180 research outputs found
EEG_SpeechCue
No full textData and scripts for: Ivanova, M., Neubert, C. R., Schmied, J., & Bendixen, A. (2023). ERP evidence for Slavic and German word stress cue sensitivity in English. Frontiers in Psychology, 14, 1193822. https://doi.org/10.3389/fpsyg.2023.1193822
Preprint: Ivanova, M., Neubert, C.R., Schmied, J., and Bendixen, A. (2023). ERP evidence for Slavic and German word stress cue sensitivity in English. PsyArXiv [Preprint]. https://doi.org/10.31234/osf.io/hqr34
Contributors to this project are Marina Ivanova, Christiane R. Neubert, Josef Schmied, and Alexandra Bendixen. Due to a technical error, Alexandra Bendixen was not part of the contributor list from 2023-03-24 to 2023-04-03
EEG_SpeechCue
No full textData and scripts for: Ivanova, M., Neubert, C. R., Schmied, J., & Bendixen, A. (2023). ERP evidence for Slavic and German word stress cue sensitivity in English. Frontiers in Psychology, 14, 1193822. https://doi.org/10.3389/fpsyg.2023.1193822
Preprint: Ivanova, M., Neubert, C.R., Schmied, J., and Bendixen, A. (2023). ERP evidence for Slavic and German word stress cue sensitivity in English. PsyArXiv [Preprint]. https://doi.org/10.31234/osf.io/hqr34
Contributors to this project are Marina Ivanova, Christiane R. Neubert, Josef Schmied, and Alexandra Bendixen. Due to a technical error, Alexandra Bendixen was not part of the contributor list from 2023-03-24 to 2023-04-03
Realistic Avatar Gaze Based on Human Eye- and Head-Tracking Data
No full textSupplemental Data for
Jochen Miksch, Sascha Feder, Alexandra Bendixen and Wolfgang Einhäuser (2025). "Realistic Avatar Gaze Based on Human Eye- and Head-Tracking Data" GI Workshop 2025 on Virtual and Augmented Reality, Chemnitz Sep 16-17, 2025. [poster contribution
Supplemental Data for "Realistic Avatar Gaze Based on Human Eye- and Head-Tracking Data"
No full textComponent of the Supplemental Data for Jochen Miksch, Sascha Feder, Alexandra Bendixen and Wolfgang Einhäuser (2025). "Realistic Avatar Gaze Based on Human Eye- and Head-Tracking Data" GI Workshop 2025 on Virtual and Augmented Reality, Chemnitz Sep 16-17, 2025. [poster contribution
Sensorimotor adaptation impedes perturbation detection in grasping
No full textData for: Müller, C., Bendixen, A., & Kopiske, K. (2024). Sensorimotor adaptation impedes perturbation detection in grasping. Psychonomic Bulletin & Review. https://doi.org/10.3758/s13423-024-02543-
Dataset supplementing the publication Einhäuser, W., Thomassen, S., & Bendixen, A. (2017). Using binocular rivalry to tag foreground sounds: towards an objective visual measure for auditory multistability. Journal of Vision, 17:34, 1-19.
No full text<p>These files supplement the publication Einhäuser, W., Thomassen, S., & Bendixen, A. (2017). Using binocular rivalry to tag foreground sounds: towards an objective visual measure for auditory multistability. Journal of Vision, 17:34, 1-19. The data are free for scientific use, provided this reference is appropriately cited.</p>
<p>exp1_data.mat contains all the data of experiment 1 as cell arrays of size 8x16x8 (subject x block x trial) or 8x16 (subject x block). Specifically:<br>
xEye: the horizontal eye position in raw (pixel coordinates)<br>
gain: the OKN slow phase gain computed from the xEye data as described in the paper; in audio-visual blocks the sign is chosen such that positive gain corresponds to the direction of the grating associated with the low tone; in unambiguous visual blocks (1,16) positive sign corresponds to the direction of the grating.<br>
ixLow, ixHigh, ixNone, ixBoth: indices for xEye and gain of the same subject and block for which the button corresponding to the low tone, the high tone, both buttons or no button was pressed.</p>
<p>exp2_data.mat and exp3_data.mat contain the data of experiment 2 and experiment 3, respectively, and are organized analogously to exp1_data.mat.</p>
<p>figure3.m through figure6.m use these data to plot the respective paper figures to exemplify usage of the data.</p>
<p> </p>
Raising a Child With Congenital Muscular Dystrophy: Impact on the Family
No full textAbstract
Date Presented 3/31/2017
This study examines the impact on families of raising children with congenital muscular dystrophy. The findings expand the understanding of challenges they face and contribute to an evidence-based approach for families.
