Does complexity matter? Meta-analysis of learner performance in artificial grammar tasks.

Does complexity matter? Meta-analysis of learner performance in artificial grammar tasks.
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DOI:
10.3389/fpsyg.2014.01084
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发表时间:
2014
影响因子:
3.8
通讯作者:
Katan P
Katan P
中科院分区:
心理学3区
文献类型:
--
作者:
Schiff R;Katan P

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复杂性已被证明会影响人工语法学习(AGL)任务的表现(根据隐式训练的语法规则将测试项目分类为语法/非语法)。然而,以前发表的AGL实验并没有采用一致的措施来调查语法复杂性对任务绩效的综合影响。本研究将Bollt和Jones提出的拓扑熵(topological entropy,TE)计算技术计算机化,旨在考察语法系统的TE与学习者的AGL任务绩效之间的关系。我们调查了文献,并确定了56个以前的AGL实验的基础上,10个不同的语法,满足抽样标准。使用自动矩阵提升动作方法,我们为这10个先前使用的AGL系统中的每一个分配了TE值,并检查了其与学习者任务绩效的相关性。元回归分析表明,一个显着的相关性,表明复杂性的影响超越了不同的设置和条件下进行的分类任务。这些结果加强了在实验和评估AGL研究结果时使用这种新的自动化工具来统一测量语法系统复杂性的重要性。
Complexity has been shown to affect performance on artificial grammar learning (AGL) tasks (categorization of test items as grammatical/ungrammatical according to the implicitly trained grammar rules). However, previously published AGL experiments did not utilize consistent measures to investigate the comprehensive effect of grammar complexity on task performance. The present study focused on computerizing Bollt and Jones's technique of calculating topological entropy (TE), a quantitative measure of AGL charts' complexity, with the aim of examining associations between grammar systems' TE and learners' AGL task performance. We surveyed the literature and identified 56 previous AGL experiments based on 10 different grammars that met the sampling criteria. Using the automated matrix-lift-action method, we assigned a TE value for each of these 10 previously used AGL systems and examined its correlation with learners' task performance. The meta-regression analysis showed a significant correlation, demonstrating that the complexity effect transcended the different settings and conditions in which the categorization task was performed. The results reinforced the importance of using this new automated tool to uniformly measure grammar systems' complexity when experimenting with and evaluating the findings of AGL studies.
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