Representational alignment between DNNs and the brain: Going beyond objects

How does the brain represent the world around us? In recent years, the unprecedented ability of deep neural networks (DNNs) to predict neural responses in human visual cortex has led to a great deal of excitement about these models’ potential to capture human visual representations. However, studies demonstrating representational alignment of visual DNNs with humans typically use brain responses to static, isolated objects, while real-life visual perception requires processing of complex, dynamic scenes. In this talk, I will discuss work aiming to move the field beyond object recognition by focusing on scene perception, video perception, and data-driven alignment via brain-guided image generation.