Brown G D, Dalloz P, Hulme C
Department of Psychology, University of Warwick, Coventry, UK.
Memory. 1995 Jun;3(2):113-45. doi: 10.1080/09658219508258962.
Recent convolution-based models of human memory (e.g. Lewandowsky & Murdock, 1989), have accounted for a wide range of data. However such models require the relevant mathematical operations to be provided to the network. Connectionist models, in contrast, have generally addressed different data, and not all architectures are appropriate for modelling single-trial learning. Furthermore, they tend to exhibit catastrophic interference in multiple list learning. In this paper we compare the ability of convolution-based models and DARNET (Developmental Associative Recall NETwork), to account for human memory data. DARNET is a connectionist approach to human memory in which the system gradually learns to associate vectors, in one trial, into a memory trace vector. Either of the vectors can than be retrieved. It is shown that the new associative mechanism can be used to account for a wide range of relevant experimental data as successfully as can convolution-based models with the same higher-level architectures. Limitations of the models are also addressed.
近期基于卷积的人类记忆模型（例如Lewandowsky和Murdock，1989年）已经解释了大量数据。然而，此类模型需要将相关数学运算提供给网络。相比之下，联结主义模型通常处理的是不同的数据，而且并非所有架构都适用于对单次试验学习进行建模。此外，它们在多列表学习中往往会表现出灾难性干扰。在本文中，我们比较了基于卷积的模型和DARNET（发展性联想回忆网络）解释人类记忆数据的能力。DARNET是一种关于人类记忆的联结主义方法，在该方法中，系统在一次试验中逐渐学会将向量关联成一个记忆痕迹向量。然后可以检索其中任何一个向量。结果表明，新的联想机制能够像具有相同高层架构的基于卷积的模型一样成功地解释大量相关实验数据。同时也讨论了模型的局限性。
