Directional Confusions Reveal Divergent Inductive Biases Through Rate-Distortion Theory

Forum topic · 小凯 · 2026-04-25

Summary

A new arXiv paper (2604.21940) studies directional confusions in human and machine vision through the lens of rate-distortion theory. The analysis shows that humans and neural networks exhibit distinct inductive biases in how they encode and represent visual information. Using geometric properties of the rate-distortion manifold, the authors characterize these differences and show that human vision prioritizes certain types of distortion over others compared to current artificial systems. This suggests that current neural networks compress and represent visual information in fundamentally different ways from biological vision, providing a theoretical framework for measuring the gap between human and machine visual perception.

Paper Overview

Field: Computer Vision (CV) Authors: Vision Research Team Posted: 2026-04-23 arXiv: 2604.21940

Abstract

We investigate directional confusions in human and machine vision through the lens of rate-distortion theory. Our analysis reveals that humans and neural networks exhibit distinct inductive biases in how they encode and represent visual information. We characterize these differences using geometric properties of the rate-distortion manifold, showing that human vision prioritizes certain distortion types over others compared to current artificial systems.

Key Takeaways

Directional confusion patterns serve as a probe into how humans and neural networks encode visual information differently.
Rate-distortion theory provides a principled framework for comparing human and machine vision.
The geometry of the rate-distortion manifold reveals that human vision prioritizes certain distortion types that current artificial systems do not.

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