The center of the filter is located at the endpoint of the saccade in space, not at the position of the fovea. The filter is stable in the face of a distracter target, is present for saccades to stationary and moving targets, and affects both the speed and direction of the postsaccadic eye movement. The spatial filter can explain the target-selecting gain change in postsaccadic pursuit, and has intriguing parallels to the process by which perceptual decisions about a restricted region of space are enhanced by attention. The effect of the spatial saccade plan on the pursuit response to a given retinal motion describes the dynamics of a coordinate transformation.</p>
Data from: Predicted tracking error triggers catch-up saccades during smooth pursuit
For foveated animals, visual tracking of moving stimuli requires the synergy between saccades and smooth pursuit eye movements. Deciding to trigger a catch-up saccade during pursuit influences the quality of visual input. This decision is a trade-off between tolerating sustained position error when no saccade is triggered or a transient loss of vision during the saccade due to saccadic suppression. Although catch-up saccades have been extensively investigated, it remains unclear how the trigger decision is made by the brain. de Brouwer et al (2002) demonstrated that catch-up saccades were less likely to occur when the expected time to foveate a target using pursuit alone is between 40 and 180ms into the future, referred to as the smooth zone. However, this descriptive result lacks a mechanistic explanation for how the trigger decision is made. More recently, we proposed a decision model (Coutinho et al., 2018) that relies on a probabilistic estimation of predicted position error (PEpred) during visual tracking. To test the model predictions, we investigated how human participants combined predicted position error, retinal slip, and the uncertainty in those estimates to make trigger decisions. We found a significant effect of the pre-saccadic magnitude of PEpred on trigger time and occurrence of catch-up saccades. To test the role of uncertainty, we blurred the moving target which led to longer and more variable saccade trigger times and more smooth pursuit trials, consistent with model predictions. As predicted by our model, large PEpred (>10deg) produced early saccades regardless of the level of uncertainty while saccades preceded by small PEpred (<10deg) were significantly modulated by high uncertainty. Our model also predicted increased signal dependent noise as retinal slip increases, which resulted in longer saccade trigger times and more smooth trials. In conclusion, the data supports our hypothesized role of PEpred in deciding when to trigger a catch-up saccade during smooth pursuit while taking into account uncertainty in sensory estimates.
Attention is allocated closely ahead of the target during smooth pursuit eye movements: evidence from EEG frequency tagging
<p>Dataset relative to the following publication:</p> <p>Chen, J., Valsecchi, M. & Gegenfurtner, K.R. (2017). Attention is allocated closely ahead of the target during smooth pursuit eye movements: Evidence from EEG frequency tagging. <strong>Neuropsychologia</strong>, doi: 10.1016/j.neuropsychologia.2017.06.024.</p> <p>Please refer to "data description.txt" in each experiment for details about the data. </p>
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>
Data from: Predicted tracking error triggers catch-up saccades during smooth pursuit
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Spatial localization during open-loop smooth pursuit
<p><strong>Introduction:</strong> Numerous previous studies have shown that eye movements induce errors in the localization of briefly flashed stimuli. Remarkably, the error pattern is indicative of the underlying eye movement and the exact experimental condition. For smooth pursuit eye movements (SPEM) and the slow phase of the optokinetic nystagmus (OKN), perceived stimulus locations are shifted in the direction of the ongoing eye movement, with a hemifield asymmetry observed only during SPEM. During the slow phases of the optokinetic afternystagmus (OKAN), however, the error pattern can be described as a perceptual expansion of space. Different from SPEM and OKN, the OKAN is an open-loop eye movement.</p> <p><strong>Methods:</strong> Visually guided smooth pursuit can be transformed into an open–loop eye movement by briefly blanking the pursuit target (gap). Here, we examined flash localization during open-loop pursuit and asked, whether localization is also prone to errors and whether these are similar to those found during SPEM or during OKAN. Human subjects tracked a pursuit target. In half of the trials, the target was extinguished for 300 ms (gap) during the steady–state, inducing open–loop pursuit. Flashes were presented during this gap or during steady–state (closed–loop) pursuit.</p> <p><strong>Results:</strong> In both conditions, perceived flash locations were shifted in the direction of the eye movement. The overall error pattern was very similar with error size being slightly smaller in the gap condition. The differences between errors in the open- and closed-loop conditions were largest in the central visual field and smallest in the periphery.</p> <p><strong>Discussion:</strong> We discuss the findings in light of the neural substrates driving the different forms of eye movements.</p>
Data from Dowiasch et al. (2020) Nonretinocentric Localization of Successively Presented Flashes During Smooth Pursuit Eye Movements
<p>Dataset associated with the following publication:</p> <p>Dowiasch S, Meyer-Stender S, Klingenhoefer S, Bremmer F. Nonretinocentric localization of successively presented flashes during smooth pursuit eye movements. <em>J Vis</em>. 2020;20(4):8. doi:10.1167/jov.20.4.8</p>
Assessing the Reliability of Smooth Pursuit Across Various Neck Postures Using a Custom Ocular Motor Detection System
ClinicalTrials.gov study NCT06634576. IPD Sharing: YES. Countries: 1. Publications: 0.
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