Optimal models of decision-making in dynamic environments
Optimal models of decision-making in dynamic environments
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DOI：
10.1016/j.conb.2019.06.006
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发表时间：
2019-10-01
影响因子：
5.7
通讯作者：
Josic, Kresimir
中科院分区：
文献类型：
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作者：
Kilpatrick, Zachary P.;Holmes, William R.;Josic, Kresimir
大自然是不断变化的，所以动物在做决定时必须考虑环境的变化。动物如何学习这些变化的时间尺度，并相应地调整它们的决策策略，目前还没有很好的理解。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在两个可选的强迫选择（2AFC）任务中实现接近最佳的表现。性能的表征需要推导和分析计算模型的最佳决策政策，这些任务。我们回顾了最近在这一领域的理论工作，并讨论了模型如何与受试者的行为进行比较，在这些任务中，正确的选择或证据质量以动态但可预测的方式发生变化。
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.