Humans inherently connect certain colors with particular concepts in semantically meaningful ways that facilitate visual communication. These colors are known as semantically resonant colors. For instance, we associate "sky" and "ocean" with shades of blue, and "cherry" with red. In this paper, we investigate how language models, including Word2Vec, RoBERTa, GPT-4o mini and the vision language model CLIP generate and represent nuanced semantically resonant colors for diverse concepts. To achieve this, we utilized a large dataset of color names and concepts, tailored models for the structure of each language model, and developed an interactive web interface, Concept2Color, as a use case. Additionally, we conducted experiments and a detailed analysis to assess the ability of these models to generate meaningful colors. Through these experiments, we examined how factors such as model design, training data and context affect the color output. Our findings reveal the capabilities and limitations of language models in processing and generating semantically resonant colors for concepts, thus contributing insights into how they depict semantic color-concept connections. These insights have implications for data visualization, design, and human-computer interaction, where leveraging effective semantic color generation can enhance communication and user experience.

Causal belief is a cognitive practice that humans apply everyday to reason about cause and effect relations between factors, phenomena, or events. Like optical illusions, humans are prone to drawing causal relations between events that are only coincidental (i.e., causal illusions). Researchers in domains such as cognitive psychology and healthcare often use logistically expensive experiments to understand causal beliefs and illusions. In this paper, we propose Belief Miner, a crowdsourcing method for evaluating people's causal beliefs and illusions. Our method uses the (dis)similarities between the causal relations collected from the crowds and experts to surface the causal beliefs and illusions. Through an iterative design process, we developed a web-based interface for collecting causal relations from a target population. We then conducted a crowdsourced experiment with 101 workers on Amazon Mechanical Turk and Prolific using this interface and analyzed the collected data with Belief Miner. We discovered a variety of causal beliefs and potential illusions, and we report the design implications for future research.

Novels are often adapted into feature films, but the differences between the two media usually require dropping sections of the source text from the movie script.Here we study this screen adaptation process by constructing narrative alignments using the Smith-Waterman local alignment algorithm coupled with SBERT embedding distance to quantify text similarity between scenes and book units.We use these alignments to perform an automated analysis of 40 adaptations, revealing insights into the screenwriting process concerning (i) faithfulness of adaptation, (ii) importance of dialog, (iii) preservation of narrative order, and (iv) gender representation issues reflective of the Bechdel test.