Human-Computer-Media Institute, Julius-Maximilians-University Würzburg, Oswald-Külpe-Weg 82, 97074, Würzburg, Germany.
Department of Psychology, University of Regensburg, Regensburg, Germany.
Sci Rep. 2024 Mar 7;14(1):5615. doi: 10.1038/s41598-024-55930-9.
Human information processing is not always rational but influenced by prior attitudes, a phenomenon commonly known as motivated reasoning. We conducted two studies (N = 556, N = 1198; UK samples) investigating motivated reasoning in the context of climate change with a focus on individual differences as potential moderating factors. While previous research investigated motivated reasoning regarding the debate whether climate change is anthropogenic, we focused on current discourses about the effectiveness of different countermeasures. To this end, participants evaluated fictitious scientific data on the effectiveness of regulations to reduce CO emissions. In both studies, participants exhibited motivated reasoning as indicated by the observation that prior attitudes about CO reduction policies predicted evaluation of the scientific data. The degree of motivated reasoning was not related to individual difference variables, namely the ability to understand and reason with numbers (Numeracy), the willingness to show this ability (Need for Cognition), and the tendency to maximize one's individual utility (Dark Factor of Personality). However, numeracy was associated with a less biased interpretation of the presented information. Our research demonstrates that motivated reasoning is a general phenomenon, and points to numerical training as one way to improve reasoning.
人类信息处理并不总是理性的，而是会受到先前态度的影响，这种现象通常被称为动机推理。我们进行了两项研究（N=556，N=1198；英国样本），以气候变化为背景，研究动机推理，重点关注个体差异作为潜在的调节因素。虽然之前的研究调查了关于气候变化是否人为引起的辩论中的动机推理，但我们关注的是关于不同应对措施有效性的当前论述。为此，参与者评估了关于减少 CO2 排放法规有效性的虚构科学数据。在两项研究中，参与者都表现出了动机推理，这表明对 CO2 减排政策的先前态度预测了对科学数据的评估。动机推理的程度与个体差异变量无关，即理解和运用数字的能力（数理性）、表现出这种能力的意愿（认知需求）以及最大化个人效用的倾向（人格的黑暗因素）。然而，数理性与对所呈现信息的更无偏差的解释有关。我们的研究表明，动机推理是一种普遍现象，并指出数字培训是提高推理能力的一种方法。
