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个体在知识共同体中的代表性。

Individual Representation in a Community of Knowledge.

机构信息

Department of Psychology, Tufts University, Medford, MA, USA.

Leeds School of Business, University of Colorado Boulder, Boulder, CO, USA.

出版信息

Trends Cogn Sci. 2019 Oct;23(10):891-902. doi: 10.1016/j.tics.2019.07.011. Epub 2019 Aug 30.

Abstract

An individual's knowledge is collective in at least two senses: it often comes from other people's testimony, and its deployment in reasoning and action requires accuracy underwritten by other people's knowledge. What must one know to participate in a collective knowledge system? Here, we marshal evidence that individuals retain detailed causal information for a few domains and coarse causal models embedding markers indicating that these details are available elsewhere (others' heads or the physical world) for most domains. This framework yields further questions about metacognition, source credibility, and individual computation that are theoretically and practically important. Belief polarization depends on the web of epistemic dependence and is greatest for those who know the least, plausibly due to extreme conflation of others' knowledge with one's own.

摘要

个体的知识至少在两个方面具有集体性

它通常来自他人的证言,而其在推理和行动中的运用则需要他人的知识来保证准确性。要参与一个集体知识系统,一个人必须知道什么?在这里,我们收集证据表明,个体对少数几个领域保留了详细的因果信息,而对大多数领域则有包含指示这些细节可在其他地方(他人的头脑或物理世界)获得的粗粒度因果模型。这个框架进一步提出了关于元认知、来源可信度和个体计算的问题,这些问题在理论和实践上都很重要。信念极化取决于认识上的相互依赖网络,而对于那些知道得最少的人来说,极化程度最大,这可能是由于极端混淆了他人的知识和自己的知识。

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