When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or “heteroscedasticity”). We asked humans to perform a categorization task in which discrete, continuously valued samples (oriented gratings) arrived in series until the observer made a choice. Human behavior was best described by a model that adaptively weighted sensory signals by their inverse prediction error and integrated the resulting quantities with a linear urgency signal to a decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in heteroscedastic natural environments. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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人类在异方差世界中的可选停止。

在做出决定时，动物必须权衡信息收集的好处与长时间审议的机会成本。决定何时停止积累信息并做出选择在自然环境中具有挑战性，在自然环境中，与决策相关的信息的可靠性本身可能会随着时间的推移发生不可预测的变化（可变方差或“异方差性”）。我们要求人类执行一项分类任务，其中离散的、连续赋值的样本（定向光栅）依次到达，直到观察者做出选择。人类行为最好用一个模型来描述，该模型通过逆预测误差对感觉信号进行自适应加权，并将结果量与线性紧急信号整合到决策阈值。该模型近似于计算正确响应的完整后验概率的贝叶斯模型的输出，并成功地预测了神经信号中决策信息的自适应加权。决策信息的自适应加权可能已经演变为促进在异方差自然环境中的可选停止。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）