Over the past four decades, efforts have been made to develop and evaluate models for Empirical Translation Process Research (TPR), yet a comprehensive framework remains elusive. This article traces the evolution of empirical TPR within the CRITT TPR-DB tradition and proposes the Free Energy Principle (FEP) and Active Inference (AIF) as a framework for modeling deeply embedded translation processes. It introduces novel approaches for quantifying fundamental concepts of Relevance Theory (relevance, s-mode, i-mode), and establishes their relation to the Monitor Model, framing relevance maximization as a special case of minimizing free energy. FEP/AIF provides a mathematically rigorous foundation that enables modeling of deep temporal architectures in which embedded translation processes unfold on different timelines. This framework opens up exciting prospects for future research in predictive TPR, likely to enrich our comprehension of human translation processes, and making valuable contributions to the wider realm of translation studies and the design of cognitive architectures.

翻译：过去四十年间，研究者们致力于开发并评估实证翻译过程研究（Empirical Translation Process Research, TPR）的模型，但至今仍缺乏一个全面的理论框架。本文追溯了CRITT TPR-DB传统中实证TPR的演变，并提出将自由能原理（Free Energy Principle, FEP）与主动推理（Active Inference, AIF）作为建模深层嵌入翻译过程的框架。本文引入了量化关联理论核心概念（关联性、s模式、i模式）的新方法，并确立了这些概念与监控模型（Monitor Model）之间的关系，将关联最大化视为自由能最小化的特例。FEP/AIF提供了数学上严谨的基础，能够建模深层时间架构，其中嵌入的翻译过程在不同时间线上展开。该框架为预测性TPR的未来研究开辟了令人振奋的前景，有望加深我们对人类翻译过程的理解，并对更广泛的翻译研究领域及认知架构设计做出宝贵贡献。