Although the Bayesian paradigm is an important benchmark in studies of human inference, the extent to which it provides a useful framework to account for human behavior remains debated. We document systematic departures from Bayesian inference under correct beliefs, even on average, in the estimates by experimental subjects of the probability of a binary event following observations of successive realizations of the event. In particular, we find underreaction of subjects' estimates to the evidence ("conservatism") after only a few observations and at the same time overreaction after longer sequences of observations. This is not explained by an incorrect prior nor by many common models of Bayesian inference. We uncover the autocorrelation in estimates, which suggests that subjects carry imprecise representations of the decision situations, with noise in beliefs propagating over successive trials. But even taking into account these internal imprecisions and assuming various incorrect beliefs, we find that subjects' updates are inconsistent with the rules of Bayesian inference. We show how subjects instead considerably economize on the attention that they pay to the information relevant to the decision, and on the degree of control that they exert over their precise response, while giving responses fairly adapted to the task. A "noisy-counting" model of probability estimation reproduces the several patterns we exhibit in subjects' behavior. In sum, human subjects in our task perform reasonably well while greatly minimizing the amount of information that they pay attention to. Our results emphasize that investigating this economy of attention is crucial in understanding human decisions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

---

来自顺序数据的不精确概率推断。


尽管贝叶斯范式是人类推理研究的重要基准，但它在多大程度上为解释人类行为提供了有用的框架仍存在争议。我们记录了在正确信念下系统性地偏离贝叶斯推理，甚至平均而言，实验对象在观察事件的连续实现后对二元事件概率的估计。特别是，我们发现受试者的估计对证据的反应不足（“保守主义”），同时在较长时间的观察后反应过度。这不能用不正确的先验来解释，也不是用许多常见的贝叶斯推理模型来解释的。我们揭示了估计中的自相关，这表明受试者对决策情况的表示不精确，信念中的噪声在连续的试验中传播。但即使考虑到这些内部的不精确性并假设各种不正确的信念，我们发现受试者的更新与贝叶斯推理的规则不一致。我们展示了被试如何反而大大节省他们对与决策相关的信息的关注，以及他们对精确反应的控制程度，同时给出相当适合任务的回答。概率估计的“噪声计数”模型再现了我们在受试者行为中表现出的几种模式。总之，我们任务中的人类受试者表现得相当好，同时大大减少了他们关注的信息量。我们的结果强调，研究这种注意力经济对于理解人类决策至关重要。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。