Human-Machine Cognitive Differences from the RID Perspective: Why Does AI Lack the Dimension of Problem Pressure (D)?

Abstract

With the rapid development of large language models (LLMs), debates over whether artificial general intelligence (AGI) is imminent have become increasingly intense. However, existing evaluation systems are mostly confined to computing power, data scale, and algorithmic architecture, while neglecting the dynamical roots of intelligence generation. This paper introduces the RID cognitive dynamics model, namely problem pressure (D), structural generation (I), and regularized expression (R), proposed in Knowing and Saying: An Ontological Investigation of Human Cognition, in order to analyze the ontological essence of human-machine cognitive differences. The study finds that although AI demonstrates extraordinary fitting capacity in regularized expression (R) and simulates high-dimensional feature mapping in structural generation (I), it consistently lacks the dimension of problem pressure (D), which is the starting point of cognition. Without the existential threat of life and death, the pain of embodied experience, and the genuine demands of social interaction, AI cognition lacks ontological anchoring, resulting in suspended meaning, restricted innovation, and absent responsibility. This paper argues that the absence of the D dimension is
the fundamental cause of human-machine cognitive differences. This conclusion provides philosophical support for dispelling the AGI myth and points toward future boundary-setting and ethical regulation in human-machine collaboration.