1,720,972 research outputs found
Dynamic re-calibration of perceived size in fovea and periphery through predictable size changes
Full text link<p>Dataset relative to the following publication:</p>
<p>Valsecchi, M. & Gegenfurtner, K.R (2016). Dynamic re-calibration of perceived size in fovea and periphery through predictable size changes. Current Biology, 26, 59-63.</p>
<p>Each folder contains the data relative to one experiment and the script that was used to generate them. </p>
<p>Each data folder contains a description of the data format which should be sufficient to replicate the analyses as performed in the paper. </p>
<p>Additional info can be deducted fromt he experimental scripts.</p>
Saccadic suppression measured by steady-state visual evoked potentials
Full text link<p>Dataset related to the following publication:</p>
<p>Chen, J., Valsecchi, M. & Gegenfurtner, K.R. (2019). Saccadic suppression measured by steady-state visual evoked potentials. <em>Journal of Neurophysiology, DIO: 10.1152/jn.00712.2018</em></p>
<p>Please refer to "data description.txt" for details about the data. </p>
Haptic Saliency Model for Rigid Textured Surfaces
No full text<p>When touching an object, we focus more on some of its parts rather than touching the whole object’s surface, i.e. some parts are more salient than others. Here we investigated how different physical properties of rigid, plastic, relieved textures determine haptic exploratory behavior. We produced haptic stimuli whose textures were locally defined by random distributions of four independent features: amplitude, spatial frequency, orientation and isotropy. Participants explored two stimuli one after the other and in order to promote exploration we asked them to judge their similarity. We used a linear regression model to relate the features and their gradients to the exploratory behavior (spatial distribution of touch duration). The model predicts human behavior significantly better than chance, suggesting that exploratory movements are to some extent driven by the low level features we investigated. Remarkably, the contribution of each predictor changed as a function of the spatial scale in which it was defined, showing that haptic exploration preferences are spatially tuned, i.e. specific features are most salient at different spatial scales.</p>
<p>Metzger, A., Toscani, M., Valsecchi, M. & Drewing, K. (2018) Haptic saliency model for rigid textured surfaces. In Prattichizzo, D., Shinoda, H., Tan, H. Z., Ruffaldi, E. & Frisoli, A. (Eds.), Haptics: Science, Technology, and Applications, 11th International Conference, EuroHaptics 2018, Pisa, Italy, June 13-16, 2018, Proceedings, Part I (pp. 389–400). Springer International Publishing, Cham.</p>
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<p>Data of the experiment is stored in a zip file, containing all data relative to the publication. The 'movement' folder containes participnts' movement data. The 'stimuli' folder containes the 2D and 3D models of the stimuli. </p>
<p>Explanaition and coding of the data is provided in the file VARIABLE_CODES.txt.</p>
Healthy Aging Is Associated With Decreased Risk-Taking in Motor Decision-Making
No full text<p>The Zip file contains all data relative to the publication.</p>
<p>Data files are identified as follows:</p>
<p>Part[Y/O for Younger/Older]Sess[Session Number]</p>
<p>A description of the variables is contained in the file VARIABLE CODES.txt</p>
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Average performance in terms of percentage correct responses and d` in Experiment 2, as a function of Display Type.
No full text<p>Error bars are between-observer 95% confidence intervals of the mean. The 6% increase in the rate of correct responses with 3D presentation was statistically significant.</p
Experimental procedure in the Learning session (A) and in the Recognition session (B) of Experiment 3.
No full text<p>The learning session was identical to the one of Experiment 1 (with 1 s exposure). In the Recognition session observer indicated the type of the display in which the image had been presented in the previous session by choosing the corresponding target. For each observer the 3D version of the image was always presented on the same side of the screen.</p
Average Hit Rate as a function of Display Type, Picture Category and Exposure Time in Experiment 1.
No full text<p>Correct recognitions of old pictures are classified as Hits. The Blue and purple brackets indicate data from the Short Exposure and Long Exposure groups, respectively. Gray bars represent the corresponding average False Alarm Rate. Incorrect recognitions of new pictures are classified as False Alarms. Error bars are between-observer 95% confidence intervals of the mean.</p
Average d′ (red and green bars) and c (gray bars) values as a function of Display Type in the two Groups.
No full text<p>Error bars are between-observer 95% confidence intervals of the mean. Sensitivity is not influenced by stereo presentation but increases significantly with longer exposure. In both groups observers are biased not to report 3D presentation (c values are on average negative).</p
Experimental procedure in the Learning session (A) and in the Recognition session (B) of Experiment 2.
No full text<p>In the Recognition session observer indicated which of the two images had been presented in the previous session (“old” picture). Target images were always paired with a distractor from the same category.</p
Results from Experiment 3 in terms of Hit and False Alarm rates (A) and of Sensitivity and Criterion (B).
No full text<p>Hits are defined as the correct indication that an image had been presented in 3D. Data are presented separately for each category of pictures (colored bars) and for the overall data (black bars). Error bars represent between-observer 95% confidence intervals of the mean. Sensitivity (d`) did not differ from 0 in any of the single categories nor in the overall data. Observers were biased to report that car and building images had been presented in 3D, whereas they tended to report that forest images had been presented in 2D.</p
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