We present a predictive account on adaptive sequential sampling of stimulus-response relations in psychophysical experiments. Our discussion applies to experimental situations with ordinal stimuli when there is only weak structural knowledge available such that parametric modeling is no option. By introducing a certain form of partial exchangeability, we successively develop a hierarchical Bayesian model based on a mixture of Pólya urn processes. Suitable utility measures permit us to optimize the overall experimental sampling process. We provide several measures that are either based on simple count statistics or more elaborate information theoretic quantities. The actual computation of information theoretic utilities often turns out to be infeasible. This is not the case with our sampling method, which relies on an efficient algorithm to compute exact solutions of our posterior predictions and utility measures. Finally, we demonstrate the advantages of our framework on a hypothetical sampling problem.

中文翻译：

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一种用于心理物理实验的自适应顺序采样的非参数推理的预测方法

我们在心理物理实验中提出了对刺激-反应关系的自适应顺序采样的预测说明。我们的讨论适用于具有序数刺激的实验情况，当只有微弱的结构知识可用时，参数化建模是没有选择的。通过引入某种形式的部分可交换性，我们相继开发了一个基于 Pólya urn 过程混合的分层贝叶斯模型。合适的效用措施使我们能够优化整个实验采样过程。我们提供了几种基于简单计数统计或更复杂的信息理论量的度量。信息理论效用的实际计算通常被证明是不可行的。我们的抽样方法不是这种情况，它依赖于一种有效的算法来计算我们的后验预测和效用度量的精确解。最后，我们展示了我们的框架在假设抽样问题上的优势。