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.

翻译：预测编码（PC）是认知科学中的一个理论框架，认为人脑通过对视觉世界的时空预测来处理认知过程。现有研究已基于PC理论开发出时空预测神经网络，模拟了其两大核心机制：通过残差修正预测和层级学习。然而，这些模型未能在真实世界预测任务中展现预测能力的提升，且忽略了PC理论中的精度加权机制。该机制指出，大脑会将更多注意力分配给精度较低的信号，这构成了人脑认知能力的重要组成部分。本研究提出了一种新型认知扩散概率模型（CogDPM），揭示了扩散概率模型与PC理论之间的内在联系。CogDPM基于扩散模型的层级采样能力开发了精度估计方法，并利用扩散模型固有属性估计的精度权重对引导过程进行加权。实验表明，精度权重能有效估计数据的可预测性。我们采用英国降水数据集和ERA地表风数据集，将CogDPM应用于真实世界预测任务。结果显示，相较于现有特定领域业务模型和通用深度预测模型，CogDPM能提供更精准的预测。