Planning Beyond the Next Trial in Adaptive Experiments: A Dynamic Programming Approach.
Planning Beyond the Next Trial in Adaptive Experiments: A Dynamic Programming Approach.
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DOI：
10.1111/cogs.12467
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发表时间：
2017-11
影响因子：
2.5
通讯作者：
Myung JI
中科院分区：
文献类型：
--
作者：
Kim W;Pitt MA;Lu ZL;Myung JI
实验是科学探究的核心。在行为科学和神经科学中，通常只能进行有限数量的观察，因此理想的做法是设计一个实验，使所研究现象的信息迅速积累起来。适应性实验有可能通过在这种研究环境中最大化推论收益来加速科学进步。迄今为止，大多数适应性实验都依赖于短视的、一步先行的策略，在这种策略中，每次试验的刺激选择都是为了最大化对下一个试验的推断。该领域的一个挥之不去的问题是，在下一次试验之后，通过优化可以获得多少额外的好处。一系列技术挑战阻碍了这一重要问题得到充分解决。本研究将动态规划（DP）应用于基于模型的感知阈值估计，这是一种适用于全视界“全局”优化的技术，该领域一直是自适应方法的主要受益者。研究结果提供了对条件的深入了解，这些条件将在下一次试验之后从优化中受益。讨论了适应性方法在认知科学中的应用。
Experimentation is at the heart of scientific inquiry. In the behavioral and neural sciences, where only a limited number of observations can often be made, it is ideal to design an experiment that leads to the rapid accumulation of information about the phenomenon under study. Adaptive experimentation has the potential to accelerate scientific progress by maximizing inferential gain in such research settings. To date, most adaptive experiments have relied on myopic, one-step-ahead strategies in which the stimulus on each trial is selected to maximize inference on the next trial only. A lingering question in the field has been how much additional benefit would be gained by optimizing beyond the next trial. A range of technical challenges has prevented this important question from being addressed adequately. The present study applies dynamic programming (DP), a technique applicable for such full-horizon, “global” optimization, to model-based perceptual threshold estimation, a domain that has been a major beneficiary of adaptive methods. The results provide insight into conditions that will benefit from optimizing beyond the next trial. Implications for the use of adaptive methods in cognitive science are discussed.