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426 results for “Stimuli”
Lingering representations of stimuli influence recall organization
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Phenotypic modulation of biofilm formation in a Staphylococcus epidermidis orthopedic clinical isolate grown under different mechanical stimuli: contribution from a combined proteomic study
<p>One of the major causes of prosthetic joint failure is infection. Recently, coagulase negative <em>Staphylococcus epidermidis</em> has been identified as an emergent, nosocomial pathogen involved in subclinical prosthetic join infections (PJIs). The diagnosis of PJIs mediated by <em>S. epidermidis</em> is usually complex and difficulties due to the absence of acute clinical signs derived from the host immune system response. Therefore, analysis of protein patterns in biofilm-producing <em>S. epidermidis</em> allows for the examination of the molecular basis of biofilm formation. Thus, in the present study, the proteome of a clinical isolate <em>S. epidermidis</em> was analyzed when cultured in its planktonic or sessile form to examine protein expression changes depending on culture conditions. After 24 hours of culture, sessile bacteria exhibited increased gene expression for ribosomal activity and for expression of proteins related to the initial attachment phase, involved in the capsular polysaccharide/adhesin, surface associated proteins and peptidoglycan biosynthesis. Likewise, planktonic <em>S. epidermidis</em> was able to aggregate after 24 hours, synthesizing the accumulation associate protein and cell-wall molecules through the activation of the YycFG and ArlRS, two component regulatory pathways. Prolonged culture under vigorous agitation generated a stressful growing environment triggering aggregation in a biofilm-like matrix as a mechanism to survive harsh conditions.</p> <p>Further studies will be essential to support these findings in order to further delineate the complex mechanisms of biofilm formation of <em>S. epidermidis</em> and they could provide the groundwork for the development of new drugs against biofilm-related infections, as well as the identification of novel biomarkers of subclinical or chronic infections mediated by these emerging, low virulence pathogens.</p>
Top-down control of saccades requires inhibition of suddenly appearing stimuli
<p>Data from:</p> <p>Wolf, C. & Lappe, M. (202x). Top-down control of saccades requires inhibition of suddenly appearing stimuli.</p> <p>For each of the six experiments the data set can be found in the corresponding txt file (e.g. e1.txt for experiment 1). Labels of the columns can be found in ColumnLabels.pdf</p> <p>For experiments 1-4 the same set of participants has been recorded and identical participant numbers refer to the same individual. For each of the last two experiments, experiment 5 and 6, a new set of participants has been recorded and identical participant numbers do not refer to the same individual from other experiments.</p> <p>For questions please contact chr.wolf[at]wwu.de</p>
Burst timing determines perceived consonant order in the McGurk combination effect (Audiovisual stimuli)
<p>The file “AV_stimuli.zip” contains the audiovisual speech stimuli generated by a native French talker for the study “Burst timing determines perceived consonant order in the McGurk combination effect.”</p> <p>The stimuli were created by splitting the acoustic component of McGurk combination stimuli into two sequences. One sequence contained only the vowels /i_i/ (where the underscore represents a 300 ms intersyllabic pause). The other sequence contained the release burst and aspiration extracted from a natural articulation of /iki/. An auditory (A) continuum was then created by adding the burst and aspiration to the vowels at nine different temporal alignments (step size of 50 ms). At 0 ms, the midpoint of the burst coincided with the midpoint of the intersyllabic pause. At the extremes, -200 and 200 ms, the burst and aspiration almost completely overlapped with the initial or final vowel, respectively. Two audiovisual (AV) continua were then created by pairing the acoustic continuum with either visual (V) /ip_i/ or /i_pi/.</p> <p><strong>Filename list:</strong></p> <p><strong>s5:</strong>congruent<strong>AV</strong>/i_i/</p> <p><strong>s6-s14: </strong>AV continuum, <strong>V</strong>/ip_i/ <strong>+</strong><strong>A</strong>/i_i/ <strong>&</strong>burst paired at different asynchronies, -200, -150, -100, -50, 0, 50, 100, 150 and 200 ms, respectively.</p> <p><strong>s15-s23: </strong>AV continuum, <strong>V</strong>/i_pi/ <strong>+</strong><strong>A</strong>/i_i/ <strong>&</strong>burst paired at different asynchronies, -200, -150, -100, -50, 0, 50, 100, 150 and 200 ms, respectively.</p> <p><strong>S27-s35: </strong>A continuum, <strong>A</strong>/i_i/ <strong>&</strong>burst paired at different asynchronies, -200, -150, -100, -50, 0, 50, 100, 150 and 200 ms, respectively.</p>
Noise stimuli, particiants with dyslexia (ACI experiment)
<p>Noise stimuli involved in the Auditory Classification Image experiment (gaussian noise). 10.000 stimuli for each participant. wav format, 48 kHz</p>
Noise stimuli (ACI experiment)
<p>Noise stimuli involved in the Auditory Classification Image experiment (gaussian noise). 10.000 stimuli for each participant. wav format, 48 kHz</p>
Familiar Environmental Sound Test -- Stimuli
