遗忘的追溯干扰模式

IF 2.3 4区 医学 Q1 Neuroscience
Antonios Georgiou, Mikhail Katkov, Misha Tsodyks
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引用次数: 0

摘要

记忆和遗忘是一枚硬币的两面,虽然前者已被广泛研究,但后者却常常被忽视。为了更好地理解遗忘,一种可行的方法是建立现象学模型,以最基本的方式实现遗忘的假定机制,然后通过实验检验这些模型的理论预测。以往研究中提出的逆行干扰就是这样一种机制,即记忆会因随后获得的记忆而被抹去。在当前的研究中,我们假设逆行消除是由相关的 "重要性 "指标控制的,因此,较重要的记忆会消除较不重要的早期获得的记忆。我们的研究表明,由此产生的数学模型的某些版本与之前报道的幂律遗忘时间过程基本一致,并且与我们用随机组合的长单词流进行的识别实验的结果非常吻合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Retroactive interference model of forgetting.

Retroactive interference model of forgetting.

Retroactive interference model of forgetting.

Retroactive interference model of forgetting.

Memory and forgetting constitute two sides of the same coin, and although the first has been extensively investigated, the latter is often overlooked. A possible approach to better understand forgetting is to develop phenomenological models that implement its putative mechanisms in the most elementary way possible, and then experimentally test the theoretical predictions of these models. One such mechanism proposed in previous studies is retrograde interference, stating that a memory can be erased due to subsequently acquired memories. In the current contribution, we hypothesize that retrograde erasure is controlled by the relevant "importance" measures such that more important memories eliminate less important ones acquired earlier. We show that some versions of the resulting mathematical model are broadly compatible with the previously reported power-law forgetting time course and match well the results of our recognition experiments with long, randomly assembled streams of words.

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来源期刊
Journal of Mathematical Neuroscience
Journal of Mathematical Neuroscience Neuroscience-Neuroscience (miscellaneous)
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: The Journal of Mathematical Neuroscience (JMN) publishes research articles on the mathematical modeling and analysis of all areas of neuroscience, i.e., the study of the nervous system and its dysfunctions. The focus is on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviours in neuroscience at all relevant scales, from the molecular world to that of cognition. The aim is to publish work that uses advanced mathematical techniques to illuminate these questions. It publishes full length original papers, rapid communications and review articles. Papers that combine theoretical results supported by convincing numerical experiments are especially encouraged. Papers that introduce and help develop those new pieces of mathematical theory which are likely to be relevant to future studies of the nervous system in general and the human brain in particular are also welcome.
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