群体交叉分类效应的代数模型及其潜在加工过程

The Algebraic Models and the Underlying Processes of Crossed Categorization

交叉分类被认为是一种能够有效减少群际偏见的方法。交叉分类效应的代数模型主要包括平均模型、相加模型和等价模型,各个模型都有自己成立的证据,其中支持相加模型的研究证据最多。尽管如此,交叉分类效应的代数模型有很明显的局限,即要求所有进行交叉的类别维度权重相等,这与真实社会情境是相矛盾的。交叉分类的潜在加工过程主要有基于认知角度的类别差异模型和基于动机角度的社会认同两种比较成熟的理论,前者又分为类别差异消除模型和类别差异减少模型。

Crossed categorization has generally been considered as a way to effectively reducing intergroup bias. The algebraic models of crossed categorization included average pattern, additive pattern and equivalence, each of which had its evidence and among which the additive model received most approval. However, there were obvious limitations in the algebraic models of crossed categorization; that was, requiring all the crossed categories were weighted equally, which was not the case with social categories in real world. The underlying processes of crossed categorization mostly referred to category differentiation process in cognitive approach and social identity theory in motivational approach, and the former was classified into category differentiation elimination model and category differentiation reduction model .