Jiayi Hong

Tianye Wang, Haoxuan Yao, Tai Sing Lee et al.

The visual system evolved to process natural scenes, yet most of our understanding of the topology and function of visual cortex derives from studies using artificial stimuli. To gain deeper insights into visual processing of natural scenes, we utilized widefield calcium-imaging of primate V4 in response to many natural images, generating a large dataset of columnar-scale responses. We used this dataset to build a digital twin of V4 via deep learning, generating a detailed topographical map of natural image preferences at each cortical position. The map revealed clustered functional domains for specific classes of natural image features. These ranged from surface-related attributes like color and texture to shape-related features such as edges, curvature, and facial features. We validated the model-predicted domains with additional widefield calcium-imaging and single-cell resolution two-photon imaging. Our study illuminates the detailed topological organization and neural codes in V4 that represent natural scenes.

Christian Seto, Jacqueline Nguyen, Jiayi Hong et al.

As Large Language Models (LLMs) become more popular within the visualization community, researchers increasingly leverage them for diverse visualization tasks such as design guideline suggestions and visualization evaluation. However, in order for LLMs to act as trustworthy and fair evaluators, we argue that LLMs would need to possess visualization literacy, be capable of following user instructions and uphold graphical integrity. We test the latest versions of the most prominent LLMs, specifically Anthropic's Claude (Opus 4.5), OpenAI's Generative Pretrained Transformers (GPT 5.2 Pro), and Google's Gemini (Gemini 3 Flash) on these features and find that while these models now possess visualization literacy, they still struggle with other features necessary for instruction following and graphical integrity. Using a modified Visualization Literacy Assessment Test (VLAT), our findings show that these recent LLMs have achieved greater than human-levels of visualization literacy in contrast to prior research. In order to test the models' abilities to follow instructions, we used few-shot and chain-of-thought prompting as proxies for instruction following tasks on evaluating visualization literacy and find that these specialized prompting techniques are becoming obsolete with respect to improving visualization literacy. Additionally, we experiment with the inherent ability of LLMs to evaluate misleading visualizations to test the models' abilities for upholding graphical integrity and find that without specialized or leading prompting techniques, the models struggle with being able to accurately identify whether a visualization is misleading or not. Our results further break down the performance of each model on these tasks, but the culmination of our findings force us to reconsider the current effectiveness of LLMs as visualization evaluators.