Insightful solutions often come about by recalling a relevant prior situation-one that shares the same essential relational pattern as the current problem. Unfortunately, our memory retrievals often depend primarily on surface matches, rather than relational matches. For example, a person who is familiar with the idea of positive feedback in sound systems may fail to think of it in the context of global warming. We suggest that one reason for the failure of cross-domain relational retrieval is that relational information is typically encoded variably, in a context-dependent way. In contrast, the surface features of that context-such as objects, animals and characters-are encoded in a relatively stable way, and are therefore easier to retrieve across contexts. We propose that the use of relational language can serve to make situations' relational representations more uniform, thereby facilitating relational retrieval. In two studies, we find that providing relational labels for situations at encoding or at retrieval increased the likelihood of relational retrieval. In contrast, domain labels-labels that highlight situations' contextual features-did not reliably improve domain retrieval. We suggest that relational language allows people to retrieve knowledge that would otherwise remain inert and contributes to domain experts' insight.

中文翻译：

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关系标签可释放惰性知识。

有洞察力的解决方案通常是通过回顾相关的先验情况而得出的，该情况具有与当前问题相同的基本关系模式。不幸的是，我们的内存检索通常主要依赖于表面匹配，而不是关系匹配。例如，一个熟悉声音系统中正反馈概念的人可能会在全球变暖的背景下不去想它。我们建议跨域关系检索失败的原因之一是关系信息通常以上下文相关的方式可变编码。相反，该上下文的表面特征（例如对象，动物和角色）以相对稳定的方式进行编码，因此更易于跨上下文检索。我们建议，使用关系语言可以帮助解决情况。关系表示更加统一，从而促进了关系检索。在两项研究中，我们发现在编码或检索时为情况提供相关标签会增加相关检索的可能性。相反，突出显示情境上下文特征的域标签并不能可靠地改善域检索。我们建议，关系语言可以使人们检索本来可以保持惰性并有助于领域专家洞察力的知识。上下文特征-无法可靠地改善域检索。我们建议，关系语言可以使人们检索本来可以保持惰性并有助于领域专家洞察力的知识。上下文特征-无法可靠地改善域检索。我们建议，关系语言可以使人们检索本来可以保持惰性并有助于领域专家洞察力的知识。