AI Article Synopsis
- The study aimed to understand how visualizations impact predictions of uncertain spatial trajectories and participant overconfidence in those predictions.
- Previous research found that predicting these trajectories is tough and often leads to overconfidence, suggesting that visual aids during training may help.
- Two experiments showed that while participants made more accurate predictions with visualizations, their advantage disappeared when the visuals were removed, indicating that visualizations can aid predictions but may also create dependency issues.
Article Abstract
Objective: The goal of this study was to explore the ways in which visualizations influence the prediction of uncertain spatial trajectories (e.g., the unknown path of a downed aircraft or future path of a hurricane) and participant overconfidence in such prediction.
Background: Previous research indicated that spatial predictions of uncertain trajectories are challenging and are often associated with overconfidence. Introducing a visualization aid during training may improve the understanding of uncertainty and reduce overconfidence.
Method: Two experiments asked participants to predict the location of various trajectories at a future time. Mean and variance estimates were compared for participants who were provided with a visualization and those who were not.
Results: In Experiment 1, participants exhibited less error in mean estimations when a linear visualization was present but performed worse than controls once the visualization was removed. Similar results were shown in Experiment 2, with a nonlinear visualization. However, in both experiments, participants who were provided with a visualization did not retain any advantage in their variance estimations once the visualization was removed.
Conclusions: Visualizations may support spatial predictions under uncertainty, but they are associated with benefits and costs for the underlying knowledge being developed.
Application: Visualizations have the potential to influence how people make spatial predictions in the presence of uncertainty. Properly designed and implemented visualizations may help mitigate the cognitive biases related to such predictions.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1177/0018720818758770 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A spatiotemporal and machine-learning platform facilitates the manufacturing of hPSC-derived esophageal mucosa.
Dev Cell
January 2025
Program in Epithelial Biology and Center for Definitive and Curative Medicine, Stanford University, Stanford, CA, USA. Electronic address:
Human pluripotent stem cell-derived tissue engineering offers great promise for designer cell-based personalized therapeutics, but harnessing such potential requires a deeper understanding of tissue-level interactions. We previously developed a cell replacement manufacturing method for ectoderm-derived skin epithelium. However, it remains challenging to manufacture the endoderm-derived esophageal epithelium despite possessing a similar stratified epithelial structure.
View Article and Find Full Text PDFProteinuria and tubular cells: Plasticity and toxicity.
Acta Physiol (Oxf)
February 2025
Department of Medicine, Cell Physiology and Metabolism, University of Geneva, Geneva, Switzerland.
Aim: Proteinuria is the most robust predictive factors for the progression of chronic kidney disease (CKD), and interventions targeting proteinuria reduction have shown to be the most effective nephroprotective treatments to date. While glomerular dysfunction is the primary source of proteinuria, its consequences extend beyond the glomerulus and have a profound impact on tubular epithelial cells. Indeed, proteinuria induces notable phenotypic changes in tubular epithelial cells and plays a crucial role in driving CKD progression.
View Article and Find Full Text PDFAn Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks.
Sensors (Basel)
January 2025
Department of Computer Science, King AbdulAziz University, Jeddah 21589, Saudi Arabia.
Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed.
View Article and Find Full Text PDFStudy on Long-Term Temperature Variation Characteristics of Concrete Bridge Tower Cracks Based on Deep Learning.
Sensors (Basel)
January 2025
Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 210096, China.
Monitoring existing cracks is a critical component of structural health monitoring in bridges, as temperature fluctuations significantly influence crack development. The study of the Huai'an Bridge indicated that concrete cracks predominantly occur near the central tower, primarily due to temperature variations between the inner and outer surfaces. This research aims to develop a deep learning model utilizing Long Short-Term Memory (LSTM) neural networks to predict crack depth based on the thermal variations experienced by the main tower.
View Article and Find Full Text PDFCINet: A Constraint- and Interaction-Based Network for Remote Sensing Change Detection.
Sensors (Basel)
December 2024
School of Physics and Electronics, Nanning Normal University, Nanning 530100, China.
Remote sensing change detection (RSCD), which utilizes dual-temporal images to predict change locations, plays an essential role in long-term Earth observation missions. Although many deep learning based RSCD models perform well, challenges remain in effectively extracting change information between dual-temporal images and fully leveraging interactions between their feature maps. To address these challenges, a constraint- and interaction-based network (CINet) for RSCD is proposed.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!