  Predictive Coding (PC) is a theoretical framework in cognitive science
suggesting that the human brain processes cognition through spatiotemporal
prediction of the visual world. Existing studies have developed spatiotemporal
prediction neural networks based on the PC theory, emulating its two core
mechanisms: Correcting predictions from residuals and hierarchical learning.
However, these models do not show the enhancement of prediction skills on
real-world forecasting tasks and ignore the Precision Weighting mechanism of PC
theory. The precision weighting mechanism posits that the brain allocates more
attention to signals with lower precision, contributing to the cognitive
ability of human brains. This work introduces the Cognitive Diffusion
Probabilistic Models (CogDPM), which demonstrate the connection between
diffusion probabilistic models and PC theory. CogDPM features a precision
estimation method based on the hierarchical sampling capabilities of diffusion
models and weight the guidance with precision weights estimated by the inherent
property of diffusion models. We experimentally show that the precision weights
effectively estimate the data predictability. We apply CogDPM to real-world
prediction tasks using the United Kindom precipitation and ERA surface wind
datasets. Our results demonstrate that CogDPM outperforms both existing
domain-specific operational models and general deep prediction models by
providing more proficient forecasting.