<p>This zip file includes environmental sound stimuli that comprise Familiar Environmental Sound Test. There are 25 individual sounds for FEST-I. These sounds are composed into FEST-S which consists of two sets of 5 semantically coherent and 5 semantically incoherent sequences, five sounds in each sequence. These sequences are used to detect semantic context effects in environmental sound perception. Two practice sequences are also included.</p>
Doll-Human Morph Stimuli
<p>This stimulus set contains images of human faces (from the Radboud Faces Database; Langner et al., 2010) morphed with dolls. Morph continua are provided at 10% intervals (11 total images per morph) and 2% intervals (50 total images per morph). 19 total morphs are provided in the stimulus set, comprising 4 female faces with neutral expressions (labelled FN), 6 female faces with happy expressions (FH), 6 male faces with neutral expressions (MN) and 3 male faces with happy expressions (MH). All faces are Caucasian, and are photographed as a frontal view, with external features covered by an oval frame.</p> <p>For more information about this stimulus set, please contact n.bowling@gold.ac.uk</p> <p>Radboud Faces Database:</p> <p>Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D. H. J., Hawk, S. T., & van Knippenberg, A. (2010). Presentation and validation of the Radboud Faces Database. <em>Cognition & Emotion, 24</em>(8), 1377-1388.</p>
Computer Generated-Human Morph Stimuli
<p>This stimulus set contains images of human faces (from the Radboud Faces Database; Langner et al., 2010) morphed with computer-generated versions of the same faces, made with FaceGen Modeller (Singular Inversions, Toronto, Canada). Morph continua are provided at 10% intervals (11 total images per morph) and 2% intervals (50 total images per morph). 16 total morphs are provided in the stimulus set, comprising 4 female faces with neutral expressions (labelled FN), 4 female faces with happy expressions (FH), 4 male faces with neutral expressions (MN) and 4 male faces with happy expressions (MH). All faces are Caucasian, and are photographed as a frontal view, with external features covered by an oval frame.</p> <p>For more information about this stimulus set, please contact n.bowling@gold.ac.uk</p> <p>Radboud Faces Database:</p> <p>Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D. H. J., Hawk, S. T., & van Knippenberg, A. (2010). Presentation and validation of the Radboud Faces Database. <em>Cognition & Emotion, 24</em>(8), 1377-1388.</p>
Dataset supporting "Violating instructed human agency: an fMRI study on oculomotor tracking of biological and nonbiological motion stimuli."
<p>Here we provide fMRI data used for the following project (for details see data description file): Gertz, H., Hilger, M., *Hegele, M., & *Fiehler, K. (2016). Violating instructed human agency: an fMRI study on oculomotor tracking of biological and nonbiological motion stimuli. Neuroimage, doi: 10.1016/j.neuroimage.2016.05.043. (*shared last authorship)</p> <p> </p> <p>Previous studies have shown that beliefs about the human origin of a stimulus are capable of modulating the coupling of perception and action. Such beliefs can be based on top-down recognition of the identity of an actor or bottom-up observation of the behavior of the stimulus. Instructed human agency has been shown to lead to superior tracking performance of a moving dot as compared to instructed computer agency, especially when the dot followed a biological velocity profile and thus matched the predicted movement, whereas a violation of instructed human agency by a nonbiological dot motion impaired oculomotor tracking (Zwickel et al., 2012). This suggests that the instructed agency biases the selection of predictive models on the movement trajectory of the dot motion. The aim of the present fMRI study was to examine the neural correlates of top-down and bottom-up modulations of perception–action couplings by manipulating the instructed agency (human action vs. computer-generated action) and the observable behavior of the stimulus (biological vs. nonbiological velocity profile). To this end, participants performed an oculomotor tracking task in an MRI environment. Oculomotor tracking activated areas of the eye movement network. A right-hemisphere occipito-temporal cluster comprising the motion-sensitive area V5 showed a preference for the biological as compared to the nonbiological velocity profile.Importantly,a mismatch between instructed human agency and a nonbiological velocity profile primarily activated medial-frontal areas comprising the frontal pole, the paracingulate gyrus, and the anterior cingulate gyrus, as well as the cerebellum and the supplementary eye field as part of the eye movement network. This mismatch effect was specific to the instructed human agency and did not occur in conditions with a mismatch between instructed computer agency and a biological velocity profile. Our results support the hypothesis that humans activate a specific predictive model for biological movements based on their own motor expertise. A violation of this predictive model causes costs as the movement needs to be corrected in accordance with incoming (nonbiological) sensory information.</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>
Video data of spontaneous responses of common marmosets (Callithrix jacchus) on 3D and 2D cricket stimuli.
