Forgetting in long-term memory, as measured in a recall or a recognition test, is faster for items encoded more recently than for items encoded earlier. Data on forgetting curves fit a power function well. In contrast, many connectionist models predict either exponential decay or completely flat forgetting curves. This paper suggests a connectionist model to account for power-function forgetting curves by using bounded weights and by generating the learning rates from a monotonically decreasing function. The bounded weights introduce exponential forgetting in each weight and a power-function forgetting results when weights with different learning rates are averaged. It is argued that these assumptions are biologically reasonable. Therefore power-function forgetting curves are a property that may be expected from biological networks. The model has an analytic solution, which is a good approximation of a power function displaced one lag in time. This function fits better than any of the 105 suggested two-parameter forgetting-curve functions when tested on the most precise recognition memory data set collected by. Unlike the power-function normally used, the suggested function is defined at lag zero. Several functions for generating learning rates with a finite integral yield power-function forgetting curves; however, the type of function influences the rate of forgetting. It is shown that power-function forgetting curves cannot be accounted for by variability in performance between subjects because it requires a distribution of performance that is not found in empirical data. An extension of the model accounts for intersecting forgetting curves found in massed and spaced repetitions. The model can also be extended to account for a faster forgetting rate in item recognition (IR) compared to associative recognition in short but not long retention intervals.

中文翻译：

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遗忘曲线：对联结主义模型的影响

在回忆或识别测试中测量的长期记忆中遗忘，对于最近编码的项目比较早编码的项目更快。遗忘曲线上的数据很好地拟合了幂函数。相比之下，许多联结主义模型预测指数衰减或完全平坦的遗忘曲线。本文提出了一种联结主义模型，通过使用有界权重和从单调递减函数生成学习率来解释幂函数遗忘曲线。有界权重在每个权重中引入指数遗忘，并且当对具有不同学习率的权重进行平均时，会产生幂函数遗忘结果。有人认为这些假设在生物学上是合理的。因此，幂函数遗忘曲线是一种可以从生物网络中预期到的特性。该模型有一个解析解，它是一个时间滞后的幂函数的一个很好的近似值。在收集的最精确识别记忆数据集上进行测试时，该函数比 105 个建议的双参数遗忘曲线函数中的任何一个都更适合。与通常使用的幂函数不同，建议的函数定义在滞后零处。使用有限积分生成幂函数遗忘曲线的几个函数；然而，函数的类型会影响遗忘率。结果表明，功率函数遗忘曲线不能用受试者之间的表现差异来解释，因为它需要经验数据中没有的表现分布。该模型的扩展解释了在大量和间隔重复中发现的交叉遗忘曲线。该模型还可以扩展到在短但不长的保留间隔内，与联想识别相比，项目识别 (IR) 中更快的遗忘率。