1 citations · 1 across the 7 of their papers we have counts for
7 papers
Revisiting Channel Effectiveness: A Multi-Dimensional Evaluation with Primitive Visual Stimuli
Soohyun Lee, Seokhyeon Park, Minsuk Chang +1
Established channel effectiveness rankings primarily assess magnitude estimation accuracy in complete chart contexts, often neglecting other perceptual tasks such as discriminabili…
A Theory of Appropriateness That Accounts for Norms of Rationality
Joel Z. Leibo, Alexander Sasha Vezhnevets, Manfred Diaz +10
We propose a society-first theory of normative appropriateness where individuals, modeled as pre-trained actors with cognitive architectures analogous to Large Language Models (LLM…
Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attention
Minsuk Chang, Yao Wang, Huichen Will Wang +3
Accounting for individual differences can improve the effectiveness of visualization design. While the role of visual attention in visualization interpretation is well recognized,…
Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations
Minsuk Chang, Yao Wang, Huichen Will Wang +2
Knowing where people look in visualizations is key to effective design. Yet, existing research primarily focuses on free-viewing-based saliency models - although visual attention i…
Efficiently Crowdsourcing Visual Importance with Punch-Hole Annotation
Minsuk Chang, Soohyun Lee, Aeri Cho +4
We introduce a novel crowdsourcing method for identifying important areas in graphical images through punch-hole labeling. Traditional methods, such as gaze trackers and mouse-base…
Assessing Graphical Perception of Image Embedding Models using Channel Effectiveness
Soohyun Lee, Minsuk Chang, Seokhyeon Park +1
Recent advancements in vision models have greatly improved their ability to handle complex chart understanding tasks, like chart captioning and question answering. However, it rema…
Extracting Human Attention through Crowdsourced Patch Labeling
Minsuk Chang, Seokhyeon Park, Hyeon Jeon +3
In image classification, a significant problem arises from bias in the datasets. When it contains only specific types of images, the classifier begins to rely on shortcuts - simpli…