Repeated forecasts of changing values are common in many everyday tasks, from predicting the weather to financial markets. A particularly simple and informative instance of such fluctuating values are random walks: Sequences in which each point is a random movement from only its preceding value, unaffected by any previous points. Moreover, random walks often yield basic rational forecasting solutions in which predictions of new values should repeat the most recent value, and hence replicate the properties of the original series. In previous experiments, however, we have found that human forecasters do not adhere to this standard, showing systematic deviations from the properties of a random walk such as excessive volatility and extreme movements between subsequent predictions. We suggest that such deviations reflect general statistical signatures of cognition displayed across multiple tasks, offering a window into underlying mechanisms. Using these deviations as new criteria, we here explore several cognitive models of forecasting drawn from various approaches developed in the existing literature, including Bayesian, error-based learning, autoregressive, and sampling mechanisms. These models are contrasted with human data from two experiments to determine which best accounts for the particular statistical features displayed by participants. We find support for sampling models in both aggregate and individual fits, suggesting that these variations are attributable to the use of inherently stochastic prediction systems. We thus argue that variability in predictions is strongly influenced by computational noise within the decision making process, with less influence from "late" noise at the output stage. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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人们如何预测随机游走？人类认知模型的经验教训。


在许多日常任务中，从预测天气到金融市场，重复预测变化的值是很常见的。这种波动值的一个特别简单且信息丰富的实例是随机游走：其中每个点都是仅从其前一个值开始的随机移动，不受任何前一个点的影响。此外，随机游走通常会产生基本的有理预测解决方案，其中新值的预测应重复最近的值，从而复制原始序列的属性。然而，在之前的实验中，我们发现人类预测者并不遵守这个标准，显示出与随机游走特性的系统性偏差，例如过度波动和后续预测之间的极端波动。我们认为，这种偏差反映了在多个任务中显示的认知的一般统计特征，为了解潜在机制提供了一个窗口。使用这些偏差作为新标准，我们在这里探讨了从现有文献中开发的各种方法中提取的几种认知预测模型，包括贝叶斯、基于误差的学习、自回归和采样机制。这些模型与来自两个实验的人类数据进行对比，以确定哪些模型最能解释参与者显示的特定统计特征。我们发现在聚合拟合和个体拟合中都支持抽样模型，这表明这些变化归因于使用固有的随机预测系统。因此，我们认为预测的可变性受到决策过程中计算噪声的强烈影响，而输出阶段受 “后期 ”噪声的影响较小。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。