ABSTRACT

We introduce a heuristic called pseudocontingencies (PCs) as an alternative account of various stereotyping phenomena. PCs give rise to the expectation that attributes are correlated based solely on asymmetries in attribute base rates. Attributes that are encountered frequently and attributes that are encountered rarely are perceived to be correlated with each other. Such differences in information densities are typical of many stereotyped targets, including the self vs. others, the in-group vs. out-groups and majority vs. minority groups. Evidence is reviewed for PCs underlying illusory correlations, confirmation biases, gender stereotypes, Simpson’s paradox, and in procedures used for implicit stereotype measurement. PCs are shown to predict specific patterns of self-enhancement and self-depreciation, the effects of intergroup contact on in-group biases and the readiness to infer stereotypes from aggregated “big-data”. Although PCs can lead to seriously flawed stereotypic expectations, they afford an efficient and possibly adaptive inference strategy.

摘要

我们介绍了一种称为伪意外事件（PC）的启发式方法，作为对各种陈规定型现象的替代解释。PC期望仅基于属性基本速率的不对称性来关联属性。经常遇到的属性和很少遇到的属性被认为是相互关联的。信息密度的这种差异是许多定型目标的典型特征，包括自我与其他，团体内与团体，多数与少数族裔。审查了有关虚假关联，确认偏差，性别刻板印象，辛普森悖论以及隐式刻板印象测量程序中的PC的证据。PC可以预测自我增强和自我贬值的特定模式，组间联系对组内偏见的影响以及从汇总的“大数据”中推断定型观念的意愿。尽管PC可能导致刻板印象的预期严重错误，但它们提供了有效且可能自适应的推理策略。