Mueller Michael L, Dunlosky John, Tauber Sarah K
Department of Psychology, Kent State University, Kent, OH, 44242, USA,
Mem Cognit. 2015 Feb;43(2):180-92. doi: 10.3758/s13421-014-0474-2.
Knowledge updating occurs when people learn about the impacts of variables on memory after experiencing their effects. For instance, judgments of learning (JOLs) for encoding strategies (e.g., imagery and repetition) show no difference during a first study-test trial; however, during a second trial, JOLs better reflect the benefits of the more effective strategy. Although this outcome indicates some knowledge updating, JOLs on a second trial rarely update to reflect the full impact of a given variable. We investigated several explanations for this incomplete updating. Evidence using prestudy JOLs from Experiments 1 and 2 disconfirmed the encoding-disrupts-updating (EDU) hypothesis, which is that the experience of encoding items on the second trial disrupts the use of new knowledge in making JOLs. In Experiment 3, we used binary JOLs to evaluate whether the lack of updating is an artifact of people not wanting to use extreme ratings, which accounted for some-but not all-of the incomplete updating. Finally, in Experiment 4, immediately after the test on the initial trial, participants received feedback about how many items they had recalled for each level of the focal variable, and their JOLs on the second trial still showed incomplete updating. Taken together, the evidence suggests that incomplete knowledge updating on JOLs arises from multiple factors, including a scaling artifact and the deficient use of accurate knowledge when making JOLs.
当人们在体验变量对记忆的影响后了解到这些变量的影响时,知识更新就会发生。例如,在第一次学习 - 测试试验中,对编码策略(如图像和重复)的学习判断(JOLs)没有差异;然而,在第二次试验中,JOLs能更好地反映更有效策略的益处。尽管这一结果表明了一些知识更新,但第二次试验中的JOLs很少会更新以反映给定变量的全部影响。我们研究了这种不完全更新的几种解释。来自实验1和2的使用预学习JOLs的证据否定了编码干扰更新(EDU)假设,该假设认为在第二次试验中对项目进行编码的经历会干扰在进行JOLs时对新知识的使用。在实验3中,我们使用二元JOLs来评估缺乏更新是否是人们不想使用极端评分的人为现象,这解释了部分但不是全部的不完全更新。最后,在实验4中,在初始试验的测试之后,参与者立即收到关于他们针对焦点变量的每个水平回忆了多少项目的反馈,并且他们在第二次试验中的JOLs仍然显示出不完全更新。综合来看,证据表明JOLs上的不完全知识更新源于多种因素,包括量表人为现象以及在进行JOLs时对准确知识的使用不足。