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25 results for “Motion Perception”
Data set for article Veto, P., Einhäuser, W., & Troje, N.F. (2017). Biological motion distorts size perception. Scientific Reports, 7, 42576.
<p>In this data set you find 3 files containing data from Experiments 1, 2 & 3 of Veto P, Einhauser W & Troje NF (2017) Biological motion distorts size perception. Scientific Reports, 7, 42576; doi: 10.1038/srep42576</p> <p><br> The data are freely available for academic use only. If you use these data for a publication, please cite the aforementioned article.<br> If you have any questions regarding the data, please do not hesitate to contact Peter Veto at vettop@gmail.com</p> <p>Each row of the files contain data from one trial.<br> Columns:</p> <p>Experiment 1<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Target orientation (1: Upright; -1: Inverted)<br> 5 - Stimulus width<br> 6 - Stimulus height<br> 7 - Response width<br> 8 - Response height</p> <p>Experiment 2<br> 1-8 Same as Experiment 1<br> 9 - Condition: dynamic (1) or static (2) target</p> <p>Experiment 3<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Walker orientation<br> (1: upper walker upright, lower walker inverted;<br> 2: upper walker inverted, lower walker upright)<br> 5 - Condition<br> (1: upper target larger (21%) than lower target;<br> 2: upper target larger (10.5%) than lower target;<br> 3: target sizes are identical;<br> 4: lower target larger (10.5%) than upper target;<br> 5: lower target larger (21%) than upper target)<br> 6 - Inter stimulus interval (from end of walker presentation to onset of target circles)<br> (1: 17ms; 2: 100ms)<br> 7 - Response<br> (1: upper target was larger;<br> 2: lower target was larger)</p>
Dataset: Feedback contribution to surface motion perception in the human early visual cortex
<p><strong>Dataset</strong></p> <p>Dataset accompanying the manuscript "Feedback contribution to surface motion perception in the human early visual cortex" (<a href="https://doi.org/10.1101/653626">biorxiv</a>).</p> <p><strong>Description</strong></p> <p>fMRI data are arrange by subject (following BIDS convention). For each subject, there are subfolders for anatomical and functional MRI data.</p> <p>├── sub-01<br> │ ├── anat<br> │ │ └── ...<br> │ ├── func<br> │ │ └── ...<br> │ ├── func_se<br> │ │ └── ...<br> │ └── func_se_op<br> │ └── ...</p> <p>The subfolder 'anat' contains four images from the MP2RAGE sequence (among these, T1 and proton-density weighted images). The subfolder 'func' contains the functional data (GE EPI, T2* weighted) from the main experiment (i.e. the data from which the haemodynamic response was estimated, and on which statistical analysis was performed). The subfolders 'func_se' and 'func_se_op' contain SE EPI images with opposite phase encode polarity that were used for distortion correction. Moreover, for each image/timeseries there is a json file with metadata.</p> <p>Anatomical images have been masked anteriorly (defaced). Functional images are in coronal oblique orientation, covering early visual cortex.</p> <p>The folder 'stimuli' contains information on the stimuli used for retinotopic mapping, including timecourse models used for population receptive field mapping. (These files are included here because of their relatively large file size, which would make distribution via a git repository impractical.) The software used for the presentation of retinotopic mapping stimuli (and for the corresponding analysis) is available on <a href="https://github.com/ingo-m/pyprf">github</a>.</p> <p>For example videos of the main experimental stimuli, see <a href="https://doi.org/10.5281/zenodo.2583017">zenodo.2583017</a>. If you would like to reproduce the experimental stimuli, the respective PsychoPy code can be found on <a href="https://github.com/ingo-m/PacMan/tree/master/stimuli/experiment">github</a>.</p> <p>The exact timing of events during the experiments (rest & stimulus blocks, target events) can be found in FSL-style design matrices ("3 column format") on <a href="https://github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata">github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata</a>.