Primate vision reveals a missing principle for robust dynamic AI

2026-08-24

AI Summary

研究通过比较人类和猕猴的视觉系统与多种AI模型，发现预测性世界模型在跨外观泛化及动态视觉处理上表现最佳，提出预测学习是实现稳健动态AI的关键途径。

Abstract

How does an intelligent visual system combine what objects look like with how they move while remaining robust as appearance changes? We addressed this question by comparing human perception and neural activity in macaque inferior temporal cortex with representations from image- and video-based neural networks spanning recognition, segmentation, optic-flow processing and predictive world modeling. Temporal integration improved object representations, but most video recognition models generalized poorly when appearance was disrupted while motion structure was preserved. Humans and macaque IT remained robust. Notably, predictive world models combined strong cross-appearance generalization with the closest correspondence to IT, outperforming other video-modeling approaches in neural fidelity. Yet no model reproduced the cortical transformation from early appearance-dominated responses toward later appearance-invariant motion coding. These results identify progressive integration of motion into object representations as a principle of robust dynamic vision and implicate predictive learning as a promising route toward realizing this computation in artificial systems.

Problem

• dynamic vision
• robustness
• appearance and motion integration

Innovation

• predictive world models
• cross-appearance generalization
• motion integration