Brown, G. D. A. (Gordon D. A.), Dalloz, Peter and Hulme, Charles. (1995) Mathematical and connectionist models of human memory : a comparison. Memory, Vol.3 (No.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 NET work), 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 Q Science > QA Mathematics
Divisions:	Faculty of Science > Psychology
Library of Congress Subject Headings (LCSH):	Memory -- Mathematical models, Connectionism, Convolutions (Mathematics)
Journal or Publication Title:	Memory
Publisher:	Psychology Press
ISSN:	0965-8211
Date:	1995
Volume:	Vol.3
Number:	No.2
Page Range:	pp. 113-145
Identification Number:	10.1080/09658219508258962
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Economic and Social Research Council (Great Britain) (ESRC)
URI:	http://wrap.warwick.ac.uk/id/eprint/37204

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