4 citations · 12 across the 15 of their papers we have counts for
17 papers
Guidelines Are Not Rules: Characterizing Terminologies around Visualization Design Guidelines
Anna L. Chinni, Md Dilshadur Rahman, Bon Adriel Aseniero +6
A common expectation in visualization research is that outcomes recommend how researchers and practitioners take action or make design decisions. We often express these as "guideli…
Predicting affective connotation of visualizations from their constituent colors
Karen B. Schloss, Halle C. Braun, Kushin Mukherjee +2
With increasing evidence that affective connotation (emotional association) is an important aspect of visual communication, there is a need for methods to predict affective connota…
AI-guided stimuli discovery and generation to optimize facial emotion perception studies in autism
Kushin Mukherjee, Na Yeon Kim, Maren Wehrheim +2
Understanding perceptual differences between autistic and neurotypical adults requires behavioral assays that are sensitive, reliable, and mechanistically informative. Facial emoti…
auto-psych: Automating the science of mind using agent-driven theory discovery and experimentation
Ben Prystawski, Kushin Mukherjee, Daniel Wurgaft +4
AI-based scientific automation is increasingly possible by using agents to generate hypotheses, design experiments, and analyze data. Data collection is a major bottleneck in this…
Affective Color Scales for Colormap Data Visualizations
Halle C. Braun, Kushin Mukherjee, Seth R. Gorelik +1
Research on affective visualization design has shown that color is an especially powerful feature for influencing the emotional connotation of visualizations. Associations between…
Presenting Large Language Models as Companions Affects What Mental Capacities People Attribute to Them
Allison Chen, Sunnie S. Y. Kim, Angel Franyutti +4
How might messages about large language models (LLMs) found in public discourse influence the way people think about and interact with these models? To explore this question, we ra…
Uncovering the Computational Ingredients of Human-Like Representations in LLMs
Zach Studdiford, Timothy T. Rogers, Kushin Mukherjee +1
The human ability to translate diverse perceptual and linguistic inputs into structured behavior has been thought to rest on learning robust representations of concepts. The rapid…
EncQA: Benchmarking Vision-Language Models on Visual Encodings for Charts
Kushin Mukherjee, Donghao Ren, Dominik Moritz +1
Multimodal vision-language models (VLMs) continue to achieve ever-improving scores on chart understanding benchmarks. Yet, we find that this progress does not fully capture the bre…
Human-centered evaluation of data visualization understanding in vision-language models
Arnav Verma, Kushin Mukherjee, Christopher Potts +2
Data visualization understanding, which requires integrating visual, numerical, and linguistic inputs, poses fundamental challenges to multimodal reasoning. Recently developed visi…
AI-enhanced semantic feature norms for 786 concepts
Siddharth Suresh, Kushin Mukherjee, Tyler Giallanza +4
Semantic feature norms have been foundational in the study of human conceptual knowledge, yet traditional methods face trade-offs between concept/feature coverage and verifiability…
Evaluating Steering Techniques using Human Similarity Judgments
Zach Studdiford, Timothy T. Rogers, Siddharth Suresh +1
Current evaluations of Large Language Model (LLM) steering techniques focus on task-specific performance, overlooking how well steered representations align with human cognition. U…
Large Language Models estimate fine-grained human color-concept associations
Kushin Mukherjee, Timothy T. Rogers, Karen B. Schloss
Concepts, both abstract and concrete, elicit a distribution of association strengths across perceptual color space, which influence aspects of visual cognition ranging from object…
SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction
Kushin Mukherjee, Holly Huey, Xuanchen Lu +4
Sketching is a powerful tool for creating abstract images that are sparse but meaningful. Sketch understanding poses fundamental challenges for general-purpose vision algorithms be…
Semantic Feature Verification in FLAN-T5
Siddharth Suresh, Kushin Mukherjee, Timothy T. Rogers
This study evaluates the potential of a large language model for aiding in generation of semantic feature norms - a critical tool for evaluating conceptual structure in cognitive s…
Human-machine cooperation for semantic feature listing
Kushin Mukherjee, Siddharth Suresh, Timothy T. Rogers
Semantic feature norms, lists of features that concepts do and do not possess, have played a central role in characterizing human conceptual knowledge, but require extensive human…
Conceptual structure coheres in human cognition but not in large language models
Siddharth Suresh, Kushin Mukherjee, Xizheng Yu +3
Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain. For many years, such use involved e…
Context Matters: A Theory of Semantic Discriminability for Perceptual Encoding Systems
Kushin Mukherjee, Brian Yin, Brianne E. Sherman +2
People's associations between colors and concepts influence their ability to interpret the meanings of colors in information visualizations. Previous work has suggested such effect…