Primary Author and Speaker: Yoonjeong Lim
Additional Authors and Speakers: Consuelo Kreider, Roxanna Bendixen</jats:p
Open Data for: Bimodal moment-by-moment coupling in perceptual multistability
No full textData and analysis scripts for: Grenzebach, J., Wegner, T.G.G., Einhäuser, W., & Bendixen, A. (2024). Bimodal moment-by-moment coupling in perceptual multistability. Journal of Vision, 24(5), article 16. https://doi.org/10.1167/jov.24.5.1
Sound predictability as a higher-order cue in auditory scene analysis
No full textA major challenge for the auditory system is to disentangle signals emitted by two or more sound sources that are active in a temporally interleaved manner (sequential stream segregation). Besides distinct characteristics of the individual signals (e.g., their timbre, location, and pitch), one important cue for distinguishing the sound sources is how their emitted signals unfold over time. It seems intuitively plausible that signals that unfold predictably with respect to their acoustic features and time-points of occurrence, such as the repetitive signature of a train moving on the rails, can be more readily identified as originating from one sound source. Based on this rationale, predictive elements have successfully been incorporated into computational models of auditory scene analysis for many years
Dataset supplementing the article Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129.
No full text<p>This dataset supplements the publication<br>
Einhäuser, W., Methfessel, P., & Bendixen, A. (2017). Newly acquired audio-visual associations bias perception in binocular rivalry. Vision Research, 133, 121-129. doi: 10.1016/j.visres.2017.02.001</p>
<p>Use is free for scientific purposes, provided the aforementioned reference is appropriately cited.<br>
Description of files<br>
- conditionsByObserver.csv<br>
contains for each of the 16 observers the color and grating direction that had been coupled to either the low-pitch or the high-pitch tone<br>
column 1: observer number<br>
column 2: color associated with low-pitch tone<br>
column 3: color associated with high-pitch tone<br>
column 4: drift direction associated with low-pitch tone<br>
column 5: drift direction associated with high-pitch tone</p>
<p>- conditionsByObserver.mat contains the same information as matlab variables (as four vectors/cell arrays with one entry per observer)</p>
<p>- toneByBlockAndTrial.csv<br>
contains the conditions for all 18 rivalry trials (6 rivalry blocks with 3 trials each) for each observer<br>
column 1: observer number<br>
column 2: block number<br>
column 3: trial number<br>
column 4: tone (low [pitch], high [pitch], none) played in this trial<br>
Note that due to a technical error for observer #16, block 6 was presented first, followed by 1,2,3,4,5; for all other observers blocks were used in the order given (1,2,3,4,5,6).</p>
<p>- toneByBlockAndTrial.mat contains the same information as a 16x6x3 matrix named toneByBlockAndTrial ; tones are coded numerically (1-low pitch,2-high pitch,3-none)</p>
<p>- eyeTraces.mat contains three cell arrays of dimensions 16x6x3 (observer x rivalry block x rivalry trial) called xEye, oknGain, and timeSinceTrialStart;</p>
<p>o each entry of xEye contains the horizontal eye position for<br>
the respective trial in eye-tracker coordinates (which correspond to screen pixels, except that (1/1) is the upper right rather than the upper left and values increase from right to left due to the setup configuration)</p>
<p>o oknGain contains the gain computed from these eye positions.</p>
<p>o timeSinceTrialStart contains the time in seconds since onset of the trial</p>
<p><br>
For all variables, the sampling rate is 500 Hz, in eye-tracker coordinates the speed of the grating is 240 units/ms. Blinks were removed from both eye-data variables, fast-phases were removed from the gain data. Removed data were set to NaN in eye-data variables.</p>
<p>- Matlab functions figure1d.m, figure 2.m, figure3.m and figure4.m compute raw versions of the aforementioned paper's figures from the datafiles to exemplify their usage.</p>
<p>[Note: In the originally published version of the article, the first two means and their standard errors of section 3.3 were stated incorrectly. All figures and statistical analyses are based on the correct data].</p>The work was supported by the German Research Foundation (DFG) through SFB/TRR-135 (B4)
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