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运用道德与社会信念的认知网络模型来解释信念的改变。

Using a cognitive network model of moral and social beliefs to explain belief change.

作者信息

Dalege Jonas, van der Does Tamara

机构信息

Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA.

出版信息

Sci Adv. 2022 Aug 19;8(33):eabm0137. doi: 10.1126/sciadv.abm0137.

Abstract

Skepticism toward childhood vaccines and genetically modified food has grown despite scientific evidence of their safety. Beliefs about scientific issues are difficult to change because they are entrenched within many interrelated moral concerns and beliefs about what others think. We propose a cognitive network model that estimates network ties between all interrelated beliefs to calculate the overall dissonance and interdependence. Using a probabilistic nationally representative longitudinal study, we test whether our model can be used to predict belief change and find support for our model's predictions: High network dissonance predicts subsequent belief change, and people are driven toward lower network dissonance. We show the advantages of measuring dissonance using the belief network structure compared to traditional measures. This study is the first to combine a unifying predictive model with an experimental intervention and to shed light on the dynamics of dissonance reduction leading to belief change.

摘要

尽管有科学证据证明儿童疫苗和转基因食品的安全性,但对它们的怀疑态度仍在增加。关于科学问题的信念很难改变,因为它们深深植根于许多相互关联的道德关切以及对他人想法的信念之中。我们提出了一种认知网络模型,该模型估计所有相互关联信念之间的网络联系,以计算总体失调和相互依存度。通过一项具有全国代表性的概率纵向研究,我们测试了我们的模型是否可用于预测信念变化,并为我们模型的预测找到了支持:高网络失调预测随后的信念变化,并且人们会被驱动趋向于降低网络失调。我们展示了与传统测量方法相比,使用信念网络结构测量失调的优势。本研究首次将统一的预测模型与实验干预相结合,并揭示了导致信念变化的失调减少动态过程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/724e/9390990/2df6749169cc/sciadv.abm0137-f1.jpg

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