Evidence Accumulation Models (EAMs) have been widely used to investigate speeded decision-making processes, but they have largely neglected the role of predictive processes emphasized by theories of the predictive brain. In this paper, we present the Predictive evidence Accumulation Models (PAM), a novel computational framework that integrates predictive processes into EAMs. Grounded in the "observing the observer" framework, PAM combines models of Bayesian perceptual inference, such as the Hierarchical Gaussian Filter, with three established EAMs (the Diffusion Decision Model, Lognormal Race Model, and Race Diffusion Model) to model decision-making under uncertainty. We validate PAM through parameter recovery simulations, demonstrating its accuracy and computational efficiency across various decision-making scenarios. Additionally, we provide a step-by-step tutorial using real data to illustrate PAM's application and discuss its theoretical implications. PAM represents a significant advancement in the computational modeling of decision-making, bridging the gap between predictive brain theories and EAMs, and offers a promising tool for future empirical research.

翻译：证据累积模型（EAMs）已被广泛用于研究快速决策过程，但它们在很大程度上忽视了预测大脑理论所强调的预测过程的作用。本文提出了预测证据累积模型（PAM），这是一种将预测过程整合到EAMs中的新型计算框架。PAM基于“观察观察者”框架，将贝叶斯感知推理模型（如分层高斯滤波器）与三种成熟的EAMs（扩散决策模型、对数正态竞赛模型和竞赛扩散模型）相结合，以模拟不确定性下的决策过程。我们通过参数恢复模拟验证了PAM，证明了其在各种决策场景下的准确性和计算效率。此外，我们提供了一个使用真实数据的分步教程，以说明PAM的应用并讨论其理论意义。PAM代表了决策计算建模领域的重大进展，弥合了预测大脑理论与EAMs之间的鸿沟，并为未来的实证研究提供了一个有前景的工具。