Peters Uwe, Krauss Alexander, Braganza Oliver
Leverhulme Centre for the Future of Intelligence, University of Cambridge.
Center for Science and Thought, University of Bonn.
Cogn Sci. 2022 Sep;46(9):e13188. doi: 10.1111/cogs.13188.
Many scientists routinely generalize from study samples to larger populations. It is commonly assumed that this cognitive process of scientific induction is a voluntary inference in which researchers assess the generalizability of their data and then draw conclusions accordingly. We challenge this view and argue for a novel account. The account describes scientific induction as involving by default a generalization bias that operates automatically and frequently leads researchers to unintentionally generalize their findings without sufficient evidence. The result is unwarranted, overgeneralized conclusions. We support this account of scientific induction by integrating a range of disparate findings from across the cognitive sciences that have until now not been connected to research on the nature of scientific induction. The view that scientific induction involves by default a generalization bias calls for a revision of the current thinking about scientific induction and highlights an overlooked cause of the replication crisis in the sciences. Commonly proposed interventions to tackle scientific overgeneralizations that may feed into this crisis need to be supplemented with cognitive debiasing strategies against generalization bias to most effectively improve science.
许多科学家经常从研究样本推广到更大的总体。人们通常认为,这种科学归纳的认知过程是一种自愿推理,研究人员在其中评估数据的可推广性,然后据此得出结论。我们对这一观点提出质疑,并主张一种新的解释。这种解释将科学归纳描述为默认情况下涉及一种泛化偏差,这种偏差会自动起作用,并经常导致研究人员在没有充分证据的情况下无意中推广他们的发现。结果就是得出毫无根据、过度泛化的结论。我们通过整合认知科学领域一系列不同的研究结果来支持这种对科学归纳的解释,这些结果迄今为止尚未与关于科学归纳本质的研究联系起来。科学归纳默认涉及泛化偏差的观点要求对当前关于科学归纳的思维进行修正,并凸显了科学领域复制危机中一个被忽视的原因。为应对可能助长这场危机的科学过度泛化而普遍提出的干预措施,需要辅以针对泛化偏差的认知去偏策略,以最有效地改进科学。