Neural Representations of Dynamic Visual Stimuli

• URL: http://arxiv.org/abs/2406.02659v1
• Date: Tue, 4 Jun 2024 17:59:49 GMT
• Title: Neural Representations of Dynamic Visual Stimuli
• Authors: Jacob Yeung, Andrew F. Luo, Gabriel Sarch, Margaret M. Henderson, Deva Ramanan, Michael J. Tarr,
• Abstract summary: We show that visual motion information as optical flow can be predicted (or decoded) from brain activity as measured by fMRI. We show that this predicted motion can be used to realistically animate static images using a motion-conditioned video diffusion model. This work offers a novel framework for interpreting how the human brain processes dynamic visual information.
• Score: 36.04425924379253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans experience the world through constantly changing visual stimuli, where scenes can shift and move, change in appearance, and vary in distance. The dynamic nature of visual perception is a fundamental aspect of our daily lives, yet the large majority of research on object and scene processing, particularly using fMRI, has focused on static stimuli. While studies of static image perception are attractive due to their computational simplicity, they impose a strong non-naturalistic constraint on our investigation of human vision. In contrast, dynamic visual stimuli offer a more ecologically-valid approach but present new challenges due to the interplay between spatial and temporal information, making it difficult to disentangle the representations of stable image features and motion. To overcome this limitation -- given dynamic inputs, we explicitly decouple the modeling of static image representations and motion representations in the human brain. Three results demonstrate the feasibility of this approach. First, we show that visual motion information as optical flow can be predicted (or decoded) from brain activity as measured by fMRI. Second, we show that this predicted motion can be used to realistically animate static images using a motion-conditioned video diffusion model (where the motion is driven by fMRI brain activity). Third, we show prediction in the reverse direction: existing video encoders can be fine-tuned to predict fMRI brain activity from video imagery, and can do so more effectively than image encoders. This foundational work offers a novel, extensible framework for interpreting how the human brain processes dynamic visual information.

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