人类列表学习随机模型的数值探索

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI:10.1109/IJCNN.1999.831451

K. Wilson, C. Osborne

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引用次数: 0

摘要

给出了一个具有首因效应和近因效应的简单网络。该模型使用随机更新的裁剪权值来产生一系列不同的记忆行为。该模型最初由Kahn, Wong和Shewington(1991,1995)提出，它显示的行为范围比最初预测的要大得多。这些行为取决于更新权重的概率、初始非零权重、稀释的类型和程度。

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A numerical exploration of a stochastic model of human list learning

A simple network which shows primacy and recency effects is presented. The model uses stochastic updating of clipped weights to produce a range of different memory behaviours. The model, originally proposed by Kahn, Wong and Shewington (1991, 1995), shows a much wider range of behaviours than originally predicted. These behaviours depend on the probability of updating weights, initial non-zero weights, type and degree of dilution.

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IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)

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