UNSPECIFIED. (1995) MATHEMATICAL AND CONNECTIONIST MODELS OF HUMAN-MEMORY - A COMPARISON. MEMORY, 3 (2). pp. 113-145. ISSN 0965-8211

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Abstract

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 then 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.

Item Type:	Journal Article
Subjects:	B Philosophy. Psychology. Religion > BF Psychology
Journal or Publication Title:	MEMORY
Publisher:	LAWRENCE ERLBAUM ASSOC LTD
ISSN:	0965-8211
Date:	June 1995
Volume:	3
Number:	2
Number of Pages:	33
Page Range:	pp. 113-145
Publication Status:	Published
URI:	http://wrap.warwick.ac.uk/id/eprint/19702

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