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266
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266 results for “Distractions”
High-resolution hard X-ray tomography and histology of a rat jaw for stem cell-mediated distraction osteogenesis
<p>Histology and microtomography of a rat jaw after distraction. These datasets appear in "<em>Combining high-resolution hard X-ray tomography and histology for stem cell-mediated distraction osteogenesis</em>" Applied Sciences 12(12) (2022) 6268.</p> <p>Micromography (hdr/img files) has pixel size of 10.0228 µm. Histology (.tif file) has pixel size of 0.243094 µm.</p> <p>Slice to volume registration scripts can be found at https://github.com/grodgers1/SliceToVolume.</p>
Reward draws the eye, uncertainty holds the eye: Associative learning modulates distracter interference in visual search.
<p>Eye tracking data and statistical analysis of:</p> <p>Koenig, S., Kadel, H., Uengoer, M., Schubö, A., & Lachnit, H. (2017). Reward draws the eye, uncertainty holds the eye: Associative learning modulates distracter interference in visual search. <em>Frontiers in Behavioral Neuroscience</em>. doi: 10.3389/fnbeh.2017.00128.</p> <p> Abstract: Stimuli in our sensory environment differ with respect to their physical salience, but moreover may acquire motivational salience by association with reward. If we repeatedly observed that reward is available in the context of a particular cue, but absent in the context of another cue, the former typically attracts more attention than the latter. However, we also may encounter cues uncorrelated with reward. A cue with 50% reward contingency may induce an average reward expectancy, but at the same time induces high reward uncertainty. In the current experiment we examined how both values, reward expectancy and uncertainty, affected overt attention. Two different colors were established as predictive cues for low reward and high reward respectively. A third color was followed by high reward on 50% of the trials and thus induced uncertainty. Colors then were introduced as distractors during search for a shape target and we examined the relative potential of the color distractors to capture and hold the first fixation. We observed that capture frequency corresponded to reward expectancy while capture duration corresponded to uncertainty. The results may suggest that within trial, reward expectancy is represented at an earlier time window than uncertainty.</p>
Learning to resist distraction by spatially predictable luminance transients and color singletons: same or different mechanisms?
<p>The file contains the dataset related to the paper entitled "Learning to resist distraction by spatially predictable luminance transients and color singletons: same or different mechanisms?" to appear in Visual Cognition.</p><p>The folder contains a text file named README.txt that contains the explanation of the folder and file structure.</p>
A Dataset on Takeover during Distracted L2 Driving
<p><strong>Abstract</strong></p> <p>Automated driving systems enable drivers to perform various non-driving tasks, which has led to concerns regarding driver distraction during automated driving. These concerns have spurred numerous studies investigating driver performance of fallback to driving (i.e., takeover). However, publicly available datasets that present takeover performance data are insufficient. The lack of datasets limits advancements in developing safe automated driving systems. This study introduces TD2D, a dataset collected from 50 drivers with balanced gender representation and diverse age groups in an L2 automated driving simulator. The dataset comprises 500 cases including takeover performance, workload, physiological, and ocular data collected across 10 secondary task conditions: (1) no secondary tasks, (2) three visual tasks, and (3) six auditory tasks. We anticipate that this dataset will contribute significantly to the advancement of automated driving systems.</p>
Driver Distraction Data ESR 8
<p>Driver Distraction Data from the Driver Simulator at <em>Škoda</em> Auto a.s.</p>
Investigating Problem of Distracted Drivers on Louisiana Roadways
<p>Corresponding data set for Tran-SET Project No. 17SALSU10. Abstract of the final report is stated below for reference:</p> <p>"Due to the escalating usage of cellphone and social networking, distracted driving is and will remain as one of the most serious problems faced by Departments of Transportation (DOTs) and law enforcement agencies. Under the aim of in-depth investigation of distracted driving crashes in Louisiana, the specific objectives of this study are: (1) reviewing the crash reports for the quality of distracted driving crash reporting, (2) analyzing distracted driving-related crashes through regression model and data mining algorithm to link the severity of distracted driving crashes with the contributing factors collected in crash data, (3) investigating the observable characteristics of distracted driving roadside and video survey, and (4) recommending the countermeasures utilizing the analysis results and reviews. About 60,000 crashes from ten-year crash data, three types of distracted driving related crashes are modeled: Fatal (K) and Severe (A) Injury; Moderate (B) and Complaint (C) Injury; and Property-damage only (PDO). One statistical method was used for prediction, multinomial logistic regression, and one data mining algorithms was used, random forest. Higher speed limit, curved road, head-on crashes were identified among the key factors. Data mining algorithms performed better in prediction compared to the multinomial logistic regression when sensitivity and specificity were used to compare the predicted results. Fisher’s exact tests of roadside manual observation data shows that gender has no significant influence in cellphone distraction (regardless of distraction type), however age can be influential and associated with driver distraction. Association rule mining of observation data shows that the most predominant type of cellphone use is manipulating mainly occurs at intersections, whereas talking is more associated with segments. In-vehicle video data were coded by the software FaceReader, which captures facial expressions of drivers while driving. Initial results do suggest valence in emotion can be attributed to timing before, during, and after cellphone calls and texting. Physical countermeasure development towards reducing the distraction-related crash severity should be targeted at preventing lane departure crashes. Physical countermeasure development towards reducing the distraction-related crash severity should be targeted at preventing lane departure crashes. Strict enforcement of texting ban with awareness campaign are also expected to prevent distracted driving."</p>
Comparison of Usual Care and Distraction (Tablet) in Children 3-5 Years Old
ClinicalTrials.gov study NCT05368961. IPD Sharing: NO. Countries: 1. Publications: 3.
