Many real-world systems can exhibit tipping points and multiple stable states, creating the potential for sudden and difficult to reverse transitions into a less desirable regime. The theory of dynamical systems points to the existence of generic early warning signals that may precede these so-called critical transitions. Recently, psychologists have begun to conceptualize mental disorders such as depression as an alternative stable state, and suggested that early warning signals based on the phenomenon of critical slowing down might be useful for predicting transitions into depression and other psychiatric disorders. Harnessing the potential of early warning signals requires us to understand their limitations as well as the factors influencing their performance in practice. In this article, we (a) review limitations of early warning signals based on critical slowing down to better understand when they can and cannot occur, and (b) study the conditions under which early warning signals may anticipate critical transitions in online-monitoring settings by simulating from a bistable dynamical system, varying crucial features such as sampling frequency, noise intensity, and speed of approaching the tipping point. We find that, in sharp contrast to their reputation of being generic or model-agnostic, whether early warning signals occur or not strongly depends on the specifics of the system. We also find that they are very sensitive to noise, potentially limiting their utility in practical applications. We discuss the implications of our findings and provide suggestions and recommendations for future research.

中文翻译：

---

使用预警信号预测心理系统的关键转变：理论和实践考虑。

许多现实世界的系统可能会出现临界点和多个稳定状态，从而有可能突然且难以逆转地转变为不太理想的状态。动力系统理论指出，在这些所谓的关键转变之前可能存在通用预警信号。最近，心理学家开始将抑郁症等精神障碍概念化为另一种稳定状态，并提出基于严重放慢现象的早期预警信号可能有助于预测抑郁症和其他精神障碍的转变。利用预警信号的潜力需要我们了解其局限性以及影响其实际表现的因素。在本文中，我们（a）回顾了基于关键减速的预警信号的局限性，以更好地了解它们何时可以发生和不能发生，以及（b）研究预警信号可以预测在线监测设置中的关键转变的条件通过从双稳态动力系统进行模拟，改变采样频率、噪声强度和接近临界点的速度等关键特征。我们发现，与它们通用或与模型无关的声誉形成鲜明对比的是，早期预警信号是否出现在很大程度上取决于系统的具体情况。我们还发现它们对噪声非常敏感，可能限制它们在实际应用中的效用。我们讨论我们的研究结果的含义，并为未来的研究提供意见和建议。