Department of General Psychology, Institute of Psychology, University of Kassel.
Department of Educational Psychology, Institute of Psychology, University of Würzburg.
J Exp Psychol Learn Mem Cogn. 2021 Jul;47(7):1083-1105. doi: 10.1037/xlm0001003. Epub 2021 Feb 1.
Research on study sequences has not considered the cross-classification of to-be-learned categories. In two experiments, we utilized cross-classified exemplars, which simultaneously belonged to categories of two orthogonal dimensions. Experiment 1 addressed the question of how interleaving one category dimension while simultaneous blocking another category dimension affects the induction of the simultaneously blocked category dimension. Experiment 2 examined our proposed mechanism by manipulating the degree of change (one-category vs. cross-category change) and the frequency of change (high vs. low) in the presentation sequence of exemplars with cross-classified characteristics. In Experiment 1, sequences that combined interleaving one dimension while blocking the other dimension were superior to sequences that provided no comparison opportunities when classifying both interleaved and blocked categories. This revealed a carry-over effect of interleaving: blocked and interleaved categories were equally well classified. Our findings are incompatible with the discriminative contrast hypothesis and the attentional bias framework, where interleaving is not assumed to support within-category comparisons. We explain the results according to the principle of change one category at a time (COCAT). Interleaving exemplars on one category dimension, but blocking them on another category dimension, enables learners to reliably map the distinctive features onto the covarying categories and the shared features onto the constant category. In contrast, there is a risk of confounding common characteristics when no comparison opportunities are given. Likewise, pure interleaving impedes category induction by confounding changing characteristics. Accordingly, Experiment 2 demonstrated that as long as a sequence implemented the COCAT principle, learners accurately identify diagnostic feature sets. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
研究学习序列时并未考虑到待学习类别的交叉分类。在两项实验中，我们利用了同时属于两个正交维度类别的交叉分类样本。实验 1 探讨了在同时阻断另一个类别维度的情况下，交错一个类别维度如何影响同时被阻断的类别维度的归纳。实验 2 通过操纵具有交叉分类特征的样本呈现序列中的变化程度（单类别与跨类别变化）和变化频率（高与低）来检验我们提出的机制。在实验 1 中，当对交错和被阻断的类别进行分类时，组合交错一个维度同时阻断另一个维度的序列优于没有提供比较机会的序列。这揭示了交错的传递效应：被阻断和交错的类别同样可以得到很好的分类。我们的发现与区分对比假设和注意力偏差框架不一致，因为交错不被认为支持类别内比较。我们根据一次改变一个类别的原则（COCAT）来解释结果。在另一个类别维度上阻断一个类别的样本，而在另一个类别维度上交错这些样本，使学习者能够可靠地将独特特征映射到共变类别上，将共享特征映射到恒定类别上。相比之下，如果没有比较机会，就有可能混淆共同特征。同样，纯粹的交错通过混淆变化特征阻碍了类别归纳。因此，实验 2 表明，只要序列实现了 COCAT 原则，学习者就能准确地识别出诊断特征集。（PsycInfo 数据库记录（c）2021 APA，保留所有权利）。