</p> <p><strong>Analysis</strong></p> <p>The analysis pipeline makes use of several MRI software packages (such as SPM and FSL for preprocessing, and CBS tools for cortical depth sampling). In order to facilitate reproducibility, the entire analysis was containerised using docker. Because of licensing issues, the docker images with the third-party software cannot be directly made available. However, the docker files and detailed instructions for the creation of the docker images are available on <a href="https://github.com/ingo-m/PacMan/tree/master/docker">github</a>.</p> <p>If you would like to reproduce the analysis, the first step will be to create the docker images (which provide an exact copy of the system environment that was used to conduct the published analysis). There are two docker images, one for the main analysis (motion correction, distortion correction, GLM fitting; named "dockerimage_pacman_jessie"), and another one for the depth sampling (named "dockerimage_cbs"). Detailed instructions on how to create the docker images can be found <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_PacMan_Image_Jessie.txt">here</a> and <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_CBS_Image.txt">here</a>.</p> <p>Once you set up the docker images, the analysis can be run automatically. For each subject, there is one parent script for the main analysis (e.g. <a href="http://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_01.sh">~/analysis/20180118/metascript_01.sh</a> for subject 20180118) and a separate script for the depth sampling (e.g. <a href="https://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_03.sh">~/analysis/20180118/metascript_03.sh</a>). The only manual adjustments you should have to perform to reproduce the analysis is to change the file paths in the first section of these scripts ('pacman_anly_path' is the parent directory containing the analysis code, i.e. the git repository, and 'pacman_data_path' is the parent directory containing the MRI data). The main analysis (metascript_01.sh) should take about 24 h per subject on a workstation with 12 cores, and the depth sampling (metascript_02.sh) about 2 h. The analysis can be run on consumer-grade hardware, but some parts of the analysis may not run with less than 16 GB of RAM (recommended: 32 GB).</p> <p>Visualisations (e.g. cortical depth profiles and signal timecourses) and group-level statistical tests are implemented in <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">py_depthsampling</a>.</p> <p><strong>Further resources</strong></p> <p>Please refer to the research paper for more details: <a href="https://doi.org/10.1101/653626">https://doi.org/10.1101/653626</a></p> <p>The analysis pipeline can be found on <a href="https://github.com/ingo-m/PacMan">https://github.com/ingo-m/PacMan</a></p> <p>A separate repository contains the code used for visualisation of depth-sampling results: <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">https://github.com/ingo-m/py_depthsampling/tree/PacMan</a></p> <p>Free & open source software package for population receptive field mapping: <a href="https://github.com/ingo-m/pyprf">https://github.com/ingo-m/pyprf</a></p> <p> </p>
User Study Data for "Perception of Ultrasound Haptic Focal Point Motion"
<p>Data from two experiments about the perception of ultrasound haptic feedback.</p>
Selective postsaccadic enhncement of motion perception
<p>Dataset for article: "Selective postsaccadic enhancement of motion perception"<br> <a href="https://doi.org/10.1016/j.visres.2021.06.011">https://doi.org/10.1016/j.visres.2021.06.011</a></p>
The Role of Biological Motion in Face Perception: Social Judgements of Morphed Expressions Differ from Videos and Photographs.
<p>This study aimed to explore the impact of realistic and artifical facial dynamism on social percpetion. We compared ratings of emotion intensity and genuineness on video recordings, dynamic morphs, and static photographs of happy, sad, fearful, and angry expressions. The experiment was created using the online behavioural experiment builder Gorilla (AnwylIrvine et al., 2020). Each participant completed 144 trials, including 48 trials for each display type (video recording, morph, static), with 12 trials for each emotion (happy, disgust, fear, sadness). </p>
Dataset for finger motion and contact by a second finger influence the tactile perception of electrovibration
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Data from: Mechanism for analogous illusory motion perception in flies and humans.
