Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
Department of Psychology, Princeton University, Princeton, NJ, USA.
Nat Hum Behav. 2021 Sep;5(9):1180-1189. doi: 10.1038/s41562-021-01065-0. Epub 2021 Mar 8.
How do we evaluate a group of people after a few negative experiences with some members but mostly positive experiences otherwise? How do rare experiences influence our overall impression? We show that rare events may be overweighted due to normative inference of the hidden causes that are believed to generate the observed events. We propose a Bayesian inference model that organizes environmental statistics by combining similar events and separating outlying observations. Relying on the model's inferred latent causes for group evaluation overweights rare or variable events. We tested the model's predictions in eight experiments where participants observed a sequence of social or non-social behaviours and estimated their average. As predicted, estimates were biased toward sparse events when estimating after seeing all observations, but not when tracking a summary value as observations accrued. Our results suggest that biases in evaluation may arise from inferring the hidden causes of group members' behaviours.
我们如何在少数负面经历与多数正面经历的情况下评价一群人？罕见的经历如何影响我们的整体印象？我们发现，由于对导致观察到的事件的潜在原因的规范性推断，罕见事件可能会被过度重视。我们提出了一种贝叶斯推断模型，通过结合相似事件和分离异常观测值来组织环境统计信息。依赖于该模型对群体评价的推断潜在原因，罕见或多变的事件会被高估。我们在八项实验中检验了该模型的预测，其中参与者观察了一系列社会或非社会行为，并对其平均值进行了估计。正如预测的那样，当观察到所有观察结果后进行估计时，估计会偏向于稀疏事件，但当随着观察结果的积累跟踪汇总值时则不会。我们的结果表明，评估中的偏差可能源于推断群体成员行为的潜在原因。
