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Inhibiting beliefs demands attention

机译:抑制信念需要注意

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Research across a variety of domains has found that people fail to evaluate statistical information in an atheoretical manner. Rather, people tend to evaluate statistical information in light of their pre-existing beliefs and experiences. The locus of these biases continues to be hotly debated. In two experiments we evaluate the degree to which reasoning when relevant beliefs are readily accessible (i.e., when reasoning with Belief-Laden content) versus when relevant beliefs are not available (i.e., when reasoning with Non-Belief-Laden content) differentially demands attentional resources. In Experiment 1 we found that reasoning with scenarios that contained Belief-Laden content required fewer attentional resources than reasoning with scenarios that contained Non-Belief-Laden content, as evidenced by smaller costs on a secondary memory load task for the former than the latter. This trend was reversed in Experiment 2 when participants were instructed to ignore their beliefs when reasoning with Belief-Laden and Non-Belief-Laden scenarios. These findings provide evidence that beliefs automatically influence reasoning, and attempting to ignore them comes with an attentional cost.
机译:跨领域的研究发现,人们无法以一种理论的方式评估统计信息。相反,人们倾向于根据其先前的信念和经验来评估统计信息。这些偏见的根源仍在争论中。在两个实验中,我们评估了何时可以轻松访问相关信念(即,使用充满信心的内容进行推理)与不可用相关信念(即,使用非信任的内容进行推理)时需要注意的推理程度资源。在实验1中,我们发现,与包含非满载内容的场景相比,使用包含满载内容的场景进行推理所需的注意力资源更少,这由前者在次要存储器加载任务上的花费要比后者少证明了这一点。这种趋势在实验2中得到了扭转,当时要求参与者在使用“充满信仰”和“非充满信仰”场景进行推理时忽略他们的信仰。这些发现提供了证据,即信念会自动影响推理,而试图忽略它们会引起注意的代价。

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