Bayesian optimal inference is often heralded as a principled, general framework for human perception. However, optimal inference requires integration over all possible world states, which quickly becomes intractable in complex real-world settings. Additionally, deviations from optimal inference have been observed in human decisions. A number of approximation methods have previously been suggested, such as sampling methods. In this study, we additionally propose point estimate observers, which evaluate only a single best estimate of the world state per response category. We compare the predicted behavior of these model observers to human decisions in five perceptual categorization tasks. Compared to the Bayesian observer, the point estimate observer loses decisively in one task, ties in two and wins in two tasks. Two sampling observers also improve upon the Bayesian observer, but in a different set of tasks. Thus, none of the existing general observer models appears to fit human perceptual decisions in all situations, but the point estimate observer is competitive with other observer models and may provide another stepping stone for future model development. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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点估计观察者：一类新的感知决策模型。

贝叶斯最优推理通常被誉为人类感知的原则性通用框架。然而，最佳推理需要整合所有可能的世界状态，这在复杂的现实世界设置中很快变得棘手。此外，在人类决策中已经观察到偏离最佳推理的情况。之前已经提出了许多近似方法，例如采样方法。在这项研究中，我们还提出了点估计观察者，它只评估每个响应类别的世界状态的单个最佳估计。我们将这些模型观察者的预测行为与五项感知分类任务中的人类决策进行了比较。与贝叶斯观察者相比，点估计观察者在一个任务中决定性地输了，在两个任务中并列并且在两个任务中获胜。两个抽样观察者也改进了贝叶斯观察者，但在一组不同的任务中。因此，现有的一般观察器模型似乎都不能在所有情况下都适合人类的感知决策，但点估计观察器与其他观察器模型相比具有竞争力，并可能为未来的模型开发提供另一个垫脚石。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。