State-trace analysis of sequence learning by simple recurrent networks

Fayme Yeates, Andy Wills, Fergal Jones, Ian P. L. McLaren

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This study investigated the use of state-trace analysis (Bamber,
1979) when applied to computational models of human learning.
We aimed to investigate the performance of simple recurrent
networks (SRNs) on a sequence learning task. Elman’s (1990)
SRN and Cleeremans & McClelland’s (1991) Augmented SRN are
both benchmark models of human sequence learning. The
differences between these models, comprising of an additional
learning parameter and the use of response units activated by
output units constituted our main manipulation. The results are
presented as a state-trace analysis, which demonstrates that the
addition of an additional type of weight component, and response
units to a SRN produces multi-dimensional state-trace plots.
However, varying the learning rate parameter of the SRN also
produced two functions on a state-trace plot, suggesting that state-trace analysis may be sensitive to variation within a single process.
Original languageEnglish
Title of host publicationProceedings of the 34th Annual Meeting of the Cognitive Science Society
EditorsNaomi Miyake, David Peebles, Richard P. Cooper
Place of PublicationAustin, Texas
PublisherCognitive Science Society
Number of pages7
ISBN (Print)9780976831884
Publication statusPublished - Aug 2012
Externally publishedYes
Event34th Annual Meeting of the Cognitive Science Society - Sapporo, Japan
Duration: 1 Aug 20124 Aug 2012

Academic conference

Academic conference34th Annual Meeting of the Cognitive Science Society


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