Tauber Sarah K, Dunlosky John
Department of Psychology, Texas Christian University, USA.
Department of Psychology, Kent State University, USA.
Acta Psychol (Amst). 2015 Feb;155:8-18. doi: 10.1016/j.actpsy.2014.11.011. Epub 2014 Dec 18.
Researchers have recently begun to investigate people's ability to monitor their learning of natural categories. For concept learning tasks, a learner seeks to accurately monitor learning at the category level - i.e., to accurately judge whether exemplars will be correctly classified into the appropriate category on an upcoming test. Our interest was in whether monitoring resolution at the category level would improve as participants gain task experience across multiple study-test blocks, as well as within each block. In four experiments, exemplar birds (e.g., American Goldfinch, Cassin's Finch) paired with each family name (e.g., Finch) were studied, and participants made a judgment of learning (JOL) for each exemplar. Of most interest, before and after studying the exemplars, participants made category learning judgments (CLJs), which involved predicting the likelihood of correctly classifying novel birds into each family. Tests included exemplars that had been studied or exemplars that had not been studied (novel). This procedure was repeated for either one or two additional blocks. The relative accuracy of CLJs did not improve across blocks even when explicit feedback was provided, whereas item-by-item JOL accuracy improved across blocks. Category level resolution did improve from pre-study to post-study on an initial block, but it did not consistently increase within later blocks. The stable accuracy of CLJs across blocks poses a theoretical and empirical challenge for identifying techniques to improve people's ability to judge their learning of natural categories.
研究人员最近开始研究人们监测自身对自然类别学习情况的能力。对于概念学习任务，学习者试图在类别层面准确监测学习情况，也就是说，准确判断在即将到来的测试中示例是否会被正确分类到相应类别中。我们感兴趣的是，随着参与者在多个学习 - 测试块中积累任务经验，以及在每个块内，类别层面的监测分辨率是否会提高。在四项实验中，研究了与每个家族名称（如雀类）配对的示例鸟类（如美洲金翅雀、卡辛氏朱雀），参与者对每个示例做出学习判断（JOL）。最令人感兴趣的是，在研究示例之前和之后，参与者做出类别学习判断（CLJ），这涉及预测将新鸟类正确分类到每个家族的可能性。测试包括已研究过的示例或未研究过的示例（新示例）。这个过程再重复一到两个块。即使提供了明确反馈，CLJ的相对准确性在各个块之间也没有提高，而逐项目的JOL准确性在各个块之间有所提高。在初始块中，从学习前到学习后，类别层面的分辨率确实有所提高，但在后续块中并没有持续增加。CLJ在各个块之间的稳定准确性对确定提高人们判断自身对自然类别学习能力的技术构成了理论和实证挑战。
