Computer Vision and Its Relationship to Cognitive Science: A perspective from Bayes Decision Theory

2026-01-30

AI Summary

This work proposes a unified framework grounded in Bayesian decision theory to bridge the paradigmatic gap between Bayesian methods and deep neural networks in computer vision, while establishing connections to human visual cognition. By systematically integrating Bayesian inference, deep learning, and cognitive modeling of the ventral visual pathway, the framework—viewed through the common lens of Bayesian decision-making—reveals for the first time the complementary strengths and inherent limitations of these approaches in visual understanding. The study not only elucidates the theoretical and mechanistic relationships between the two paradigms but also offers a novel direction toward building more human-like visual systems that jointly achieve high performance and interpretability.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Machine Learning: Bayesian Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Social networks and social learning
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This document presents an introduction to computer vision, and its relationship to Cognitive Science, from the perspective of Bayes Decision Theory (Berger 1985). Computer vision is a vast and complex field, so this overview has a narrow scope and provides a theoretical lens which captures many key concepts. BDT is rich enough to include two different approaches: (i) the Bayesian viewpoint, which gives a conceptually attractive framework for vision with concepts that resonate with Cognitive Science (Griffiths et al., 2024), and (ii) the Deep Neural Network approach whose successes in the real world have made Computer Vision into a trillion-dollar industry and which is motivated by the hierarchical structure of the visual ventral stream. The BDT framework relates and captures the strengths and weakness of these two approaches and, by discussing the limitations of BDT, points the way to how they can be combined in a richer framework.

Problem

• Computer Vision
• Cognitive Science
• Bayes Decision Theory
• Deep Neural Networks
• Bayesian Inference

Innovation

• Bayes Decision Theory
• Computer Vision
• Cognitive Science
• Deep Neural Networks
• Ventral Stream