Demo Data from EDBT 2026 / reviewers in the wild / expert
Andrew M. Lovett
dblp:60/5729
· DBLP profile ↗
14ranked-venue papers
8as first author
4since 2021 · last 2025
0009-0007-4463-1946ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 13 · 8 first-author · 3 since 2021Applied, interdisciplinary, general and emerging computing · 11 · 7 first-author · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 3 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Artificial intelligence
2 papers |
Knowledge representation and reasoning · 35% Vision and language · 35% Image recognition and object detection · 30% |
Topics — the 8 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
graphical perception |
0.8 | 1 | 2024 | What Does the Chart Say? Grouping Cues Guide Viewer Comparisons and Conclusions in Bar Charts · IEEE Trans. Vis. Comput. Graph. 2024 |
Visualization and visual analytics › perception
perception in visualization |
0.8 | 1 | 2024 | What Does the Chart Say? Grouping Cues Guide Viewer Comparisons and Conclusions in Bar Charts · IEEE Trans. Vis. Comput. Graph. 2024 |
Visualization and visual analytics
visual comparison |
0.8 | 1 | 2024 | What Does the Chart Say? Grouping Cues Guide Viewer Comparisons and Conclusions in Bar Charts · IEEE Trans. Vis. Comput. Graph. 2024 |
Visualization and visual analytics › visual encoding
bar chart |
0.2 | 1 | 2024 | What Does the Chart Say? Grouping Cues Guide Viewer Comparisons and Conclusions in Bar Charts · IEEE Trans. Vis. Comput. Graph. 2024 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
qualitative reasoning |
0.1 | 1 | 2008 | CogSketch · AAAI 2008 |
Computer vision › Vision and language
sketch understanding |
0.1 | 1 | 2008 | CogSketch · AAAI 2008 |
Computer vision › Image recognition and object detection › object recognition › shape-based object recognition
sketch recognition |
0.1 | 1 | 2007 | Incremental Learning of Perceptual Categories for Open-Domain Sketch Recognition · IJCAI 2007 |
Learning and educational technologies › multimedia learning
sketch-based learning |
0.0 | 1 | 2008 | CogSketch · AAAI 2008 |
Methods — techniques the papers use, named apart from their topics
crowdsourced experiment · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Visualizing Motion Traces Enhances Pursuit Detection in Dynamic Scenes
Yishu Ji, Maria Kon, Andrew M. Lovett, Mark Livingston, Sangeet S. Khemlani, Yalong Yang 0001, Cindy Xiong Bearfield |
CogSci | 3 |
| 2024 | Measuring and Modeling Pursuit Detection in Dynamic Visual Scenes
Maria Kon, Sangeet S. Khemlani, Andrew M. Lovett |
CogSci | 3 |
| 2024 | What Does the Chart Say? Grouping Cues Guide Viewer Comparisons and Conclusions in Bar ChartsabstractReading a visualization is like reading a paragraph. Each sentence is a comparison: the mean of these is higher than those; this difference is smaller than that. What determines which comparisons are made first? The viewer's goals and expertise matter, but the way that values are visually grouped together within the chart also impacts those comparisons. Research from psychology suggests that comparisons involve multiple steps. First, the viewer divides the visualization into a set of units. This might include a single bar or a grouped set of bars. Then the viewer selects and compares two of these units, perhaps noting that one pair of bars is longer than another. Viewers might take an additional third step and perform a second-order comparison, perhaps determining that the difference between one pair of bars is greater than the difference between another pair. We create a visual comparison taxonomy that allows us to develop and test a sequence of hypotheses about which comparisons people are more likely to make when reading a visualization. We find that people tend to compare two groups before comparing two individual bars and that second-order comparisons are rare. Visual cues like spatial proximity and color can influence which elements are grouped together and selected for comparison, with spatial proximity being a stronger grouping cue. Interestingly, once the viewer grouped together and compared a set of bars, regardless of whether the group is formed by spatial proximity or color similarity, they no longer consider other possible groupings in their comparisons. Cindy Xiong Bearfield, Chase Stokes, Andrew M. Lovett, Steven Franconeri |
IEEE Trans. Vis. Comput. Graph. | 3 |
| 2021 | Selection, Engagement, & Enhancement: A Framework for Modeling Visual Attention
Andrew M. Lovett, Will Bridewell, Paul Bello |
CogSci | 1 |
| 2019 | Attentional Capture: Modeling Automatic Mechanisms and Top-Down Control
Andrew M. Lovett, Will Bridewell, Paul Bello |
CogSci | 1 |
| 2018 | An Attention-Driven Computational Model of Human Causal Reasoning
Paul Bello, Andrew M. Lovett, Gordon Briggs, Kevin O'Neill |
CogSci | 2 |
| 2017 | Goal-Directed Deployment of Attention in a Computational Model: A Study in Multiple-Object Tracking
Andrew M. Lovett, Will Bridewell, Paul Bello |
CogSci | 1 |
| 2015 | Topological Relations between Objects Are Categorically Coded
Andrew M. Lovett, Steven Franconeri |
CogSci | 1 |
| 2014 | Modeling Spatial Abstraction during Mental Rotation
Andrew M. Lovett, Holger Schultheis |
CogSci | 1 |
| 2013 | Glushko Dissertation Prize Symposium
Douglas Knox Bemis, Neil Cohn, George Kachergis, Andrew M. Lovett, Robert L. Goldstone |
CogSci | 4 |
| 2013 | Modeling Spatial Ability in Mental Rotation and Paper-Folding
Andrew M. Lovett, Kenneth D. Forbus |
CogSci | 1 |
| 2012 | Modeling Multiple Strategies for Solving Geometric Analogy Problems
Andrew M. Lovett, Kenneth D. Forbus |
CogSci | 1 |
| 2008 | CogSketch
Kenneth D. Forbus, Andrew M. Lovett, Kate Lockwood, Jon Wetzel, Camillia Matuk, Benjamin D. Jee, Jeffrey M. Usher |
AAAI | 2 |
| 2007 | Incremental Learning of Perceptual Categories for Open-Domain Sketch Recognition
Andrew M. Lovett, Morteza Dehghani, Kenneth D. Forbus |
IJCAI | 1 |