Machine Cognition and Prior Knowledge: A Study Based on Computer Vision Models

Abstract

By reviewing the evolution and applications of computer‑vision models, this paper systematically analyzes the beneficial impact of prior knowledge on machine cognition—namely, improved data efficiency, enhanced robustness, and increased interpretability. Vision models exploit a rich set of a priori image properties—spatial locality, translational invariance, and hierarchical organization—by embedding these priors, explicitly or implicitly, into network architecture, preprocessing pipelines, and regularization terms. Such incorporation enables models to achieve high accuracy and clearer internal representations even when training datasets are scarce. In parallel, we examine the cognitive constraints and potential risks of relying on a priori knowledge. Overly strong priors can restrict the expressive range of machine cognition, introduce subjectivity, and pose difficulties in formally representing the acquired knowledge. This dual perspective underscores both the promise and the pitfalls of integrating prior knowledge into visual‑perception systems.