Consider the following two (hypothetical) generic causal claims: “Living in a neighborhood with many families with children increases purchases of bicycles” and “living in an affluent neighborhood with many families with children increases purchases of bicycles.” These claims not only differ in what they suggest about how bicycle ownership is distributed across different neighborhoods (i.e., “the data”), but also have the potential to communicate something about the speakers’ values: namely, the prominence they accord to affluence in representing and making decisions about the social world. Here, we examine the relationship between the level of granularity with which a cause is described in a generic causal claim (e.g., neighborhood vs. affluent neighborhood) and the value of the information contained in the causal model that generates that claim. We argue that listeners who know any two of the following can make reliable inferences about the third: 1) the level of granularity at which a speaker makes a generic causal claim, 2) the speaker’s values, and 3) the data available to the speaker. We present results of four experiments ( = 1323) in the domain of social categories that provide evidence in keeping with these predictions.

中文翻译：

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告诉我你的（认知）预算，我会告诉你你看重什么

考虑以下两个（假设的）通用因果关系：“居住在有许多有孩子的家庭的社区会增加自行车的购买量”和“生活在有许多有孩子的家庭的富裕社区会增加自行车的购买量”。这些说法不仅在自行车拥有量如何在不同社区之间分配（即“数据”）方面有所不同，而且有可能传达有关发言者价值观的信息：即，他们对富裕程度的重视程度。代表社会并做出有关社会的决定。在这里，我们检查一般因果主张（例如，邻里与富裕邻里）中描述原因的粒度级别与生成该主张的因果模型中包含的信息值之间的关系。我们认为，了解以下任意两项的听众可以对第三项做出可靠的推论：1）演讲者提出一般因果主张的粒度水平，2）演讲者的价值观，以及 3）演讲者可用的数据。我们展示了社会类别领域的四项实验 (= 1323) 的结果，这些实验提供了与这些预测一致的证据。