When making decisions in complex environments we must selectively sample and process information with respect to task demands. Previous studies have shown that this requirement can manifest in the influence that extreme outcomes (i.e. values at the edges of a distribution) have on judgment and choice. We elucidate this influence via a task in which participants are presented, briefly, with an array of numbers and have to make one of two judgments. In 'preferential' judgments where the participants' goal was to choose between a safe, known outcome, and an unknown outcome drawn from the array, extreme-outcomes had a greater influence on choice than mid-range outcomes, especially under shorter time-limits. In 'perceptual' judgments where the participants' goal was to estimate the arrays' average, the influence of the extremes was less pronounced. A novel cognitive process model captures these patterns via a two-step selective-sampling and integration mechanism. Together our results shed light on how task goals modulate sampling from complex environments, show how sampling determines choice, and highlight the conflicting conclusions that arise from applying statistical and cognitive models to data.

中文翻译：

---

阐明极端结果在知觉和优先选择中的不同影响

在复杂环境中做出决策时，我们必须根据任务需求有选择地采样和处理信息。以前的研究表明，这一要求可以体现在极端结果（即分布边缘的值）对判断和选择的影响中。我们通过一项任务来阐明这种影响，在该任务中，参与者简要地展示了一系列数字，并且必须做出两个判断中的一个。在“优先”判断中，参与者的目标是在安全的已知结果和从数组中提取的未知结果之间进行选择，极端结果比中等结果对选择的影响更大，尤其是在较短的时间限制下. 在“感知”判断中，参与者的目标是估计阵列的平均值，极端事件的影响不那么明显。一种新的认知过程模型通过两步选择性采样和整合机制来捕捉这些模式。我们的结果共同阐明了任务目标如何调节复杂环境中的采样，展示了采样如何决定选择，并突出了将统计和认知模型应用于数据时产生的相互矛盾的结论。