In realistic and challenging decision contexts, people may show biases that prevent them from choosing their favored options. For example, astronomer Johannes Kepler famously interviewed several candidate fiancées sequentially, but was rejected when attempting to return to a previous candidate. Similarly, we examined human performance on searches for attractive faces through fixed-length sequences by adapting optimal stopping computational theory developed from behavioral ecology and economics. Although economics studies have repeatedly found that participants sample too few options before choosing the best-ranked number from a series, we instead found overlong searches with many sequences ending without choice. Participants employed irrationally high choice thresholds, compared to the more lax, realistic standards of a Bayesian ideal observer, which achieved better-ranked faces. We consider several computational accounts and find that participants most resemble a Bayesian model that decides based on altered attractiveness values. These values may produce starkly different biases in the facial attractiveness domain than in other decision domains.

中文翻译：

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寻找Mr(s) Right：决策偏差会阻止我们找到最有吸引力的面孔

在现实和具有挑战性的决策环境中，人们可能会表现出偏见，使他们无法选择自己喜欢的选项。例如，天文学家约翰内斯·开普勒（Johannes Kepler）曾先后采访了几位候选未婚妻，但在试图返回前一位候选人时遭到拒绝。同样，我们通过采用从行为生态学和经济学发展而来的最优停止计算理论，研究了人类在通过固定长度序列搜索有吸引力的面孔时的表现。尽管经济学研究一再发现参与者在从序列中选择排名最高的数字之前抽样的选项太少，但我们发现搜索时间过长，许多序列没有选择就结束了。与贝叶斯理想观察者更为宽松、现实的标准相比，参与者采用了不合理的高选择阈值，这实现了更好的人脸排名。我们考虑了几个计算账户，发现参与者最类似于贝叶斯模型，该模型基于改变的吸引力值来决定。这些值可能会在面部吸引力领域产生与其他决策领域截然不同的偏见。