The predictive processing framework (PPF) attempts to tackle deep philosophical problems, including how the brain generates consciousness, how our bodies influence cognition, and how cognition alters perception. As such, it provides a zeitgeist that incorporates concepts from physics, computer science, mathematics, artificial intelligence, economics, psychology, and neuroscience, leveraging and, in turn, influencing recent advances in reinforcement learning and deep learning that underpin the artificial intelligence in many of the applications with which we interact daily. PPF purports to provide no less than a grand unifying theory of mind and brain function, underwriting an account of perception, cognition, and action and their dynamic relationships. While mindful of legitimate criticisms of the framework, to which we return below, an important test of PPF is its utility in accounting for individual differences such as psychopathology. These, then, are the central concern of this special section of the Journal of Abnormal Psychology: What is the state of the art with regards to applying the PPF to the symptoms of mental illness? How might we leverage its insights to elevate and systematize our explanations, and ideally treatments, of those symptoms? And, conversely, can we refine and refute aspects of the PPF by considering the particular challenges that our patients experience as departures from the parametric estimates of the PPF? (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

---

当我们考虑预测处理时我们在想什么。

预测处理框架（PPF）试图解决深层的哲学问题，包括大脑如何产生意识、我们的身体如何影响认知以及认知如何改变感知。因此，它提供了一种时代精神，融合了物理学、计算机科学、数学、人工智能、经济学、心理学和神经科学的概念，利用并反过来影响强化学习和深度学习的最新进展，这些进展支撑着人工智能在许多领域的发展。我们每天与之交互的应用程序。PPF 旨在提供一个关于心智和大脑功能的宏大统一理论，支持对感知、认知和行动及其动态关系的解释。尽管我们注意到对该框架的合理批评（我们将在下面返回），但 PPF 的一个重要测试是它在解释诸如精神病理学等个体差异方面的效用。那么，这些就是《异常心理学杂志》这一特殊部分的中心关注点：将 PPF 应用于精神疾病症状的最新技术是什么？我们如何利用它的见解来提升和系统化我们对这些症状的解释以及理想的治疗方法？相反，我们是否可以通过考虑患者所经历的与 PPF 参数估计值背离的特殊挑战来完善和反驳 PPF 的各个方面？（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。