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An Intermediate Asymptotics Approach for Theory Testing

机译:理论测试中的中间渐近方法

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In memory research, concepts of forgetting and the rate of forgetting are different. To estimate the rate of forgetting,w e need to construct a mathematical function at first, such as power function. Different rate of forgetting can be viewed as an evidence for different memory systems, but the problem for us is how to estimate such parameter. The same parameter estimated from a single-power function by least-squares regression often changes at different recall period in a whole range. Then, which recall period's parameters can be used to theory testing is a question. In light of nonlinear science, we put forward an intermediate asymptotics approach to capture the key characteristics of different memory. Using new method, we find that different types memory do have different rate of forgetting, which is inconsistent with McBride and Doshner (1999), but support multiple memory systems view (e.g., Schacter & Tulving, 1994).
机译:在记忆研究中,遗忘的概念和遗忘速度不同。为了估计遗忘速率,W E首先需要构造数学函数,例如功率函数。可以将不同的遗忘速度视为不同的内存系统的证据,但我们的问题是如何估计此类参数。通过最小二乘回归从单功率函数估计的相同参数通常在整个范围内的不同召回时段发生变化。然后,召回期间的参数可用于理论测试是一个问题。鉴于非线性科学,我们提出了一种中间渐近方法来捕获不同记忆的关键特征。使用新方法,我们发现不同的类型内存确实具有不同的遗忘速率,这与McBride和Doshner(1999)不一致,而是支持多个内存系统视图(例如,Sichacter&Tulving,1994)。

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