人际事件的解释:论平衡与因果关系的意义

U Rudolph, U von Hecker
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引用次数: 0

摘要

Brown和VanKleeck(1989)认为,人际事件的感知原因受两类因素的调节:第一,用于描述这些事件的人际动词带有关于哪一个潜在的互动伙伴引起了该事件的问题的隐含信息。第二,对人际事件的解释受平衡原则的支配。例如,积极事件主要由积极原因解释,而消极事件主要由消极原因解释。此外,这两种机制的相互作用也对社会事件的解释产生了重要的影响:(1)在平衡三合一中,一个事件被归因于被视为因果主导的互动伙伴(根据用于描述互动的动词的隐含因果关系)。(2)然而,对于不平衡的三联词,这种数据模式是相反的:在这里,根据动词的隐含因果关系,事件被归因于被视为因果关系较弱的互动伙伴。本研究探讨了这种归因转移是否可以用感知共变信息的相应变化来解释。结果表明,共识知觉和显著性知觉确实与因果归因相对应,因为它们是针对不同类型的三联音而获得的。因此,经典归因变量被认为是解释平衡与不平衡事件的归因转移的有希望的候选者。
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[Explanation of interpersonal events: on the significance of balance and causality].

According to Brown and VanKleeck (1989), the perceived causes of interpersonal events are mediated by two kinds of factors: First, the interpersonal verbs used to describe these events carry implicit information with regard to the question of which one of the potential interaction partners has caused the event. Second, explanations of interpersonal events are governed by the principle of balance. For example, positive events are predominantly explained by positive causes, and negative events by negative causes. In addition, the interaction of the two mechanisms also has important consequences concerning the explanation of social events: (1) In balanced triads, an event is ascribed to the interaction partner who is seen as the causally dominant one (according to the implicit causality of the verb that is used to describe the interaction). (2) However, this pattern of data is reversed for unbalanced triads: here, the event is ascribed to the interaction partner who is seen as the causally less dominant one, according to the implicit causality of the verb. The present study addresses the question of whether this attributional shift can be explained in terms of corresponding changes in perceived covariation information. Results indicate that the perception of consensus and distinctiveness indeed correspond to the causal attributions as they are obtained for different kinds of triads. Thus, classical attribution variables are regarded as promising candidates in order to explain these attributional shifts for balanced versus unbalanced events.

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