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ShareScore release 0.9.0
Dataset results
7 results for “Decision making under uncertainty”
Data and code for "Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making" (CHI 2017)
<p>This repository contains data and analysis code for the following paper:</p> <p>Michael Fernandes, Logan Walls, Sean Munson, Jessica Hullman, and Matthew Kay. "Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making", Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI 2018. DOI: 10.1145/3173574.3173718<br> </p>
Supplementary Data: Real-time optimal flood control decision making under uncertainty
<p>The files in this record contain data for real-time optimal flood control decision making and risk propagation under multiple uncertainties considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Pubugou Reservoir data;</li> <li>Source code and results of the Martingale Model of Forecast Evolution (MMFE);</li> <li>Source code and results of the SMAA-2 model;</li> <li>Source code and results of SMAA-TOPSIS model;</li> <li>Source code and results of the stochastic programming with recourse model.</li> </ul>
Optimal policy for uncertainty estimation concurrent with decision making
<p>Dataset for "Optimal policy for uncertainty estimation concurrent with decision making".</p> <p>The dataset should be merged into the code folder, thus the program can work.</p>
Uncertainty in Pedestrian Decision-Making in Urgent Scenarios Modulates Multi-Level Neural Hierarchies from Perception to Execution
<p><span>In urgent traffic scenarios, pedestrians exhibit decision-making uncertainty, significantly influencing safe interaction dynamics with automated vehicles. However, the inherent mechanisms of such decision behavior remain inadequately understood. To address this gap, we designed dynamic interactive stimulus experiments to replicate pedestrian-vehicle interactions in urgent scenarios, incorporating spatiotemporal pressure and introducing substantial penalties for decision failures. We employed multimodal data analysis, including behavioral data, electroencephalography (EEG) and eye-tracking data, to investigate the influence of urgency on uncertainty in decision-making and the underlying multi-level neural processes. Our findings demonstrate that as the urgency of the stimulus increases, humans adjust their decision objectives, resulting in an initial decrease followed by an increase in decision uncertainty when dealing with more urgent stimuli. Specifically, urgency augments top-down perceptual processes during the early perception stage. <span>Such a mechanism implies an enhanced dependence on prior experiences for perceptual </span></span><span><span><span>decision<span>-making in high-urgency situations. </span></span></span></span><span>While urgency accelerated motion preparation time during the decision-execution stage, it is noteworthy that the culmination of evidence accumulation (represented by the CPP peak) manifested later than the actual response. These results suggest that insufficient perceptual information and evidence accumulation may increase decision-making uncertainty. Our experimental study unveils a correlation between human decision-making uncertainty and scenario urgency, particularly within a defined urgency range. </span></p>
Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks
<p>This dataset contains the behavioral choice data obtained from N = 230 participants that played a two-armed bandit task (139 females, age: 34 +/- 10 years) in partial and complete feedback conditions, as described in (Findling, Skvortsova et al., 2019, Nature Neuroscience, https://doi.org/10.1038/s41593-019-0518-9).</p> <div> <div> <div> <p>The experiment was performed on the Prolific platform (prolific.co) and the research was carried out following the principles and guidelines for experiments including human participants provided in the declaration of Helsinki and approved by the relevant authorities (Inserm Ethical Review Committee, IRB #00003888). All participants provided written informed consent prior to their inclusion.</p> </div> </div> </div>
Decision Making in Multiple Sclerosis Care Under Uncertainty
ClinicalTrials.gov study NCT04035720. IPD Sharing: NO. Countries: 1. Publications: 1.
Perceptual Decision Making Under Conditions of Visual Uncertainty
ClinicalTrials.gov study NCT01845883. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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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.