Burson Katherine A, Larrick Richard P, Klayman Joshua
Ross School of Business, University of Michigan, Ann Arbor, MI 48109, USA.
J Pers Soc Psychol. 2006 Jan;90(1):60-77. doi: 10.1037/0022-3514.90.1.60.
People are inaccurate judges of how their abilities compare to others'. J. Kruger and D. Dunning (1999, 2002) argued that unskilled performers in particular lack metacognitive insight about their relative performance and disproportionately account for better-than-average effects. The unskilled overestimate their actual percentile of performance, whereas skilled performers more accurately predict theirs. However, not all tasks show this bias. In a series of 12 tasks across 3 studies, the authors show that on moderately difficult tasks, best and worst performers differ very little in accuracy, and on more difficult tasks, best performers are less accurate than worst performers in their judgments. This pattern suggests that judges at all skill levels are subject to similar degrees of error. The authors propose that a noise-plus-bias model of judgment is sufficient to explain the relation between skill level and accuracy of judgments of relative standing.
人们在判断自己与他人能力的比较上并不准确。J. 克鲁格和D. 邓宁（1999年、2002年）认为，尤其是缺乏技能的执行者对自己的相对表现缺乏元认知洞察力，并且不成比例地导致了高于平均水平的效应。缺乏技能的人高估了自己实际的表现百分位，而有技能的执行者则能更准确地预测自己的表现百分位。然而，并非所有任务都显示出这种偏差。在三项研究中的一系列12项任务中，作者表明，在中等难度的任务上，表现最好和最差的人在准确性上差异很小，而在更难的任务上，表现最好的人在判断上比表现最差的人更不准确。这种模式表明，所有技能水平的判断者都存在相似程度的误差。作者提出，一个判断的噪声加偏差模型足以解释技能水平与相对地位判断准确性之间的关系。
