Kornell Nate, Bjork Robert A
Department of Psychology, Williams College, 18 Hoxsey Street, Williamstown, MA 01267, USA.
Department of Psychology, University of California, Los Angeles, CA 90095, USA.
Behav Sci (Basel). 2025 Jul 9;15(7):924. doi: 10.3390/bs15070924.
For over a century, forgetting research has shown that recall decreases along a power or exponential function over time. It is tempting to assume that empirical forgetting curves are equivalent to the rate at which individual memories are forgotten. This assumption would be erroneous, because forgetting curves are influenced by an often-neglected factor: the distribution of memory strengths relative to a recall threshold. For example, if memories with normally distributed initial strengths were forgotten at a linear rate, percent correct would not be linear, it would decrease rapidly when the peak of the distribution was crossing the recall threshold and slowly when one of the tails was crossing the threshold. We describe a distribution model of memory that explains the divergence between forgetting curves and item forgetting rates. The model predicts that forgetting curves can be approximately linear (or even concave, like the right side of a frown) when percent correct is high. This prediction was supported by previous evidence and an experiment where participants learned word pairs to a criterion. Beyond its theoretical implications, the distribution model also has implications for education: Creating memories that are just above the threshold helps on short-term tests but does not form lasting memories.
一个多世纪以来，遗忘研究表明，随着时间的推移，回忆率会沿着幂函数或指数函数下降。人们很容易认为经验性遗忘曲线等同于个体记忆被遗忘的速率。但这个假设是错误的，因为遗忘曲线受到一个常常被忽视的因素影响：记忆强度相对于回忆阈值的分布。例如，如果初始强度呈正态分布的记忆以线性速率被遗忘，那么正确百分比将不是线性的，当分布峰值越过回忆阈值时它会迅速下降，而当其中一个尾部越过阈值时则会缓慢下降。我们描述了一个记忆分布模型，该模型解释了遗忘曲线与项目遗忘率之间的差异。该模型预测，当正确百分比很高时，遗忘曲线可能近似线性（甚至是凹形的，就像皱眉的右侧）。这一预测得到了先前证据以及一项实验的支持，在该实验中参与者将单词对学习到一个标准。除了其理论意义外，该分布模型对教育也有启示：创造略高于阈值的记忆在短期测试中有所帮助，但不会形成持久记忆。
