Cortex-Canvas: An Interactive Web Interface for Executing and Evaluating Models of Category-Selective Regions in Human Visual Cortex

Authors

Ruolin Wang, Yuxuan Li, Mayukh Deb, Kushal Reddy Dudipala, Kruthik Ravikanti, Sanjana Chillarege, Arya Bhanushali, Ranjani Koushik, Aashraya Katiyar, N. Apurva Ratan Murty

@inproceedings{10.1145/3772363.3798548,
author = {Wang, Ruolin and Li, Yuxuan and Deb, Mayukh and Reddy Dudipala, Kushal and Ravikanti, Kruthik and Chillarege, Sanjana and Bhanushali, Arya and Koushik, Ranjani and Katiyar, Aashraya and Ratan Murty, N Apurva},
title = {Cortex-Canvas: An Interactive Web Interface for Executing and Evaluating Models of Category-Selective Regions in Human Visual Cortex},
year = {2026},
isbn = {9798400722813},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3772363.3798548},
doi = {10.1145/3772363.3798548},
abstract = {The rapid growth of AI models and large-scale fMRI datasets in NeuroAI has outpaced the tools available for making sense of them. As a result, researchers face growing challenges in understanding which models perform well, which brain regions or datasets remain difficult to predict, and how existing models can be used to explore new scientific hypotheses about the human brain. We present Cortex-Canvas, a web-based interactive system designed to support both structured comparison and hypothesis-driven exploration in NeuroAI research. Cortex-Canvas integrates two complementary components: the Scoreboard, which provides multi-dimensional views of model evaluation across brain regions, datasets, and training sources; and the Lab, which exposes trained brain-encoding models as executable tools, allowing users to upload custom stimuli and test hypotheses through in silico experimentation.},
booktitle = {Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems},
articleno = {191},
numpages = {7},
keywords = {NeuroAI, Visual Cortex, Encoding Models, Data Visualization},
series = {CHI EA '26}
}