Machine Learning Modeling for Multi-order Human Visual Motion Processing

• URL: http://arxiv.org/abs/2501.12810v1
• Date: Wed, 22 Jan 2025 11:41:41 GMT
• Title: Machine Learning Modeling for Multi-order Human Visual Motion Processing
• Authors: Zitang Sun, Yen-Ju Chen, Yung-Hao Yang, Yuan Li, Shin'ya Nishida,
• Abstract summary: This research aims to develop machines that learn to perceive visual motion as do humans. Our model architecture mimics the cortical V1-MT motion processing pathway. We trained our dual-pathway model on novel motion datasets with varying material properties of moving objects.
• Score: 5.043066132820344
• License:
• Abstract: Our research aims to develop machines that learn to perceive visual motion as do humans. While recent advances in computer vision (CV) have enabled DNN-based models to accurately estimate optical flow in naturalistic images, a significant disparity remains between CV models and the biological visual system in both architecture and behavior. This disparity includes humans' ability to perceive the motion of higher-order image features (second-order motion), which many CV models fail to capture because of their reliance on the intensity conservation law. Our model architecture mimics the cortical V1-MT motion processing pathway, utilizing a trainable motion energy sensor bank and a recurrent graph network. Supervised learning employing diverse naturalistic videos allows the model to replicate psychophysical and physiological findings about first-order (luminance-based) motion perception. For second-order motion, inspired by neuroscientific findings, the model includes an additional sensing pathway with nonlinear preprocessing before motion energy sensing, implemented using a simple multilayer 3D CNN block. When exploring how the brain acquired the ability to perceive second-order motion in natural environments, in which pure second-order signals are rare, we hypothesized that second-order mechanisms were critical when estimating robust object motion amidst optical fluctuations, such as highlights on glossy surfaces. We trained our dual-pathway model on novel motion datasets with varying material properties of moving objects. We found that training to estimate object motion from non-Lambertian materials naturally endowed the model with the capacity to perceive second-order motion, as can humans. The resulting model effectively aligns with biological systems while generalizing to both first- and second-order motion phenomena in natural scenes.

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