Nature is in constant flux, so animals must account for changes in their environment when making decisions. How animals learn the timescale of such changes and adapt their decision strategies accordingly is not well understood. Recent psychophysical experiments have shown humans and other animals can achieve near-optimal performance at two alternative forced choice (2AFC) tasks in dynamically changing environments. Characterization of performance requires the derivation and analysis of computational models of optimal decision-making policies on such tasks. We review recent theoretical work in this area, and discuss how models compare with subjects' behavior in tasks where the correct choice or evidence quality changes in dynamic, but predictable, ways.

中文翻译：

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动态环境中决策的最佳模型。

大自然在不断变化，因此动物在做出决定时必须考虑环境的变化。动物如何了解此类变化的时间尺度并相应地调整其决策策略尚不清楚。最近的心理物理学实验表明，人类和其他动物可以在动态变化的环境中执行两种替代强制选择（2AFC）任务时取得接近最佳的表现。性能表征需要推导和分析此类任务的最佳决策策略的计算模型。我们回顾了该领域最近的理论工作，并讨论了模型如何与任务中受试者的行为进行比较，其中正确的选择或证据质量以动态但可预测的方式变化。