This paper reviews how the "usefulness" of representations is conceptualized across philosophy, neuroscience, and computer science. It proposes a three-level framework—Representations as Information, Usable, and Used—to organize diverse perspectives on neural representations.

This paper argues that modularity is a fundamental computational principle underlying both natural and artificial intelligence. It reviews how modularity provides significant advantages in efficiency, generalization, and robustness across diverse fields like engineering, neuroscience, and AI, suggesting it as a core design principle for future intelligent systems.