<p>The degree to which nonhuman animals recognize 2D images as representing the corresponding real objects remains debated. The common marmoset monkey (<em>Callithrix jacchus</em>) is often cited as a species which spontaneously shows natural behaviors to 2D images, e.g. grabbing behaviors to insects and fear responses to snakes. In this study, ten marmosets from two different groups were tested with a live cricket, a 3D plastic model, a monochrome image and two video recordings of the cricket.<br> The monkeys showed the grabbing behavior to the real cricket and the 3D plastic model, but to none of the 2D images. Our experiment suggests that depth information is the most important factor eliciting predatory behavior from the marmosets. In behavioral experiments, monkeys' responses toward 2D images of real objects should be carefully interpreted.</p> <p>All session videos are uploaded here with a 'LOG.txt' file which has sessions & timestamps when coded behaviors occurs.</p>
Research data supporting: "Non-trivial stimuli-responsive collective behaviours emerging from microscopic dynamic complexity in supramolecular polymer systems"
<p>Contains the relevant simulation data and input files. See "readme.txt" for information.</p>
Using UV stimuli to evoke prey capture strikes in head-fixed zebrafish larvae
<p>Hunting in larval zebrafish begins with eye convergence and orienting turns, proceeds to approach swims, and ends with the strike, where larvae consume the prey. Here, we describe a protocol to present UV stimuli to zebrafish, which greatly increases the occurrence of hunting initiation and strikes. We also describe how we record and analyze strike behavior in head-fixed larvae. Our goals are to increase the robustness of prey capture, and to allow other labs to implement strike behavioural essay</p>
Ambisonic Stimuli Files and Binaural Renders
<p>This repository hosts the audio files used to render the layer-based stimuli for the experiment described in 'A Study on Loudspeaker SPL Decays for Envelopment and Engulfment across an Extended Audience' (2024 AES International Conference on Acoustics and Sound Reinforcement). </p><p>Binaural renders of the 7th-order Ambisonic stimuli are included for headphone listening.</p>
Data, stimuli, and analyses for "High-level aftereffects reveal the role of statistical features in visual shape encoding"
<h2><strong>Data and code share.</strong></h2><p>This record contains data and code (written in MATLAB) to reproduce the results shown in:</p><p>Morgenstern, Y. , Storrs, K., R., Schmidt, F., Hartmann, F., Tiedemann, H., Tiedemann, H, ., Wagemans, J., & Fleming, R. (<i>in press</i>) . High-level aftereffects reveal the role of statistical features in visual shape coding. Current Biology</p><p>Below is a summary of shared scripts that load data, run the analysis (including options for fitting model parameters or loading pre-computed fitted parameters), and plotting the results.</p><h3><strong>Figure 1</strong></h3><ul><li><i>Fig1C_shapespace.m</i>: draw shape space (as in Figure 1C)</li><li><i>Fig1EFG_plotpsychometricdata.m</i>: fit psychometric model to pooled data and plot (as in Figure 1EFG)</li><li><i>getExptShapesHbias.m</i>: saves a data structure (which we call 'package') with adaptor, test, and human biases from experiment 1. (Used to fit models; e.g., see <i>fitGabPyr2Hbais.m</i> or <i>fitTAEGANfit2Hbais.m</i>)</li></ul><h3><strong>Figure 2 and 3A</strong></h3><ul><li><i>fig3A_modeval_expt1.m</i>: generate figure that evaluates models in Figure 3A on how well they predict aftereffects in Experiment 1. (script located in the 'Figures 2 and 3A/models' directory).</li></ul><p>Code to fit the models, and figures that show examples of model predictions are in the model directories, and summarized below:</p><h4>Model: <strong>GabPyrAE</strong> </h4><ul><li><i><strong>note: </strong></i>To run GabPyr, you will likely need to recompile the .mex files in 'matlabPyrTools/mex'. Then move the recompiled files into the 'matlabPyrToos' directory</li><li><i>fig2B_GabPyrAEVisFigs.m</i>: produce GabPyrAE model images for example adaptor and test image ( as in Figure 2B )</li><li><i>figS2BC_GabPyrAEExp</i>.m: get GabPyrAE model responses to simulated tilt aftereffect experiment using anisotropic noise, and plot model responses as in Figures S2BC.