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首页> 外文期刊>Journal of Mathematical Psychology >How many dimensions underlie judgments of learning and recall redux: Consideration of recall latency reveals a previously hidden nonmonotonicity
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How many dimensions underlie judgments of learning and recall redux: Consideration of recall latency reveals a previously hidden nonmonotonicity

机译:学习和召回Redux的判断是多少尺寸:考虑召回延迟显示出先前隐藏的非单调性

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摘要

Jang and Nelson (2005) used state-trace analysis to examine factors that affect judgments of learning (JOLs) given as a prediction of future cued recall success. Koriat's (1997) cue-utilization framework predicted that intrinsic cues (e.g., item difficulty) would have approximately the same effects on recall as they would have on JOLs whereas extrinsic cues (e.g., number of presentations) would have greater effects on recall than on JOLs. In contradiction to the prediction from the cue-utilization framework, Jang and Nelson repeatedly found a monotonic state-trace solution, suggesting that a single latent variable (e.g., memory strength) explained both JOLs and recall. However, performance can be measured in many ways, and dissociations between JOLs and recall may arise from factors other than intrinsic and extrinsic cues. Thus, an apparent monotonic solution could be an artifact of the particular choice of behavioral measures or experimental manipulations. In light of this possibility, we reanalyzed Jang and Nelson's data and conducted a new experiment, considering recall latency as well as recall accuracy and including the manipulation of immediate versus delayed JOLs. Even when additionally including both immediate- and delayed-JOL conditions, state-trace analysis with JOL magnitude and recall accuracy generally suggested a single latent variable, except for a single case in which immediate JOLs produced high overconfidence. However, the state-trace results with JOL magnitude and recall latency primarily revealed a nonmonotonic function, indicating that more than one latent variable is needed to explain the relationship between JOLs and recall. (C) 2018 Elsevier Inc. All rights reserved.
机译:JANG和NELSON(2005)使用了国家追踪分析,检查了作为对未来召回成功的预测的学习(JOLS)判断的因素。韩国(1997年)提示利用框架预测,内在提示(例如,物品难度)对召回的效果大致相同的效果,因为它们在jols上有召回,而外在提示(例如,介绍数量)对召回的影响比上的效果更大jols。据矛盾与提示利用框架的预测,Jang和Nelson反复发现单调状态追踪解决方案,表明单个潜在的变量(例如,记忆强度)解释了JOL和召回。然而,可以在许多方面测量性能,并且jols和召回之间的解散可能会因内在和外部线索而以外的因素而产生。因此,表观单调溶液可以是行为措施或实验操纵的特定选择的伪影。鉴于这种可能性,我们重新分析了jang和尼尔森的数据,并考虑了召回延迟以及召回准确性并包括立即与延迟延迟的操纵,并包括延迟延迟延迟延迟延迟的延迟延迟的数据进行了新的实验。即使在另外包括立即和延迟的JOL条件下,具有JOL幅度的状态迹线分析通常也暗示了单个潜变量,除了单一的单一案例,即直接高速增长的高度交通。然而,具有JOL级别和召回延迟的状态跟踪结果主要揭示了非单调函数,表明需要多于一个潜在的变量来解释JOLS和召回之间的关系。 (c)2018年Elsevier Inc.保留所有权利。

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