We design an induced value choice experiment where the objects are valued according to only a single attribute with a continuous measure. Subjects have an imperfect perception of the choice objects but can reduce their imperfect perception with cognitive effort. Subjects are given a choice set involving several lines of various lengths and are told to select one of them. They strive to select the longest line because they are paid an amount that is increasing in the length of their selection. This ”idealized” choice experiment produces a dataset that is uniquely suited to study apparently random choice. We also manipulate the available cognitive resources of the subjects by imposing either a high or low cognitive load. We find that both choices and the allocation of effort are affected by the material incentives in the choice problem and the available cognitive resources. We find evidence that optimal choices have shorter deliberation times than suboptimal choices, which is consistent with previous theoretical predictions. The distribution of errors can have significant implications for the specification of stochastic choice models. Specifications where errors have a Gumbel distribution appear to provide a better fit than those with a normal distribution. Despite that the cognitive load manipulation affects both choice and search, it is notable that neither the Gumbel distribution results nor the relationship between optimal choice and deliberation time appear to be affected by the available cognitive resources. This perhaps suggests that these results are general and persistent features of choice.

我们设计了一个诱导值选择实验，在该实验中，仅根据具有连续度量的单个属性对对象进行赋值。受试者对选择对象的感知不完善，但可以通过认知努力减少其感知不完善。给受试者一个选择集，该选择集涉及几条不同长度的线，并被告知选择其中之一。他们努力选择最长的线，因为他们得到的报酬随着其选择时间的增加而增加。这个“理想化”的选择实验产生了一个独特的数据集，该数据集非常适合研究表面上的随机选择。我们还通过施加较高或较低的认知负荷来操纵对象的可用认知资源。我们发现选择和努力的分配都受到选择问题中物质激励和可用认知资源的影响。我们发现有证据表明，与次优选择相比，最优选择的审议时间更短，这与先前的理论预测是一致的。错误的分布可能对随机选择模型的规范产生重大影响。误差具有Gumbel分布的规范似乎比正态分布的规范更适合。尽管认知负荷操纵会影响选择和搜索，但值得注意的是，无论是Gumbel分布结果还是最佳选择与思考时间之间的关系都不会受到可用认知资源的影响。