<p>Visual motion detection is one of the most important computations performed by visual circuits. Yet, we perceive vivid illusory motion in stationary, periodic luminance gradients that contain no true motion. This illusion is shared by diverse vertebrate species, but theories proposed to explain this illusion have remained difficult to test. Here, we demonstrate that in the fruit fly <i>Drosophila</i>, the illusory motion percept is generated by unbalanced contributions of direction-selective neurons' responses to stationary edges. First, we found that flies, like humans, perceive sustained motion in the stationary gradients. The percept was abolished when the elementary motion detector neurons, T4 and T5, were silenced. In vivo calcium imaging revealed that T4 and T5 neurons encode the location and polarity of stationary edges. Furthermore, our proposed mechanistic model allowed us to predictably manipulate both the magnitude and direction of the fly's illusory percept by selectively silencing either T4 or T5 neurons. Interestingly, human brains possess the same mechanistic ingredients that drive our model in flies. When we adapted human observers to moving light edges or dark edges, we could manipulate the magnitude and direction of their percepts as well, suggesting that mechanisms similar to the fly's may also underlie this illusion in humans. By taking a comparative approach that exploits <i>Drosophila</i> neurogenetics, our results provide a causal, mechanistic account for a long-known visual illusion. These results argue that this illusion arises from <a>architectures</a> for motion detection that are shared across phyla.</p>
Data from Schmitt et al. 2020: A causal role of area hMST for self-motion perception in humans. Cerebral Cortex Communications
<p><strong>Dataset associated with the following publication:</strong></p> <p>Constanze Schmitt, Bianca R Baltaretu, J Douglas Crawford, Frank Bremmer, A Causal Role of Area hMST for Self-Motion Perception in Humans, <em>Cerebral Cortex Communications</em>, Volume 1, Issue 1, 2020, tgaa042, <a href="https://doi.org/10.1093/texcom/tgaa042">https://doi.org/10.1093/texcom/tgaa042</a> Published 2020 July 20.</p> <p><strong>Description of dataset:</strong></p> <p>In our study we presented an optic flow stimulus simulating forward self-motion across a ground plane in one of three directions (30° to the left, straight ahead, 30° to the right) to eight human participants. In 57% of all trials TMS pulses were applied either to right hemisphere hMST or a control area while the optic flow stimulus was presented. Participants indicated the perceived heading of the self-motion stimulus.</p> <p>The dataset contains the responses (perceived headings) of all eight participants (s1 to s8). The responses are reported here in degrees with 0 degrees representing a movement straight ahead, negative values describing movements forward to the left and positive values describing movements forward to the right.</p> <p> </p>
The Effect of Exercise on Motion Perception and Fear in Low Back Pain
ClinicalTrials.gov study NCT05433311. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Self-motion Perception in Parkinson's Disease
ClinicalTrials.gov study NCT03137238. IPD Sharing: NO. Countries: 1. Publications: 27.
Data from: Mechanism for analogous illusory motion perception in flies and humans.
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Data from: TMS reveals flexible use of form and motion cues in biological motion perception
The perception of human movement is a key component of daily social interactions. Although extrastriate area MT+/V5 is closely associated with motion processing, its role in the processing of sparse 'biological motion' displays is still unclear. We developed two closed matched psychophysical tasks to assess simple coherent motion perception and biological motion perception, and measured changes in performance caused by application of TMS over MT+/V5. Performance of the simple motion discrimination task was significantly depressed by TMS stimulation, and highly correlated within observers in TMS conditions, but there was no significant decrement in performance of the biological motion task, despite low intra-observer correlations across TMS conditions. We conclude that extrastriate area MT+/V5 is an obligatory waypoint in the neural processing of simple coherent motion, but is not obligatory for the processing of biological motion. Results are consistent with a dual neural processing route for biological motion processing.