</li><li><i>getGabPyrAEMod.m: </i>given an adaptor and test image, this function produces the unfit GabPyrAE prediction</li><li><i>fitGabPyr2Hbias</i>.m: fit GabPyrAE model to best predict human baises in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitGabPyrNormMod2Stims</i>.m</li><li><i>evalGabPyrAE_fitmod_aic.m</i>: evaluate GabPyrAE fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalGabPyrAE_unfitmod_aic.m</i>: evaluate GabPyrAE fitted model on how well it predicts human biases from experiment 1</li></ul><h4>Model: <strong>TAE</strong></h4><ul><li><i>fig2CD_TAEmod.m</i>: produce TAE model images for example adaptor and test image (as in Figure 2CD)</li><li><i>getTAEModonShape.m: </i>given an adaptor and test image, this function produces TAE Original prediction. Input to function is adaptor and test shapes, and TAE model parameters alpha and sigma.</li><li><i>getTAEGAN_spwt_onShape.m: </i>given an adaptor and test image, this function produces TAEGAN prediction. Input to function is adaptor and test shapes, and TAEGAN model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much TAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>getTAE_spwt_onShape_nn.m: </i>given an adaptor and test image, this function produces TAE nearest neighbour prediction. Input to function is adaptor and test shapes, and TAE nearest neighbour model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much TAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>fitTAEGAN2Hbias</i>.m: fit TAEGAN model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitTAEGANMod2Stims</i>.m</li><li><i>fitTAENN2Hbias</i>.m: fit TAE nearest neighbour model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitTAENNMod2Stims</i>.m.</li><li><i>evalTAEGAN_fitmod_aic.m</i>: evaluate TAEGAN fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalTAENN_fitmod_aic.m</i>: evaluate TAE nearest neighbour fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalTAE_unfitmod_aic.m</i>: evaluate TAE Original model on how well it predicts human biases from experiment 1</li></ul><h4>Model: <strong>PSAE</strong></h4><ul><li><i>fig2EF_PSAEmod.m</i>: produce PSAE model images for example adaptor and test image</li><li><i>getPos_spwt_ShiftononShape_io.m: </i>given an adaptor and test image, this function produces PSAEGAN prediction. Input to function is adaptor and test shapes, and PSAEGAN model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much PSAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>getPos_spwt_ShiftonShape_io_nn.m: </i>given an adaptor and test image, this function produces PSAE nearest neighbour prediction. Input to function is adaptor and test shapes, and PSAE nearest neighbour model parameters which include a constant term, alpha and sigma, as well as Gaussian pooling parameter that determines how much PSAE to incorporate on the test or mean shapes from neighbouring adaptor line segments.</li><li><i>fitPSAEGAN2Hbias</i>.m: fit PSAEGAN model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitPSAEGANMod2Stims</i>.m</li><li><i>fitPSAENN2Hbias</i>.m: fit PSAE nearest neighbour model to best predict human biases in Experiment 1 using data structure from <i>getExptShapesHbias.m</i>. This is the general function that calls on MATLAB's GA algorithm to minimize the error function in <i>fitPSAENNMod2Stims</i>.m.</li><li><i>evalPSAEGAN_fitmod_aic.m</i>: evaluate PSAEGAN fitted model on how well it predicts human biases from experiment 1.</li><li><i>evalPSAENN_fitmod_aic.m</i>: evaluate PSAE nearest neighbour fitted model on how well it predicts human biases from experiment 1.</li></ul><h4>Model: <strong>ShapeComp </strong>and <strong>No Adaptation</strong></h4><ul><li><i>eval_ShapeComp _aic.m</i>: evaluate ShapeComp 1 parameter fitted model on how well it predicts human biases from experiment 1.</li><li><i>eval_NoAdaptation _aic.m</i>: evaluate model that predicts no adaptation on how well it predicts human biases from experiment 1.</li></ul><h3><strong>Figure 3BC</strong></h3><ul><li><i>fig3BC_Experiment2.m</i>: load, analyze, and plot experiment 2 data (as in Figure 3B and C).</li><li><i>figS4_Expt2_stimuli.m</i>: show adaptors (in black) and test shapes for ShapeComp (purple), PSAE fit GAN (green), and no adaptation model (white) (as in Figure S4)</li></ul><p> </p>
Behavioral data associated with "Passive exposure to task-relevant stimuli enhances categorization learning"
