10 papers
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…
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…
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…
Uncovering the Computational Ingredients of Human-Like Representations in LLMs
Zach Studdiford, Timothy T. Rogers, Kushin Mukherjee +1
The ability to translate diverse patterns of inputs into structured patterns of behavior has been thought to rest on both humans' and machines' ability to learn robust representati…