Measuring Individual Differences in Implicit Learning with Artificial Grammar Learning Tasks: Conceptual and Methodological Conundrums

Measuring Individual Differences in Implicit Learning with Artificial Grammar Learning Tasks: Conceptual and Methodological Conundrums
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通过人工语法学习任务测量内隐学习的个体差异:概念和方法难题

DOI:
10.1027/2151-2604/a000280
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发表时间:
2017
期刊:
Zeitschrift für Psychologie
影响因子:
--
通讯作者:
Hagemann
Hagemann
中科院分区:
--
文献类型:
--
作者:
Danner;Hagemann

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内隐学习可以定义为没有意图或意识的学习。我们从概念上讨论和实证研究如何内隐学习的个体差异可以测量人工语法学习(AGL)任务。我们解决是否应指示参与者对字母串的语法性或新奇进行评级,并研究知识测试对测量质量的影响。我们讨论这些问题,从概念的角度来看,并报告三个实验表明,(1)AGL的可靠性是温和的,太低的个人评估,(2)知识测试降低了任务的一致性,并增加了可报告的语法知识的相关性,(3)在AGL任务的性能是独立的一般智力和教育程度。
Implicit learning can be defined as learning without intention or awareness. We discuss conceptually and investigate empirically how individual differences in implicit learning can be measured with artificial grammar learning (AGL) tasks. We address whether participants should be instructed to rate the grammaticality or the novelty of letter strings and look at the impact of a knowledge test on measurement quality. We discuss these issues from a conceptual perspective and report three experiments which suggest that (1) the reliability of AGL is moderate and too low for individual assessments,(2) a knowledge test decreases task consistency and increases the correlation with reportable grammar knowledge, and (3) performance in AGL tasks is independent from general intelligence and educational attainment.
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