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：

---

从顺序数据中进行不精确的概率推断。


尽管贝叶斯范式是人类推理研究的重要基准，但它在多大程度上提供了解释人类行为的有用框架仍然存在争议。我们记录了在正确的信念下，甚至在平均情况下，实验对象在观察事件的连续实现后对二元事件的概率的估计中与贝叶斯推理的系统偏离。特别是，我们发现仅在几次观察后受试者对证据的估计反应不足（“保守主义”），同时在较长的观察序列后反应过度。这不能用不正确的先验或许多常见的贝叶斯推理模型来解释。我们发现了估计中的自相关性，这表明受试者对决策情况的表示不精确，并且信念中的噪音在连续的试验中传播。但即使考虑到这些内部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。我们展示了受试者如何大大节省对与决策相关的信息的注意力，以及他们对精确反应的控制程度，同时给出与任务相当适应的反应。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。总而言之，人类受试者在我们的任务中表现相当不错，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）。