Preventing Distracted Driving Phase II
ClinicalTrials.gov study NCT05608018. IPD Sharing: YES. Countries: 1. Publications: 6.
Virtual Reality Distraction for Reduction
ClinicalTrials.gov study NCT04416555. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Manual Cervical Distraction: Measuring Chiropractic Delivery for Neck Pain Clinical Trial
ClinicalTrials.gov study NCT01765751. IPD Sharing: YES. Countries: 1. Publications: 5.
Audio Distraction for Traction Pin Placement
ClinicalTrials.gov study NCT05927480. IPD Sharing: NO. Countries: 1. Publications: 8.
The Study Evaluates the Effect of an Interactive Projector as a Distraction for Children During Anesthetic Induction. The Primary Objective is to Reduce Perioperative Anxiety, Measured With the Modifi
ClinicalTrials.gov study NCT07230743. IPD Sharing: NO. Countries: 1. Publications: 8.
The Effect of Two Distraction Strategies in Reducing Preoperative Anxiety in Children
ClinicalTrials.gov study NCT05285995. IPD Sharing: NO. Countries: 1. Publications: 21.
Virtual Reality Outperforms Game Card Distraction in Reducing Distress During Pediatric Wound Care
ClinicalTrials.gov study NCT07335666. IPD Sharing: NO. Countries: 1. Publications: 5.
The Effect of Virtual Reality Distraction on Preoperative Anxiety in Abdominal Surgery Patients
ClinicalTrials.gov study NCT05718661. IPD Sharing: YES. Countries: 1. Publications: 5.
Bubble Blowing As an Effective Distraction During Pediatric IV Insertion
ClinicalTrials.gov study NCT05899452. IPD Sharing: YES. Countries: 1. Publications: 19.
iPad as a Distraction Tool During Facial Laceration Repair
ClinicalTrials.gov study NCT02217436. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Attention and distraction in the modular visual system of a jumping spider
Open the record for dataset details and reuse information.
Noise distracts foraging bats
<p>Predators frequently must detect and localize their prey in challenging environments. Noisy environments have been prevalent across the evolutionary history of predator-prey relationships, but now with increasing anthropogenic activities noise is becoming a more prominent feature of many landscapes. Here, we use the gleaning pallid bat, <i>Antrozous pallidus, </i>to investigate the mechanism by which noise disrupts hunting behavior. Noise can primarily function to <i>mask </i>– obscure by spectrally overlapping a cue of interest, or <i>distract </i>– occupy an animal's attentional or other cognitive resources. Using band-limited white noise treatments that either overlapped the frequencies of a prey cue or did not overlap this cue, we find evidence that distraction is a primary driver of reduced hunting efficacy in an acoustically-mediated predator. Under exposure to both noise types successful prey localization declined by half, search time nearly tripled, and bats used 25% more sonar pulses than when hunting in ambient conditions. Overall, the pallid bat does not seem capable of compensating for environmental noise. These findings have implications for mitigation strategies, specifically the importance of reducing sources of noise on the landscape rather than attempting to reduce the bandwidth of anthropogenic noise.</p>
Data from: Why does noise reduce response to alarm calls? Experimental assessment of masking, distraction and greater vigilance in wild birds
1. Environmental noise from anthropogenic and other sources affects many aspects of animal ecology and behaviour, including acoustic communication. Acoustic masking is often assumed in field studies to be the cause of compromised communication in noise, but other mechanisms could have similar effects. 2. We tested experimentally how background noise disrupted the response to conspecific alarm calls in wild superb fairy-wrens, Malurus cyaneus, assessing the effects of acoustic masking, distraction and changes in vigilance. We first examined the birds' response to alarm-call playbacks accompanied by different amplitudes of background noise that overlapped the calls in acoustic frequency. We then scored and videoed their response to alarm calls in two types of background noise, that did or did not overlap call frequency, but were broadcast at a constant amplitude. 3. Birds were less likely to flee to alarm calls in higher amplitudes of overlapping noise, demonstrating that noise itself compromised communication independently of environmental correlates. Background noise affected the response only if it overlapped in frequency with the alarm calls, implying that the effect was not due to distraction. Further, birds were equally vigilant during background noise of overlapping or non-overlapping frequency, indicating that the lack of response to alarm calls in overlapping noise was not due to enhanced vigilance and awareness that there was no predator. 4. We conclude that alarm-call reception was compromised by masking, a mechanism that is often assumed but rarely tested in an ecological context. Masking compromised reception of high-frequency 'aerial' alarm calls and so could reduce survival in background noise of similar frequency. While anthropogenic noise, which is often of lower frequency, is unlikely to affect communication with these calls, it could affect reception of acoustic cues of danger, or other conspecific or heterospecific alarm calls.
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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.