Department of Psychology, Harvard University, Cambridge, MA, USA.
Center for Brains, Minds, and Machines, MIT, Cambridge, MA, USA.
Psychon Bull Rev. 2023 Aug;30(4):1273-1293. doi: 10.3758/s13423-023-02252-y. Epub 2023 Mar 27.
Why, when, and how do stereotypes change? This paper develops a computational account based on the principles of structure learning: stereotypes are governed by probabilistic beliefs about the assignment of individuals to groups. Two aspects of this account are particularly important. First, groups are flexibly constructed based on the distribution of traits across individuals; groups are not fixed, nor are they assumed to map on to categories we have to provide to the model. This allows the model to explain the phenomena of group discovery and subtyping, whereby deviant individuals are segregated from a group, thus protecting the group's stereotype. Second, groups are hierarchically structured, such that groups can be nested. This allows the model to explain the phenomenon of subgrouping, whereby a collection of deviant individuals is organized into a refinement of the superordinate group. The structure learning account also sheds light on several factors that determine stereotype change, including perceived group variability, individual typicality, cognitive load, and sample size.
刻板印象为什么会改变？又是在何时以及如何改变？本文基于结构学习原理，提出了一种计算方法：刻板印象是由关于个体在群体中分配的概率信念所决定的。该方法有两个特别重要的方面。首先，群体是根据个体之间的特征分布灵活构建的；群体不是固定的，也不假定与我们必须提供给模型的类别相对应。这使得模型能够解释群体发现和细分的现象，即异常个体与群体分离，从而保护群体的刻板印象。其次，群体是分层结构的，因此群体可以嵌套。这使得模型能够解释亚群的现象，即一群异常个体被组织成对上级群体的细化。结构学习方法还解释了几个决定刻板印象变化的因素，包括感知的群体可变性、个体典型性、认知负荷和样本大小。
