publications
2026
- Multidimensional dynamics of object representations in the human visual systemZirui Chen, Leyla Isik, and Michael F BonnerbioRxiv, 2026
Natural image representations are distributed across many dimensions of visual cortex activity, but little is known about how the multidimensional structure of these representations evolves over time following stimulus onset. Here we examined the temporal dynamics and latent dimensional structure of natural object representations in large-scale EEG and MEG data. We also compared these data with leading representational models derived from large-scale human similarity judgments and deep neural networks. Our findings reveal a rapid expansion of stimulus dimensionality in the brain, which peaks within 100 milliseconds and gradually decays over hundreds of milliseconds. The dynamics of these dimensionality changes tracked the decoding accuracy for both behavioral embeddings and neural network features, suggesting that dimensionality may be a general indicator of representational expressivity. Interestingly, the dimensionality of the neural representations could not be fully explained by leading behavior-based or neural network models. Follow-up experiments showed that the remaining neural variance carried additional perceptually relevant information not yet explained by leading models. Together, these findings reveal previously unrecognized complexity in measurements of dynamic human brain responses to natural objects.
2025
- Universal dimensions of visual representationZirui Chen, and Michael F BonnerScience Advances, 2025
Do neural network models of vision learn brain-aligned representations because they share architectural constraints and task objectives with biological vision or because they learn universal features of natural image processing? We characterized the universality of hundreds of thousands of representational dimensions from visual neural networks with varied construction. We found that networks with varied architectures and task objectives learn to represent natural images using a shared set of latent dimensions, despite appearing highly distinct at a surface level. Next, by comparing these networks with human brain representations measured with fMRI, we found that the most brain-aligned representations in neural networks are those that are universal and independent of a network’s specific characteristics. Remarkably, each network can be reduced to fewer than ten of its most universal dimensions with little impact on its representational similarity to the human brain. These results suggest that the underlying similarities between artificial and biological vision are primarily governed by a core set of universal image representations that are convergently learned by diverse systems.
2023
- A stimulus-driven approach reveals vertical luminance gradient as a stimulus feature that drives human cortical scene selectivityAnnie Cheng, Zirui Chen, and Daniel D DilksNeuroImage, 2023
Human neuroimaging studies have revealed a dedicated cortical system for visual scene processing. But what is a “scene”? Here, we use a stimulus-driven approach to identify a stimulus feature that selectively drives cortical scene processing. Specifically, using fMRI data from BOLD5000, we examined the images that elicited the greatest response in the cortical scene processing system, and found that there is a common “vertical luminance gradient” (VLG), with the top half of a scene image brighter than the bottom half; moreover, across the entire set of images, VLG systematically increases with the neural response in the scene-selective regions (Study 1). Thus, we hypothesized that VLG is a stimulus feature that selectively engages cortical scene processing, and directly tested the role of VLG in driving cortical scene selectivity using tightly controlled VLG stimuli (Study 2). Consistent with our hypothesis, we found that the scene-selective cortical regions—but not an object-selective region or early visual cortex—responded significantly more to images of VLG over control stimuli with minimal VLG. Interestingly, such selectivity was also found for images with an “inverted” VLG, resembling the luminance gradient in night scenes. Finally, we also tested the behavioral relevance of VLG for visual scene recognition (Study 3); we found that participants even categorized tightly controlled stimuli of both upright and inverted VLG to be a place more than an object, indicating that VLG is also used for behavioral scene recognition. Taken together, these results reveal that VLG is a stimulus feature that selectively engages cortical scene processing, and provide evidence for a recent proposal that visual scenes can be characterized by a set of common and unique visual features.