To make informed decisions in natural environments that change over time, humans must update their beliefs as new observations are gathered. Studies exploring human inference as a dynamical process that unfolds in time have focused on situations in which the statistics of observations are history-independent. Yet, temporal structure is everywhere in nature and yields history-dependent observations. Do humans modify their inference processes depending on the latent temporal statistics of their observations? We investigate this question experimentally and theoretically using a change-point inference task. We show that humans adapt their inference process to fine aspects of the temporal structure in the statistics of stimuli. As such, humans behave qualitatively in a Bayesian fashion but, quantitatively, deviate away from optimality. Perhaps more importantly, humans behave suboptimally in that their responses are not deterministic, but variable. We show that this variability itself is modulated by the temporal statistics of stimuli. To elucidate the cognitive algorithm that yields this behavior, we investigate a broad array of existing and new models that characterize different sources of suboptimal deviations away from Bayesian inference. While models with "output noise" that corrupts the response-selection process are natural candidates, human behavior is best described by sampling-based inference models, in which the main ingredient is a compressed approximation of the posterior, represented through a modest set of random samples and updated over time. This result comes to complement a growing literature on sample-based representation and learning in humans. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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在具有时间结构的变化环境中的人类推理。

为了在随时间变化的自然环境中做出明智的决定，人类必须在收集到新的观察结果时更新他们的信念。探索人类推理作为随时间展开的动态过程的研究集中在观察统计与历史无关的情况。然而，时间结构在自然界中无处不在，并且会产生依赖于历史的观察结果。人类是否根据观察的潜在时间统计来修改他们的推理过程？我们使用变点推理任务从实验和理论上研究了这个问题。我们表明，人类将他们的推理过程调整到刺激统计中时间结构的精细方面。因此，人类在质量上以贝叶斯方式行事，但在数量上偏离最优性。也许更重要的是，人类的行为不是最理想的，因为他们的反应不是确定性的，而是可变的。我们表明这种可变性本身是由刺激的时间统计调制的。为了阐明产生这种行为的认知算法，我们研究了大量现有的和新的模型，这些模型表征了偏离贝叶斯推理的次优偏差的不同来源。虽然具有破坏响应选择过程的“输出噪声”的模型是自然的候选者，但基于采样的推理模型可以最好地描述人类行为，其中主要成分是后验的压缩近似，通过适度的随机数表示样本并随时间更新。这一结果补充了越来越多关于人类基于样本的表示和学习的文献。