Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory
Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory
复制标题
预测最佳学习间隔：记忆的多尺度上下文模型
DOI：
10.1037/e520562012-290
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发表时间：
2009
影响因子：
30.7
通讯作者：
E. Vul
中科院分区：
文献类型：
--
作者：
M. Mozer;H. Pashler;N. Cepeda;Robert V. Lindsey;E. Vul
当人们了解事实时（例如，外语词汇）的学习时间间隔对记忆保持有显著影响。行为实验表明，间距和保留之间的非单调关系：短或长的学习时间间隔产生较低的线索回忆准确率比中间间隔。适当的学习间隔可以在教育相关的时间尺度上加倍保留。我们引入了一个多尺度上下文模型（MCM），它能够预测特定的学习时间表对特定材料保留的影响。MCM的预测是基于经验数据，这些数据表征了在一次学习之后对材料的遗忘。MCM是两个现有内存模型的综合（Staddon，Chelaru，& Higa，2002; Raaijmakers，2003）。从表面上看，这些模型是不相关和不兼容的，但我们表明，他们共享一个核心功能，使他们能够集成。MCM可以确定学习时间表，最大限度地提高学习的持久性，并对教育和培训产生影响。MCM既可以是输入随时间波动的神经网络，也可以是泄漏积分器的级联。MCM与记忆的贝叶斯多尺度模型（Kording，Tenenbaum，Shadmehr，2007）非常相似，但MCM能够更好地解释人类的陈述性记忆。
When individuals learn facts (e.g., foreign language vocabulary) over multiple study sessions, the temporal spacing of study has a significant impact on memory retention. Behavioral experiments have shown a nonmonotonic relationship between spacing and retention: short or long intervals between study sessions yield lower cued-recall accuracy than intermediate intervals. Appropriate spacing of study can double retention on educationally relevant time scales. We introduce a Multiscale Context Model (MCM) that is able to predict the influence of a particular study schedule on retention for specific material. MCM's prediction is based on empirical data characterizing forgetting of the material following a single study session. MCM is a synthesis of two existing memory models (Staddon, Chelaru, & Higa, 2002; Raaijmakers, 2003). On the surface, these models are unrelated and incompatible, but we show they share a core feature that allows them to be integrated. MCM can determine study schedules that maximize the durability of learning, and has implications for education and training. MCM can be cast either as a neural network with inputs that fluctuate over time, or as a cascade of leaky integrators. MCM is intriguingly similar to a Bayesian multiscale model of memory (Kording, Tenenbaum, & Shadmehr, 2007), yet MCM is better able to account for human declarative memory.