<p>Behavioral data associated with Schmid et al. (2023) "<i>Passive exposure to task-relevant stimuli enhances categorization learning</i>", and example code for loading these data. See README.md for details.</p><p> </p>
Data for: Effects of reproductive status on behavioral and neural responses to isolated pup stimuli in female California mice
<p>The transition to motherhood in mammals is marked by changes in females' perception of and responsiveness to sensory stimuli from infants. Our understanding of maternally induced sensory plasticity relies most heavily on studies in uniparental, promiscuous house mice and rats, which may not be representative of rodent species with different life histories. We exposed biparental, monogamous California mouse (<em>Peromyscus californicus)</em> mothers and ovariectomized virgin females to one of four acoustic and olfactory stimulus combinations (Control: clean cotton and white noise; Call: clean cotton and pup vocalizations; Odor: pup-scented cotton and white noise; Call + Odor: pup-scented cotton and pup vocalizations) and quantified females' behavior and Fos expression in select brain regions. Behavior did not differ between mothers and ovariectomized virgins. Among mothers, however, those exposed to the Control condition took the longest to sniff the odor stimulus, and mothers exposed to the Odor condition were quicker to sniff the odor ball compared to those in the Call condition. Behavior did not differ among ovariectomized virgins exposed to the different conditions. Fos expression differed across conditions only in the anterior hypothalamic nucleus (AHN), which response to aversive stimuli: among mothers, the Control condition elicited the highest AHN Fos and Call + Odor elicited the lowest. Among ovariectomized virgin females, Call elicited the lowest Fos in the AHN. Thus, reproductive status in California mice alters females' behavioral responses to stimuli from pups, especially odors, and results in the inhibition of defense circuitry in response to pup stimuli.</p>
Familiarity, homogeneity, and discrimination of song dialects: Data and playback study stimuli
<p>Male songbirds of many species sing local song dialects that are restricted to defined geographical areas. In most tests of responses to local versus foreign dialects, males respond more aggressively to songs from their own dialect, presumably because local males represent more of a threat to their success. We asked how hearing foreign songs during development and territory establishment affects discrimination of the local dialect in wild Savannah sparrows, <em>Passerculus sandwichensis</em>. After foreign songs had been heard from loudspeakers in the study area in at least two consecutive breeding seasons, males reduced the intensity of their responses to the local version of population-specific buzz segment of the song. Four years after the foreign songs were last broadcast on the study area, males again responded more aggressively to the local version of the buzz. As for the basis of these responses, we found no evidence that birds discriminated among dialects by comparing them to their own songs. However, auditory experience with a foreign song, whether during song development (from speaker-simulated song tutors) or during the current breeding season (from neighbours' songs), reduced the intensity of birds' responses to the local buzz type. Both familiarity, in the form of auditory experience with a song type, and homogeneity, when a song type is sung by all or nearly all of the population, appear to contribute to heightened aggressive responses to a local song dialect.</p>
Stimuli from: AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures
<p>Stimuli generated using a Diffusion Model from the paper "AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures".</p> <p>Please cite as:<br><span>Ciupinska, K.; Marchesi, S.; Abbo, G. A.; Belpaeme, T. and Wykowska, A. (2024). <strong>AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures</strong>. In <em>Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: AWAI</em>; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 1436-1443. DOI: 10.5220/0012596400003636</span></p>
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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.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.