Data from: Audiovisual synchrony perception in observing human motion to music
To examine how individuals perceive synchrony between music and body motion, we investigated the characteristics of synchrony perception during observation of a Japanese Radio Calisthenics routine. We used the constant stimuli method to present video clips of an individual performing an exercise routine. We generated stimuli with a range of temporal shifts between the visual and auditory streams, and asked participants to make synchrony judgments. We then examined which movement-feature points agreed with music beats when the participants perceived synchrony. We found that extremities (e.g., hands and feet) reached the movement endpoint or moved through the lowest position at music beats associated with synchrony. Movement onsets never agreed with music beats. To investigate whether visual information about the feature points was necessary for synchrony perception, we conducted a second experiment where only limited portions of video clips were presented to the participants. Participants consistently judged synchrony even when the video image did not contain the critical feature points, suggesting that a prediction mechanism contributes to synchrony perception. To discuss the meaning of these feature points with respect to synchrony perception, we examined the temporal relationship between the motion of body parts and the ground reaction force (GRF) of exercise performers, which reflected the total force acting on the performer. Interestingly, vertical GRF showed local peaks consistently synchronized with music beats for most exercises, with timing that was closely correlated with the timing of movement feature points. This result suggests that synchrony perception in humans is based on some global variable anticipated from visual information, instead of the feature points found in the motion of individual body parts. In summary, the present results indicate that synchrony perception during observation of human motion to music depends largely on spatiotemporal prediction of the performer's motion.
Data from: Decision-level adaptation in motion perception
Prolonged exposure to visual stimuli causes a bias in observers' responses to subsequent stimuli. Such adaptation-induced biases are usually explained in terms of changes in the relative activity of sensory neurons in the visual system which respond selectively to the properties of visual stimuli. However, the bias could also be due to a shift in the observer's criterion for selecting one response rather than the alternative; adaptation at the decision level of processing rather than the sensory level. We investigated whether adaptation to implied motion is best attributed to sensory-level or decision-level bias. Three experiments sought to isolate decision factors by changing the nature of the participants' task while keeping the sensory stimulus unchanged. Results showed that adaptation-induced bias in reported stimulus direction only occurred when the participants' task involved a directional judgement, and disappeared when adaptation was measured using a non-directional task (reporting where motion was present in the display, regardless of its direction). We conclude that adaptation to implied motion is due to decision-level bias, and that a propensity towards such biases may be widespread in sensory decision-making.
Self-Motion Perception in the Elderly
<p>Self-motion through space generates a visual pattern called optic flow. It can be used to determine one’s direction of self-motion (heading). Previous studies have already shown that this perceptual ability, which is of critical importance during everyday life, changes with age. In most of these studies subjects were asked to judge whether they appeared to be heading to the left or right of a target. Thresholds were found to increase continuously with age. In our current study, we were interested in absolute rather than relative heading judgements and in the question about a potential neural correlate of an age-related deterioration of heading perception. Two groups, older test subjects and younger controls, were shown optic flow stimuli in a virtual-reality setup. Visual stimuli simulated self-motion through a 3-D cloud of dots and subjects had to indicate their perceived heading direction after each trial. In different subsets of experiments we varied individually relevant stimulus parameters: presentation time, number of dots in the display, stereoscopic vs. non-stereoscopic stimulation, and motion coherence. We found decrements in heading performance with age for each stimulus parameter. In a final step we aimed to determine a putative neural basis of this behavioural decline. To this end we modified a neural network model which previously has proven to be capable of reproduce and predict certain aspects of heading perception. We show that the observed data can be modeled by implementing an age related neuronal cell loss in this neural network. We conclude that a continuous decline of certain aspects of motion perception, among them heading, might directly be based on an age-related progressive loss of groups of neurons being activated by visual motion. </p>
Data from: Decision-level adaptation in motion perception
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Data from: Lesions to right posterior parietal cortex impair visual depth perception from disparity but not motion cues
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Data from: TMS reveals flexible use of form and motion cues in biological motion perception
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Data from: Do perceptual biases emerge early or late in visual processing? Decision-biases in motion perception
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Data from: Audiovisual synchrony perception in observing human motion to music
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International Brain Laboratory public data
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OpenNeuro
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