Mettler Everett, Massey Christine M, Kellman Philip J
Department of Psychology, University of California, Los Angeles.
Institute for Research in Cognitive Science, University of Pennsylvania.
J Exp Psychol Gen. 2016 Jul;145(7):897-917. doi: 10.1037/xge0000170. Epub 2016 Apr 28.
Understanding and optimizing spacing during learning is a central topic for research in learning and memory and has substantial implications for real-world learning. Spacing memory retrievals across time improves memory relative to massed practice-the well-known spacing effect. Most spacing research has utilized fixed (predetermined) spacing intervals. Some findings indicate advantages of expanding over equal spacing (e.g., Landauer & Bjork, 1978); however, evidence is mixed (e.g., Karpicke & Roediger, 2007), and the field has lacked an integrated explanation. Learning may instead depend on interactions of spacing with an underlying variable of learning strength that varies for learners and items, and it may be better optimized by adaptive adjustments of spacing to learners' ongoing performance. Two studies investigated an adaptive spacing algorithm, Adaptive Response-Time-based Sequencing or ARTS (Mettler, Massey & Kellman, 2011) that uses response-time and accuracy to generate spacing. Experiment 1 compared adaptive scheduling with fixed schedules having either expanding or equal spacing. Experiment 2 compared adaptive schedules to 2 fixed "yoked" schedules that were copied from adaptive participants, equating average spacing across conditions. In both experiments, adaptive scheduling outperformed fixed conditions at immediate and delayed tests of retention. No evidence was found for differences between expanding and equal spacing. Yoked conditions showed that learning gains were due to adaptation to individual items and learners. Adaptive spacing based on ongoing assessments of learning strength yields greater learning gains than fixed schedules, a finding that helps to understand the spacing effect theoretically and has direct applications for enhancing learning in many domains. (PsycINFO Database Record
理解并优化学习过程中的间隔是学习与记忆研究的核心课题,对现实世界中的学习具有重要意义。相对于集中练习,分散记忆检索时间能提高记忆力,这就是著名的间隔效应。大多数间隔研究采用固定(预先确定)的间隔时间。一些研究结果表明扩展间隔比等距间隔更具优势(例如,Landauer & Bjork,1978);然而,证据并不一致(例如,Karpicke & Roediger,2007),该领域缺乏一个综合的解释。学习可能反而取决于间隔与学习强度这一潜在变量的相互作用,而学习强度会因学习者和学习项目的不同而变化,通过根据学习者当前表现对间隔进行自适应调整,可能会实现更好的优化。两项研究调查了一种自适应间隔算法,即基于响应时间的自适应排序算法(ARTS,Mettler、Massey & Kellman,2011),该算法利用响应时间和准确性来生成间隔。实验1将自适应调度与具有扩展间隔或等距间隔的固定调度进行了比较。实验2将自适应调度与从自适应参与者那里复制的两个固定“匹配”调度进行了比较,使各条件下的平均间隔相等。在这两个实验中,在即时和延迟的记忆测试中,自适应调度的表现均优于固定条件。未发现扩展间隔和等距间隔之间存在差异的证据。匹配条件表明,学习收益源于对个体项目和学习者的适应。基于对学习强度的持续评估的自适应间隔比固定调度能带来更大的学习收益,这一发现有助于从理论上理解间隔效应,并在许多领域对增强学习有直接应用。(PsycINFO数据库记录)